WO2020006289A1

WO2020006289A1 - Systems and methods for monitoring patient muscle activity and for tracking patients with motor disorders

Info

Publication number: WO2020006289A1
Application number: PCT/US2019/039588
Authority: WO
Inventors: Damon P CARDENAS; Michael R. Girouard; Luke E. WHITMIRE
Original assignee: Brain Sentinel, Inc.
Priority date: 2018-06-27
Filing date: 2019-06-27
Publication date: 2020-01-02

Abstract

Systems and methods are described for analyzing patient muscle activity using electromyography. In some embodiments, systems and method may monitor a patient for abnormal muscle activity that may be indicative of one or more motor disorders. For example, an electromyography signal may be collected and automatically partitioned to create a plurality of data sets, each of the plurality of data sets including parts of a duration width. Data sets may further be scaled by calculating a ratio between muscle activity data in individual time windows of a data set and a maximum activity value for the patient. In some embodiments, scaled levels of muscle activity may be generated and compared to determine if levels of scaled muscle activity vary with duration width in a manner that is consistent with one or more motor disorders.

Description

SYSTEMS AND METHODS FOR MONITORING PATIENT MUSCLE ACTIVITY AND FOR TRACKING PATIENTS WITH MOTOR DISORDERS

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Patent Application No.

62/690,779 titled“Systems and Methods for Detection of Muscle Activity and for Tracking the Health Status of Patients with Motor Disorders”, filed June 27, 2018. The disclosure of the foregoing application is herein fully incorporated by reference.

FIELD

[0002] This application is directed to systems, methods, and computer programs for processing muscle-related signals using electromyography and the identification of abnormal-muscle activity or changes in muscle activity.

BACKGROUND

[0003] Many health conditions may be associated with a patient having an impaired ability to control or activate muscle. For example, some patients may suffer from one or more conditions including epilepsy, disorders involving psychogenic-nonepileptic seizures (PNES), cerebral palsy, dystonia, Multiple Sclerosis, Parkinson’s disease, essential tremor disorder, strokes, injuries to the central nervous system, and other conditions that may permanently or temporarily result in abnormal control or activation of muscle. Some motor disorders may also involve abnormal control of movements during sleep or may be triggered by one or more patient activity such as physical exertion. For example, sleep disorders that may involve abnormal movement may be classified as parasomnias. Those and other related conditions may be classified as motor disorders. In many cases, the root cause of a patient motor disorder may not be known. For example, epilepsy may refer to a group of neurological disorders characterized by seizure activity, but often times this classification is based on presentation of the disorder without understanding of the root cause or causes of the disorder. For example, one or more specific disorders or conditions that may more fully characterize a patient suffering from epilepsy may not be diagnosed.

[0004] It may be difficult to fully diagnose a patient as having one or more of the above motor disorders. For example, a full diagnosis of the cause, type, and severity of a motor disorder may involve multiple visits to one or more specialists and repeated execution of numerous tests or test protocols. However, on any given day, a number of factors may influence the severity of detectable symptoms or measurable indicators of a motor disorder. Accordingly, periodic testing of patient muscle activity during scheduled visits many not always be used to detect early signatures of a motor disorders and/or reliably track or identify changes in patient conditions. This may complicate strategies for patient care. For example, where manifestation of symptoms is sporadic or intermittent, diagnosis and characterization of a motor disease may be delayed making it difficult for caregivers to provide the most appropriate care in a timely manner.

[0005] It would be useful to collect muscle-related-electrical signals in a continuous or near continuous manner to more fully track changes in muscle activity. This may be accomplished using mobile sensor devices configured for daily or regular patient wear. Electromyography (EMG), which may be executed using sensors that may be minimally intrusive to patients during daily wear, may be ideally suited for this purpose. However, it may be difficult to fully utilize the large amounts of data collected when monitoring a patient in this manner. Notably, automated methods for screening data to identify patients that exhibit unusual motor signatures are lacking in commercially available devices that monitor patient muscle activity using EMG. Electromyographic systems and methods herein enable the routine collection of muscle -activity signals without disrupting a patient’s living environment and may further be used to automatically screen signal data to identify patients whose condition may be changing or who may have an underlying motor disorder, a capability noticeably lacking from prior art system and methods based on EMG.

[0006] There remains a need for improved systems and methods useful in identifying and/or tracking muscle activity that may be indicative of muscle impairment, including, for example, some conditions that may be commonly present together with seizure activity. There is further a need for systems and methods for analyzing collected EMG signals to rapidly or automatically determine muscle activity levels and form of muscle activity to better characterize a patient’s musculature. For example, there is a need for systems and methods for analysis of different types of motor disorders, including disorders that may present activity that may be intermittent, sporadic, and/or operate over a range of different time scales. The above and other needs are addressed by the methods, systems, and computer programs herein.

SUMMARY

[0007] In some embodiments, a method of monitoring a patient susceptible to seizures for seizure activity and characterizing muscle activity for the patient using EMG is described, the method including disposing an EMG monitoring unit on or near one or more muscles of the patient and collecting an EMG signal; executing one or more seizure-detection routines configured for processing said EMG signal and identifying portions of said EMG signal wherein the patient is having a seizure or has had a seizure; selecting one or more remaining portions of said EMG signal, the remaining portions of said EMG signal excluding said portions of said EMG signal wherein a patient is having a seizure or has had a seizure; processing said one or more remaining portions of said EMG signal to provide muscle -activity data over a time period; partitioning said muscle activity data to create a first partitioned data set, the first partitioned data set including a plurality of data parts of a first duration width; scaling said plurality of data parts of said first partitioned data set in order to provide scaled muscle -activity values for said plurality of data parts, the scaling of said plurality of data parts comprising determining an average value of muscle -activity data for each of said plurality of data parts to provide a plurality of average muscle-activity values and then dividing each of said plurality of average muscle -activity values by a maximum-muscle-activity value for said patient; comparing one or more of said scaled muscle -activity values to one or more muscle -activity thresholds; and initiating one or more responses based on the comparing of one or more of said scaled muscle-activity values to one or more of said muscle-activity thresholds, the one or more responses including at least one of adjusting data collection for one or more sensors, automatically instructing a caregiver or patient to take one or more medications or to engage in a low-physical -stress activity, and transmitting a message identifying one or more muscle -activity excursions in which said scaled muscle-activity value may have met said one or more muscle -activity thresholds. Related systems and computer programs for executing the method are also described.

[0008] In some embodiments, a method of characterizing a patient’s muscle activity and identifying one or more muscle -activity profiles for the patient, the method including disposing an EMG monitoring unit on or near one or more muscles of the patient and collecting an EMG signal; processing at least a portion of said EMG signal to determine muscle -activity data over a time period; partitioning said muscle -activity data to create a plurality of data sets, the plurality of data sets each including a plurality of data parts of a duration width, the plurality of data sets characterized by different duration widths; scaling each of said plurality of data sets to provide scaled muscle-activity data for each of the plurality of data sets and using the scaled muscle -activity data to determine at least one overall scaled muscle-activity value for each of said plurality of data sets; and identifying a muscle -activity profile for said patient based on a dependence of said at least one overall scaled muscle -activity values versus duration width. Related systems and computer programs for executing the method are also described.

[0009] In some embodiments, a method of characterizing a patient’s muscle activity and identifying one or more muscle -activity profiles for the patient, the method including accessing electromyography (EMG) data from one or more units of computer memory; processing at least a portion of said EMG data to determine muscle -activity data over a time period; partitioning said muscle -activity data to create a plurality of data sets, the plurality of data sets each including a plurality of data parts of a duration width, the plurality of data sets characterized by different duration widths; scaling each of said plurality of data sets to provide scaled muscle-activity data for each of the plurality of data sets and using the scaled muscle -activity data to determine at least one overall scaled muscle -activity value for each of said plurality of data sets; and identifying a muscle -activity profile for said patient based on a dependence of said at least one overall scaled muscle -activity values versus duration width. Related systems and computer programs for executing the method are also described.

BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Fig. 1 is an illustration of an embodiment of a system for collecting and/or processing an EMG signal.

[0011] Fig. 2 is an illustration of an embodiment of a monitoring unit for collecting and/or processing an EMG signal.

[0012] Fig. 3 is an illustration of an embodiment of a base station for collecting and/or processing an EMG signal.

[0013] Fig. 4 is a flowchart showing a method for processing a collected EMG signal. The method shown in Fig. 4 may include generating at least one partitioned data set from a collected EMG signal.

[0014] Fig. 5 shows a recorded voltage of an EMG signal and a first partitioned data set of the EMG signal. Fig.5 further shows a second partitioned data set of the EMG signal.

[0015] Fig. 6 is a flowchart showing another method for processing a collected EMG signal. The method shown in Fig. 6 may include generating two or more partitioned data sets from a collected EMG signal and comparing scaled muscle-activity values for the two or more partitioned data sets.

[0016] Fig. 7 shows a curve illustrating the general form of scaled muscle -activity data versus duration width.

[0017] Fig. 8 is a flowchart showing another method for processing a collected EMG signal.

[0018] Fig. 9 is a flowchart showing a method of accessing EMG data and processing the data.

[0019] Fig. 10 shows a control chart for collected EMG data.

DETAILED DESCRIPTION

[0020] The following terms as used herein should be understood to have the indicated meanings.

[0021] When an item is introduced by“a” or“an,” it should be understood to mean one or more of that item.

[0022] The term“based on” as used herein refers to a situation wherein one element is dependent upon another element. For example, one or more responses may be based on a comparison of one or more scaled muscle-activity values to one or more muscle-activity thresholds. The element may be solely based on the other element. However, use of the term“based on” does not preclude the situation wherein an element may be dependent upon another element and still further elements. Where one element is exclusively dependent upon another element, the term“exclusively based on” may be used.

[0023] “Comprises” means includes but is not limited to.

[0024] “Comprising” means including but not limited to.

[0025] The term "coupled" as used herein means in communication with via either a wireless or a wired connection. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may be present. In contrast, when an element is referred to as being "directly connected" or "directly coupled" to another element, there are no intervening elements present.

[0026] The term“meeting an excursion threshold” as used herein refers to a condition wherein signal or data exceeds a maximum threshold, is below a minimum threshold, or both.

[0027] The term,“partitioning” as used herein means to divide or break up. For example, “partitioning of muscle-activity data” means dividing muscle -activity data into two or more parts, each part including data collected over time windows of a fixed duration width or over a fixed duration width range. Where collected muscle-activity data is partitioned into parts as described above, a collection of parts may be referred to as a series of data or data series. For example, if a 60- minute portion of collected data is partitioned or broken up into 60 parts, each of the parts may include about a 1 -minute time window of data, and the collection of parts may be referred to as a first partitioned data series. Likewise, if a 60-minute portion of collected data is broken up into 30 parts, each of the parts may include an about 2-minute time window of data, and the collection of parts may be referred to as a second partitioned data series.

[0028] The term "seizure event" as used herein, unless the context indicates otherwise, includes physiological events wherein a patient has suffered a seizure or exhibited physiological activity resembling the presence of a seizure, even if a true seizure may not have occurred.

[0029] Where a range of values is described, it should be understood that intervening values, unless the context clearly dictates otherwise, between the upper and lower limits of that range, and any other stated or intervening value in other stated ranges, may be used within embodiments described herein.

[0030] Systems and methods herein may collect EMG signal from a patient or access muscle -activity data from stored memory. The systems and methods herein may further use EMG signals or data derived therefrom to monitor a patient for seizure activity, to identify abnormal muscle activity, to monitor a patient for changes in muscle activity levels or forms, to identify muscle activity that may correspond with one or more muscle-activity profiles or combinations thereof. By way of nonlimiting example, abnormal muscle activity may include muscle tremors, excessive muscle tone or muscle contractions, deficient muscle tone, atypical coordination between different muscles of the body, deficiencies in gait or posture, and any combination thereof. Abnormal muscle activity may sometimes be related to one or more motor disorders using a muscle-activity profile.

[0031] The systems and methods herein may be directed towards collection of EMG signals while a patient may be engaged in daily living, such as while a patient is in a home environment or in some other locality. In some embodiments, systems may further be configured for initiating an alarm and contacting local or remote caregivers in response to detection of abnormal or dangerous seizure- related-muscle activity. Systems may further be configured to direct or automatically direct caregivers in executing one or more actions (or predefined actions) in response to detection of abnormal or dangerous seizure-related-muscle activity. For example, based on detection of a change in the magnitude or form of muscle activity and/or the identification of muscle activity matching one or more muscle-activity profiles, a message (or other prompt) may sometime be displayed on a system base station or other device. The prompt may direct a caregiver to provide one or more care actions - such as providing a muscle relaxer or sedative to a patient or directing a patient to engage in a low physical stress activity.

[0032] The systems and methods described herein may be applied using surface EMG electrodes included in sensors that are minimally intrusive for daily wear. As used herein the term sensor and monitoring unit may be used interchangeably. Generally, minimally-intrusive sensors or monitoring units may not include any encumbering external wires or exposed leads, may be low in weight, may be applied using non-irritating adhesives, may be of minimal thickness, may be conveniently disposed over or adjacent peripheral muscles (such as the biceps or triceps, for example), or integrate a combination of the above features or attributes. For example, in some embodiments herein, surface EMG electrodes may be included in monitoring units without external wires, of less than about 0.75 kgs in weight, and configured to be disposed on one or more peripheral muscle (e.g., the biceps or triceps) of a patient without using any acrylic or other irritating adhesive. Monitoring units described herein may preferably operate without needing to be recharged for at least about 12 hours to about 48 hours, or even significantly longer.

[0033] Detection of seizures as described herein, unless where explicitly described, is based on EMG. In some embodiments, one or more detected seizure events may further be characterized or classified in some way. For example, a seizure event may be classified based on type as a generalized- tonic-clonic (GTC) seizure, a seizure event including one or more parts of a GTC seizure, a psychogenic or non-epileptic seizure (PNES), a complex-partial seizure, or as another type of seizure. Seizures may further be classified or graded in intensity. Further, in some embodiments, the semiology or duration width of a seizure and/or parts of a seizure may be determined. This analysis may also be referred to as an analysis of the dynamics for a seizure or seizure event.

[0034] Figure 1 illustrates an embodiment of a system 10 which may be configured for executing the methods herein or for other purposes. As shown in each of Fig. 1 and Fig. 2, the system 10 may include an EMG monitoring unit 12 (which will be referred to herein as a monitoring unit). The monitoring unit 12 may be configured as a portable and wearable EMG device that may be disposed on, or near one or more muscles or surrounding tissue of a patient. In some particular patients or difficult situations, it may useful to at least partially insert one or more electrodes into a muscle or adjacent tissue.

[0035] The monitoring unit 12 may communicate with other elements of system 10 using any of various wireless local area network technologies. The monitoring unit 12 may, for example, communicate wirelessly to the internet using WiFi, Bluetooth, or through another local network in order to connect with other devices of system 10. Using a local network, a monitoring unit 12 may link to and send data over the internet directly or via an intermediate base station 14. The base stationl4 may be a computer designed to be placed in a locality of a monitoring unit 12 and coupled to the monitoring unit 12. For example, the monitoring unit 12 and base station 14 may include one or more transmitters, transceivers, or radios to facilitate one-way or two-way communications between the elements 12, 14. In some embodiments, a caregiver may be contacted directly through a local network such as WiFi. A base station 14 may be connected to the internet wirelessly (such as through a local network) or may be linked to the internet through a hard connection. A monitoring unit 12 may sometimes operate as a data recorder, such as without a wireless connection and/or without alarming capability. For example, monitoring unit 12 may include one or more units of memory to collect an EMG signal. Collected EMG signal may be stored and sent for review by shipping a detachable unit of memory to a caregiver, such as may be done when exchanging a monitoring unit 12.

[0036] The monitoring unit 12 may comprise one or more EMG electrodes capable of collecting muscle-related-electrical signals from muscles at or near the skin surface of a patient and delivering those electrical signals to a processor. For example, as shown in Figure 2, the monitoring unit 12 may include one or more processors 48 for processing muscle-related signals using EMG.

The base station 14 may likewise comprise a computer capable of receiving and processing EMG signals from the monitoring unit 12 and/or data from other sensors. The devices 12, 14 may further be configured for determining from the processed signals whether a seizure may have occurred and sending an alert to a caregiver. An alert transceiver 20 may be carried by, or placed near, a caregiver to receive and relay alerts transmitted by either of monitoring unit 12 and/or base station 14. Other components that may be included in the system 10, include for example, wireless communication devices 22, 24, storage database 26, one or more additional sensors, such as acoustic sensor 16, a video camera 18, electronic devices for detecting changes in the integrity of an electrode skin interface, and one or more environmental transceivers or other elements for identifying a position or orientation of a monitoring unit 12 or patient.

[0037] A monitoring unit 12 may be a simple sensor, such as an electrode, that may send signals to the base station 14 or to another monitoring unit 12 for processing and analysis or may comprise a“smart” sensor having data processing and storage capability. A monitoring unit 12 may include one or more smart client applications. In some embodiments, a simple sensor may be connected via a wired or wireless connection to a battery-operated transceiver mounted on a belt or other garment or accessory worn by a person. Accordingly, in this disclosure, monitoring units 12 may be of varying designs, such as a basic monitoring unit design including electrodes for collecting muscle -related electrical signals and means for providing collected or stored signals to another device, such as may include a simple transmitter or memory unit which may be detached or otherwise accessed by another device for post-process download and signal analysis.

[0038] In some embodiments, a monitoring unit 12 may send some signals to the base station 14 for further analysis or for classification. For example, the monitoring unit 12 may process and use EMG signals (or optionally EMG signals and additionally ECG, temperature, acceleration, orientation sensors, saturated oxygen, and/or audio sensor signals) to make an initial assessment regarding the likelihood of occurrence of a seizure and may send those signals and its assessment to the base station 14 for separate processing, confirmation, or classification. However, in some embodiments, the monitoring unit 12 may be specifically designed to detect and/or classify seizure events using EMG and without relying on a plurality of other types of sensor data. Accordingly, battery resources may be conserved as compared to some other systems that may rely on multiple sensors and data types for detection and/or classification of seizure activity. Likewise, in addition to signals useful for detecting seizures, monitoring unit 12 may route signals for other analyses of muscle activity levels and forms, such as to track and characterize muscle -activity and levels thereof. In some embodiments, if the base station 14 confirms that a seizure is likely occurring, then the base station 14 may initiate an alarm for transmission over the network 30 to alert a designated individual by way of email, text, phone call, or any suitable wired or wireless messaging indicator. In some embodiments, an alarm may include a message that may indicate, by way of example, how an event was classified, one or more selectable states selected by a patient prior to a seizure event detection, a predetermined protocol for initiating a response, and combinations thereof.

[0039] It should be appreciated that the monitoring unit 12 may, in some embodiments, be smaller and more compact than the base station 14, and it may be convenient to use a power supply with only limited strength or capacity. Therefore, it may be advantageous, in some embodiments, to control the amount of data that is transferred between the monitoring unit 12 and the base station 14 or other devices as this may increase the lifetime of any power supply elements integrated in or associated with the monitoring unit 12. In some embodiments, if one or more of the monitoring unit 12, the base station 14, or a caregiver, e.g., a remotely located caregiver monitoring signals provided from the base station 14, determines that a seizure may be occurring, video camera 18 may be triggered to collect video information of the patient. Or, other sensor systems, including, for example, systems which may be remotely located or disposed on or in the patient’s body, may be activated.

[0040] The base station 14, which may be powered by atypical household power supply and may contain a battery for backup, may have more processing, transmission, and analysis power available for its operation than the monitoring unit 12 and may be able to store a greater quantity of signal history and evaluate a received signal against that greater amount of data. The base station 14 may communicate with an alert transceiver 20 located remotely from the base station 14, such as in the bedroom of a family member, or to a wireless device 22, 24 carried by a caregiver or located at a work office or clinic. The base station 14 and/or transceiver 20 may send alerts or messages to designated people via any suitable means, such as through a network 30 to a cell phone 22, PDA 24 or other client device. The system 10 may thus provide an accurate log of seizures, which may allow a patient’s physician to understand more quickly the success or failure of a treatment regimen. Of course, the base station 14 may simply comprise a computer having installed a program capable of receiving, processing and analyzing signals as described herein and capable of transmitting an alert. A base station 14 may include one or more smart client applications. In other embodiments, the system 10 may simply comprise, for example, EMG electrodes as part of a device configured to transmit signals to a smartphone, such as an iPhone, configured to receive EMG signals from the electrodes for processing the EMG signals as described herein using an installed program application. In further embodiments, so-called“cloud” computing and storage may be used via network 30 for storing and processing the EMG signals and related data. In yet other embodiments, one or more EMG electrodes may be packaged together as a single unit with a processor capable of processing EMG signals as disclosed herein and sending an alert over a network. In other words, the apparatus may comprise a single item of manufacture that may be placed on a patient and that does not require a base station or separate transceiver. Or the base station may be a smartphone or tablet computer, for example.

[0041] Collected signals may be sent to a remote database 26 for storage. The monitoring unit 12 and base station 14 may be remotely accessed via network 30 by one or more remote computers 32 to allow updating of monitoring unit 12 and/or base station 14 software and data transmission. The one or more remote computers 32 may also access one or more of database 26 or other devices herein to access collected EMG signal and to analyze the signals in order to characterize muscle activity levels and forms as further described in the methods herein. And, in some embodiments, the remote computer 32 or another computer may also serve to monitor exchange of signals including alarm signals and EMG signals between different devices associated with any number of designated individuals set to receive the signals. The base station 14 may generate an audible alarm, as may a remote transceiver 20 or monitoring unit 12. In some embodiments, wireless links may be two-way for software and data transmission and message delivery confirmation. Base station 14 may also employ one or all of the messaging methods listed above for seizure notification. The base station 14 or monitoring unit 12 may provide an“alert cancel” button to terminate an incident warning.

[0042] In some embodiments, a transceiver may additionally be mounted within a unit of furniture or some other structure, e.g., an environmental unit or object. If a monitoring unit 12 is sufficiently close to that transceiver, such a transceiver may be capable of sending data to a base station 14. Thus, the base station 14 may be aware that information is being received from that transceiver, and therefore base station 14 may identify the associated environmental unit. In some embodiments, a base station 14 may select a specific template file, e.g., such as including threshold values and other data as described further herein, that is dependent upon whether or not it is receiving a signal from a certain transceiver. Thus, for example, if the base station 14 receives information from a detector and from a transceiver that is associated with a bed or crib, it may treat the data differently than if the data is received from a transceiver associated with another environmental unit, such as, for example, clothing typically worn while an individual may be exercising or an item close to a user’s sink where for example a patient may brush his or her teeth. An environmental transceiver may sometimes identify that a patient is in a location where they typically may rest or relax. Muscle activity data may be collected and sometimes marked as being provided when a patient is typically at rest or relaxing or otherwise expected to be engaged in a low physical stress activity. Or, one or more of monitoring unit 12 or base station 14 may accept one or more inputs to identify that a patient may be at rest or relaxing. A base station 14 may also send information to a monitoring unit 12 instructing the monitoring unit 12 to use one or more specific template files. More generally, a monitoring or collection system may be configured with one or more elements with global positioning system (GPS) capability, and location or position information may be used to adjust one or more routines that may be used in a seizure detection algorithm or to identify data that may be used for otherwise

characterizing muscle activity. For example, GPS capability may be included along with or among one or more microelectromechanical sensor elements included in a monitoring unit 12.

[0043] Fig. 2 illustrates an embodiment of a monitoring unit 12. The monitoring unit 12 may include EMG electrodes 34 and may also include, in some embodiments, ECG electrodes 36. The monitoring unit 12 may further include amplifiers with leads-off detectors 38. The one or more leads- ofif detectors may provide signals that indicate whether the electrodes are in physical contact with the person’s body or otherwise too far from the person’s body to detect muscle activity, temperature, brain activity, or other patient phenomena. In some embodiments, data derived from the leads-off detection may further be used to assist in classification of detected seizure-related events. The monitoring unit 12 may further include one or more elements 40, such as solid state

microelectromechanical structures, configured for detection of position and/or orientation of the monitoring unit 12. For example, an element 40 may include one or more micromachined inertial sensors such as one or more gyroscopes, accelerometers, magnetometers, or combinations thereof.

[0044] The monitoring unit 12 may further include a temperature sensor 42 to sense the person’s temperature. Other sensors (not shown) may be included in the monitoring unit 12, as well, such as accelerometers, microphones, and oximeters. Signals from EMG electrodes 34, ECG electrodes 36, temperature sensor 42, orientation and/or position sensors 40 and other sensors may be provided to a multiplexor 44. The multiplexor 44 may be part of the monitoring unit 12 or may be part of the base station 14 if, for example, the monitoring unit 12 is not a smart sensor. The signals may then be communicated from the multiplexor 44 to one or more analog-to-digital (A-D) converters 46. Analog-to-digital converters 46 may be part of the monitoring unit 12 or may be part of the base station 14. The signals may then be communicated to one or more microprocessors 48 for processing and analysis as disclosed herein including seizure detection and/or unless of muscle -activity levels and forms. The microprocessors 48 may be part of the monitoring unit 12 or may be part of the base station 14. The monitoring unit 12 and/or base station 14 may further include memory of suitable capacity. The microprocessor 48 may communicate signal data and other information using a transceiver 50. [0045] In some embodiments, the exemplary monitoring unit of Fig. 2 may be differently configured. Many of the components of the detector of Fig. 2 may be in base station 14 rather than in the monitoring unit 12. For example, monitoring unit 12 may simply comprise an EMG electrode 34 in wireless communication with a base station 14. In such an embodiment, A-D conversion and signal processing may occur at the base station 14. If, for example, an ECG electrode 36 is included, then multiplexing may also occur at the base station 14. A monitoring unit may include one or more ancillary memory units that may be detachable from the monitoring unit and sent to one or more caregivers for review.

[0046] In another example, the monitoring unit 12 of Fig. 2 may comprise an electrode portion having one or more of the EMG electrodes 34, ECG electrodes 36 and temperature sensor 42 in wired or wireless communication with a small belt-wom transceiver portion. The transceiver portion may include a multiplexor 44, an A-D converter 46, microprocessor 48, transceiver 50 and other components, such as memory and I/O devices (e.g., alarm cancel buttons and visual display).

[0047] Fig. 3 illustrates an embodiment of a base station 14 that may include one or more microprocessors 52, a power source 54, a backup power source 56, one or more EO devices 58, and various communications means, such as an Ethernet connection 60 and wireless transceiver 62. In some embodiments, the base station 14 may have more processing and storage capability than the monitoring unit 12 and may include a larger electronic display for displaying EMG signal graphs for a caregiver to review EMG signals in real-time as they are received from the monitoring unit 12 or historical EMG signals from memory. The base station 14 may process EMG signals and other data received from the monitoring unit 12. If the base station 14 determines that a seizure is likely occurring, it may send an alert to a caregiver via transceiver 62.

[0048] Either or both of base station 18 and alert transceiver 20 may include one or more units of programmable memory including instructions linking one or more inputs to one or more output functions. For example, inputs may include an event signal indicating that a detected seizure event has occurred, or inputs may include other information concerning the detected event, such as the time of detection and type of event. In some embodiments, inputs described herein may include or be based on one or more responsivity index values as further described in commonly owned US

Provisional Patent Application No. 62/781,334 filed December 18, 2018 and titled“Methods and Systems for Providing Care to a Seizure Patient,” the full contents of which are herein incorporated by reference. As further described therein a base station 18 may act as a data input device and/or function to correlate inputs and outputs as may be used to mediate or encourage care actions when responding to a seizure. As described further herein, an input may be based on one or more scaled muscle -activity levels, such as may indicate identification of a muscle-activity profile or excursion event wherein a muscle-activity level meets one or more thresholds. Additionally, an input may include one or more signals generated based on one or more caregiver inputs verifying that a certain care action has been taken. Outputs may comprise one or more instructions to execute any of various functions executable by either of base station 18 or alert transceiver 20. For example, output functions described in some embodiments herein include the display of one or more messages (e.g., messages including instructions to perform a care action), initiation of one or more alarms or types of alarms, and combinations thereof. Inputs and output functions may be encoded or linked in various suitable ways, such as in the form of a decision tree or decision matrix. For example, a decision tree may require one or more inputs to automatically execute a certain action. In some embodiments, recognition of a previous action may be a required input for a further action. Thus, at least some care actions may be organized in stages, such that a desired sequence of actions may executed by a local caregiver, actions in the sequence being verified such that earlier actions have been performed before additional care messages (i.e., messages with further care instructions) are provided.

[0049] Various devices in the apparatus of Figs. 1-3 may communicate with each other via wired or wireless communication. The system 10 may comprise a client-server or other architecture and may allow communication via network 30. Of course, the system 10 may comprise more than one server and/or client. In other embodiments, the system 30 may comprise other types of network architecture, such as a peer-to-peer architecture, or any combination or hybrid thereof.

[0050] Figure 4 is a flowchart of the method 90 for monitoring a patient using EMG and characterizing patient muscle activity. In the method 90, collected EMG signals may be used to monitor a patient to detect seizures. One or more alarms may further be initiated if a seizure event is detected. Alternatively, seizure events may be detected and recorded without initiating an alarm. For example, seizure-event detection may be used for defining when a seizure may have occurred and for selecting one or more parts of a collected signal. Together with monitoring a patient for seizure activity, EMG signals may be collected and processed to identify changes in muscle activity, such as may indicate a change in a patient condition. Electromyography signals may also be collected and processed over time and used to assist in identification of one or more underlying conditions which may be related to seizure activity or which may commonly manifest together with seizure activity.

[0051] The method 90 may be executed using the system 10 and/or elements described in reference therein or another suitable system may be used. For example, in an embodiment wherein method 90 is executed using system 10, an EMG signal is collected using a monitoring unit 12 and the collected EMG signal may be processed therein for seizure detection. Time-stamping of detected seizure events may also be executed in monitoring unit 12. One or more local and/or remote alarms may additionally be initiated, such as may include contacting a local or remote caregiver using an audible or other perceptible alarm provided using one or more of the devices 12, 14, 20, 22, and/or 24. Collected EMG signal, time-stamped based on seizure-related event detection, may further be sent to one or more external processors for additional processing to characterize patient muscle activity. For example, monitoring unit 12 may be coupled to base station 14 via a wireless local network or to external computer 32 via a wireless connection over network 30. One or more processors in the devices 14, 32 may be configured with instructions for processing collected signals to characterize patient muscle activity. For example, the one or more processors may be suitably configured to select one or more portions of EMG signal, process selected signal to provide muscle -activity data over time, partition muscle-activity data, scale partitioned muscle -activity data, and analyze scaled muscle- activity data for signs of abnormal motor activity. Routing and analysis of signals may be executed automatically, and notification of abnormal motor activity or changes in motor activity may also operate automatically.

[0052] In another embodiment wherein method 90 is executed using system 10 and/or elements described in reference therein, an EMG signal is collected using a monitoring unit 12, and the collected EMG signal may be again processed to determine if a patient may be having a seizure. For example, signal may be processed to detect seizure activity using a processor included in the monitoring unit 12 and/or using a processor in a base station 14. At least one of the monitoring unit 12 and the base station 14 may further include one or more units of memory for storing the collected EMG signal. The one or more units of memory may be detachable or ancillary memory units that may be physically shipped to a remote caregiver and information therein downloaded for review.

Thus, seizure detection and/or alarm initiation may be executed in real-time whereas other processing of signals and analysis to characterize patient muscle activity may be executed during post-collection review of data.

[0053] In another embodiment wherein method 90 is executed using system 10 and/or elements described in reference therein, an EMG signal is collected using a monitoring unit 12, and the collected EMG signal may again be processed to determine if a patient may be having a seizure.

In addition, monitoring unit 12 may itself include a processor that is configured for additional processing to characterize patient muscle activity. Accordingly, seizure detection and/or alarm initiation as well as other processing of signals may be executed in real-time.

[0054] As shown in step 92, one or more EMG signals may be collected. Collection of one or more EMG signals may, for example, include disposing one or more monitoring units 12 in association with one or more patient muscles. For example, one or more monitoring units 12 may be disposed on the skin of a patient near a patient’s biceps, triceps, hamstrings, quadriceps, frontalis, temporalis, wrist flexors, or other suitable muscle or muscles of a patient’s body and/or any combination of the muscles thereof. One or more of the monitoring units 12 may sometimes be disposed on muscles on opposite sides of a patient’s body or on one or more pairs or groups of muscles for which information is desired about how coordinated or synchronized muscle activity may be. For example, two or more monitoring units 12 may be disposed on muscles controlling movement of a joint (e.g., an agonist and antagonist pair of muscles) and/or on opposite sides of a joint, including, for example, joints that may sometimes be subject to an abnormal or painful level of flexion during movement for a patient with a muscle disorder. Such joints may include, by way of nonlimiting example, the shoulder, hip, elbow, knee, neck, or ankle joint. Using the elbow joint as an example, EMG electrodes may be used to measure activity from muscles on the proximal side of the joint such as the biceps, triceps, or both. EMG electrodes may sometimes be used to further collect data from muscles on the distal side of the joint, such as one or more muscles of the forearm.

[0055] In one embodiment, at least one monitoring unit 12, such as may be disposed on the biceps or triceps, may include a processor configured for seizure monitoring and alarm initiation. One or more smaller or more basic monitoring units, such as may comprise electrodes or groups of electrodes, may further be disposed on a patient to collected muscle activity from a nearby joint. For example, one or more electrodes may be configured to collect and store EMG data. The stored data may then be downloaded for analysis upon removing or replacing the electrodes. Alternatively, one or more electrodes or groups of electrodes may communicate with the at least one monitoring unit 12 via a short range low power consumption wireless connection. Periodically, data collected by the electrodes may be routed to the monitoring unit 12 and may be analyzed therein and/or periodically transmitted to one or more external processors for further analysis.

[0056] For the sake of brevity, discussion in additional steps of method 90 may refer to a single EMG signal and/or single monitoring unit 12. However, it should be understood that, unless the context indicates otherwise, embodiments wherein more than one EMG signal and/or more than one monitoring unit 12 (such as a first monitoring unit 12 and other units simply comprising one or more electrodes) are envisioned.

[0057] One or more seizure-detection routines are further executed in the step 92. Seizure- detection routines may be used to time-stamp detected seizures. Some of the seizure -detection routines herein may further be used to determine the dynamics of a seizure, such as may be used to identify different portions of a collected EMG signal. In some situations, it may be useful to use a first seizure-detection routine to detect seizures and to initiate one or more alarms. For example, a routine for that purpose may be designed with sufficient sensitivity and selectivity for seizure detection. A second routine may be more particularly designed to determine the dynamics of a seizure.

[0058] For example, a processor may execute a seizure-detection routine configured to process an EMG signal in order to calculate one or more property values of one or more measurable properties of a collected EMG signal and compare the one or more property values to one or more thresholds in order to detect one or more seizure -related events. By way of nonlimiting example, a property value may be an amplitude of an EMG signal, a number of zero crossings exhibiting a hysteresis, a T-squared statistical value determined from an EMG signal, or a principal component value determined from an EMG signal, or another suitable property value may be used. Associated thresholds may include a threshold amplitude, threshold number of zero crossings exhibiting a hysteresis, threshold T-squared value or a threshold principal component value. Based on a comparison of property values and associated thresholds, seizure-related events may be detected and starting and/or ending time points for a seizure may be determined. In some embodiments, based on detection of seizures using one or more seizure -detection routines, collected EMG signals may be organized into one or more signal types. For example, EMG signal may be organized into a signal type collected during periods wherein a patient seizure was recorded, a signal type collected during periods wherein no seizures were detected, or other signal types collected during one or more periods which are temporally correlated with seizure activity in some way. Exemplary seizure detection routines suitable for use in method 90 are described below. Unless explicitly described otherwise, the following routines may also be used in other methods described herein in which seizure detection is executed.

[0059] In some embodiments, a seizure -detection routine may include collecting muscle- related electrical signals and processing the signals by amplification. The amplified signals may further be analyzed using one or more integration algorithms or frequency transformation algorithms. Analysis may, for example, include determining whether integrated or frequency transformed signals are above a predetermined or threshold power content within one or more time-windows. In some embodiments, a seizure detection routine may include a power content threshold that is about 10% to about 150% of a maximum voluntary muscle contraction value provided by a given a patient. In some embodiments, integration windows may be set at about 25 milliseconds to about 200 milliseconds, the windows may be staggered to help minimize latency between seizure onset and seizure detection.

[0060] In some embodiments, sensitivity for detection of seizure events may be enhanced by executing a frequency transform (or otherwise isolating one or more frequency band) and determining the variance/covariance of power amplitudes for a plurality of selected frequency bands. For example, the variance/covariance of data may be used to calculate one or more of a T-squared statistical value or principal component value for a collected signal. T-squared statistical values or principal component values may then be compared to one or more threshold T-squared values or threshold principal component values to detect seizure activity. Seizure detection routines executed using one or more frequency or integration algorithm are further described in US Patent No.

8,386,025, commonly owned by Applicant, and titled, "Device and Method for Monitoring Muscular Activity," with an issue date of February 26, 2013. Seizure detection routines executed based on T- square analysis and or principal component analysis are, for example, further described in US Patent No. 9,186,105, commonly owned by Applicant, and titled, "Method and Apparatus for Detecting Seizures," with an issue date of November 17, 2015. The contents of each of the above patents are herein fully incorporated by reference.

[0061] In some embodiments, seizure-detection routines herein may be configured to process a collected EMG signal to generate a seizure-event signal, the processing including counting a number of crossings between a filtered, collected signal and a predetermined hysteresis value defining a positive and a negative threshold value within each of a number of time windows. Seizure-detection routines executed based on a number of crossings between a signal and a hysteresis value are, for example, described in US Patent No. 9,949,654, commonly owned by Applicant, and titled, "Method of Detecting Seizures," with an issue date of April 24, 2018, the contents of which are herein fully incorporated by reference. As described therein, hysteresis values may range from about +/- 0 pV to about +/- 500 pV or from about +/- 20 pV to about +/- 250 pV.

[0062] In some embodiments, as also described in US Patent Application No. 14/407,249, filed December 11, 2014, and titled "Method and System of Detecting Seizures," commonly owned by Applicant, seizure detection may include determining a signal content in each of two frequency bands and determining a ratio between the frequency bands. A ratio between frequency bands may then be compared to a threshold ratio for seizure detection. The full content of US Patent Application No. 14/407,249 is herein incorporated by reference.

[0063] As shown in step 94, one or more portions of a collected EMG signal may be selected for further analysis. Selection of one or more portions of an EMG signal may, for example, involve removing one or more portions of an EMG signal before remaining portions of the signal are further processed. In some embodiments, selection of signals may include removing from a collected EMG signal a subset of the collected signal collected during one or more seizures so that the remaining collected signal may be further processed. Thus, a selected EMG signal may be selected so that the signal is unbiased by seizure events and representative of a patient’s muscle activity during normal or resting periods. For example, a selected EMG signal may be from a portion of signal without a recorded seizure. Selection of data may, for example, be executed using a processor included in monitoring unit 12. Alternatively, selection of data may be executed using one or more processors included in either of remote computer 32 or base station 14.

[0064] A selected or removed part of an EMG signal may include one or more parts of an EMG signal temporally correlated to seizure activity in some way. For example, a part of an EMG signal may be temporally correlated to a detected seizure because it may be determined that the part was collected during an interval preceding the start of a detected seizure. For example, it may be determined that a part of an EMG signal was collected within about 2 minutes to about 60 minutes preceding a start of a detected seizure. Where a portion of an EMG signal is temporally correlated with a detected seizure the portion of signal may be referred to as a pre-seizure portion of signal. Alternatively, a part of an EMG signal may be temporally correlated to a seizure because the part of EMG signal was collected following a detected seizure. For example, it may be determined that a part of an EMG signal was collected within about 10 minutes or about 60 minutes following completion of a detected seizure. Accordingly, muscle activity may be characterized for all collected signals during a monitoring period. Muscle activity may also be characterized for resting or intermediate periods isolated from a detected seizure. Or, muscle activity may be characterized for signals collected during periods of pre-seizure activation or post-seizure recovery. Associated data may, for example, be particularly useful in identifying changes in muscle activity that may serve as early stage triggers of seizures.

[0065] For some patients, selection of data may be based on one or more other considerations. For example, a selected portion of an EMG signal may include data collected while a patient is resting or sleeping or otherwise engaged in one or more activities. To facilitate selection of a portion of an EMG signal, a patient may sometimes select one or more device settings, such as may be provided on one or more of the devices 12, 14. For example, a device setting may identify that a patient is resting or engaged in some other activity. Alternatively, a system may automatically determine that a patient may be resting or engaged in another activity based on other considerations. For example, as described herein, the system 10 may include one or more environmental transceiver as may be used to identify a position or orientation of a monitoring unit 12 or patient. For example, an environmental transceiver may, for example, be associated with a bed, crib or clothing typically worn while an individual may be exercising. Likewise, an environmental transceiver may sometimes identify that a patient is in a location where they typically may rest or relax.

[0066] In step 96, one or more collected EMG signals or selected parts thereof may be processed to provide the collected EMG signals or selected parts thereof in a form related to a standard of measurement for muscle activity or muscle output. For example, processing in step 96 may include determining one or more of an amplitude, power content, or other standard for measurement of muscle activity for the EMG signal over time. Where an EMG signal has been processed to provide data for a standard of muscle activity or muscle output over time, the processed EMG signal may sometimes be referred to as muscle activity data over time.

[0067] For example, a collected EMG signal may be amplified and processed using an analog-to -digital converter in order to produce amplitude or power content muscle-activity data. One or more operations such as rectification, low pass filtering, or other operations that may be used to form or condition an EMG signal may also be executed in the step 96. One or more test signals of known strength may sometimes be applied using one or more electrodes and detected at one or more other system electrodes. For example, electrodes may be arranged in a bipolar detection arrangement, and a test signal may be applied at the common electrode and detected using one or more of a pair of detection electrodes. The detected test signal may be used to verify contact integrity of electrodes with the skin and may also be used to calibrate or normalize a detected amplitude, power content, or other standard for measurement of muscle activity for the EMG signal over time.

[0068] In some embodiments, collected EMG signal may be processed in order to isolate one or more spectral regions of EMG signal. Isolation of EMG signal in one or more spectral regions may, for example, include use of one or more filters. Filtering may be achieved using software or electronic circuit components, such as bandpass filters (e.g., Baxter-King filters), suitably weighted. Collection of data in step 92, selection of data in step 94, and processing of data in step 96 may be described conveniently as distinct steps. However, such description should not be interpreted as limiting methods herein to filtering with either software or electronic circuit components. For example, analog or digital signal processing or combinations of analog and digital signal processing may be used for isolation of spectral data or for other suitable applications. In addition, selection of data in step 94 may be dependent upon processing to identify one or more seizure events, such as may be executed as part of step 92. That processing, may, itself, involve various operations used to provide muscle activity data over time. Accordingly, some operations in step 96, such as determining an amplitude of EMG signal, may alternatively be executed in either of step 92 as part of seizure detection or in step 94.

[0069] For example, in some embodiments, an EMG signal may be processed to isolate one or more frequency bands, and the amplitude or magnitude of signal isolated from one or more frequency bands may be determined. For example, the total power content for one or more collected bands may be determined and compared to a threshold in order to detect seizure activity. Thus, the power content in one or more bands may be determined in step 92.

[0070] In some embodiments, the magnitude of a statistical value related to levels of muscle activity and processed from isolated signal in one or more frequency bands may be determined. For example, a statistical value of a collected EMG signal such as a T-squared statistical value or principal component value may be determined, as described in some exemplary seizure detection routines executed in step 91. Accordingly, T-squared and/or principal component values may be tracked and recorded. And, in this disclosure, where reference is made, for example, to an amplitude, a magnitude, or to some other value related to a standard or metric of muscle output, the magnitude of a statistical metric calculated from an EMG signal, such as a T-squared value or principal component value, may, in some embodiments, alternatively be applied. Accordingly, one or more of a T-squared and/or principal component value of an EMG signal may be calculated in step 92. Either or both of those values may serve as a standard of muscle activity and may be further selected for further processing in additional steps of the method 90.

[0071] As further described below, muscle -activity data may be partitioned and further processed or scaled to determine one or more scaled muscle-activity values for a patient. In this way, muscle -activity data may be processed to provide a more sensitive or indicative measure of some motor disorders than provided simply be muscle activity that is not scaled. However, in some cases, it may be useful to measure both scaled muscle-activity and muscle activity for a patient. For example, embodiment of method 90 that describe monitoring a patient for excursion events related to threshold detection of scaled muscle-activity values may also track an overall muscle activity, such as a power or amplitude of muscle activity. Such muscle activity may be determined as described herein in relation to step 96.

[0072] In step 98, muscle-activity data may be partitioned or broken up in order to create at least one data set. A data set created by partitioning may sometimes be referred to as a partitioned data set. For example, muscle -activity data may be partitioned into a first partitioned data set. The first partitioned data set includes data that has been broken up into a plurality of parts or time- windows, each of the plurality of parts having a first duration width. Parts or time windows in a data set may be immediately adjacent each other or an intervening portion of time may exist between them parts. And, in some embodiments, adjacent time windows may be staggered or overlapped. In some embodiments, a data set may include time windows that may be separated in time. For example, in some embodiments, parts or time-windows of a data set may be separated in time to collect data over a long duration time period but minimizing overall amounts of collected data. Partitioning of muscle activity data and creation of one or more partitioned data sets is further described in relation to Figure 5. In the example shown therein, a first partitioned data set may include a plurality of parts of duration width x. A second partitioned data set may include a plurality of parts of duration width 2x.

[0073] In some embodiments, muscle activity data may be partitioned into a first data set including data in one or more parts or time -windows of a first duration width. The first duration width ranging from about 5 seconds to about 600 seconds. In some embodiments, the first duration width range may include a lower duration width boundary of about 5 seconds, about 10 seconds, 50 seconds, or about 100 seconds. In some embodiments, the first duration width range may include an upper duration width boundary of about 600 seconds, 400 seconds, or about 200 seconds.

[0074] In step 100, at least one partitioned data set may be scaled. The term“scaling” as used herein means to take a ratio or proportion of something. Scaling may, for example, be used to represent muscle -activity data in proportion to a reference value. For example, muscle-activity data may be scaled by dividing the data by a reference muscle-activity value. Scaling a partitioned data set may include determining an average value of muscle-activity data (or other standard for

characterizing muscle activity) for parts of the data set and then determining a ratio of the average value of muscle-activity data to a maximum muscle-activity value achieved for a patient. Thus, scaling a data set may provide individual scaled muscle-activity values for each part of a data set. A ratio may be expressed as a percentage value or other metric or value for comparing two numbers. A standard for characterizing muscle activity for a patient may include, for example, and without limitation, an average, a mean, a median, a mode or another suitable measure to express a central or typical value in a collection of data, and any combination thereof. And, unless the context clearly describes otherwise, in this specification, where one or more of the above standards are described another suitable standard may be substituted. For example, where an average value of muscle-activity data is described a median or other suitable standard of muscle activity may also be used.

[0075] In some embodiments, a maximum muscle-activity value achieved for a patient may refer to a maximum value achieved within an individual part of a data set. For example, scaling a data part may include determining an average value of muscle-activity data for a certain part and taking a ratio between the muscle -activity data for that part and a maximum muscle activity value achieved within that part. Scaling in this manner may be referred to as scaling data against or using a local- maximum-muscle-activity value. A maximum-muscle-activity value achieved for a patient may also refer to a maximum value achieved within all parts of a data set or some number of adjacent or nearby parts of a data set. For example, scaling a data part may include determining an average value of muscle -activity data for a certain part and taking a ratio between the muscle-activity data for that part and a maximum-muscle-activity value achieved over all data parts of a partition or some number of adjacent or nearby parts of a data set. Scaling in this manner may be referred to as scaling data against or using a global -maximum-muscle-activity value.

[0076] For example, as shown in Equation 1, a scaled value of muscle -activity data may be determined by 1) determining an average magnitude value of muscle -activity data in a part of a partitioned data set, and 2) dividing the average magnitude value of muscle-activity data in that part by a local -maximum -magnitude value (Magnitude _{(locai maxj}) of muscle -activity data achieved within that part.

Magnitude (scaled) = [Magnitude _(ave) / Magnitude _(i0Cai max)] Eqn. 1

Alternatively, as shown in Equation 2, a scaled value of muscle -activity data may be determined by 1) determining an average magnitude value of muscle-activity data in a part of a partitioned data set, and 2) dividing the average magnitude value of muscle-activity data in that part by a global- maximum -magnitude value (Magnitude _{(giobai maxj}) of muscle -activity data achieved over all parts of a data set or some number of adjacent or nearby parts of a data set.

Magnitude _(SCaied) = [Magnitude _(ave) / Magnitude _(gi0bai max)] Eqn. 2

The procedure may then be repeated for other parts of a data set in order to scale a data set over each part or time-window within the data set.

[0077] In some embodiments, scaling of muscle -activity data over a data set may include determining an average or mean value of muscle-activity data included in time windows among the data set. Scaling of muscle activity data may further include dividing the mean or average value of motor activity data included in the time windows by a reference value. For example, a reference value may be an empirically derived value based on measured periods of muscle exertion typically exhibited by during rest or when executing one or more tasks. For example, as shown in Equation 3, a scaled value of muscle-activity data may be determined by 1) determining an average magnitude value of muscle-activity data in a part of a partitioned data set, and 2) dividing the average magnitude value by a reference value (Magnitude _(ref>).

Magnitude _(scaled) = [Magnitude _(ave) / Magnitude _(Ref)] Eqn. 3

The procedure may then be repeated for other parts of a data set in order to scale the data set over each part or time -window within the data set.

[0078] In step 102, one or more scaled muscle -activity values may be compared to one or more muscle -activity thresholds. For example, scaled muscle-activity values for each part of a partitioned data set may be compared to one or more muscle -activity thresholds. Alternatively, a number of scaled muscle-activity value may be averaged together so as to generate an overall scaled muscle -activity value, and the overall scaled muscle-activity value may be compared to a muscle- activity threshold. An overall scaled muscle -activity value may comprise an averaged scaled muscle- activity value determined by averaging the results of individual scaled muscle-activity value calculations (e.g., calculations of muscle -activity for each of a plurality of parts of a partitioned data set) over a monitoring period or over a part of a monitoring period.

[0079] Muscle-activity thresholds may be established empirically. For example, empirically derived muscle -activity thresholds may be based on collected muscle -activity data for patients of a particular patient demographic. Thresholds may also be established from historical muscle -activity data collected for an individual patient. For example, a patient may be monitored over a first period of time to collect EMG signal over the first time period. The collected EMG signal in this first period of time may be used to establish a normal range of signal for the patient. For example, a normal range of signal for a patient may be defined as a range that extends some number of units of standard deviations from a median or average signal value. Threshold deviation from historical boundaries of muscle activity (e.g., a muscle activity excursion) may be based on whether patient muscle -activity data exceeds a value that is a threshold number of standard deviations from normal EMG data, or another suitable indicator of deviation may be used. For example, excursion events may be monitored in a second time period based on thresholds defined empirically from patient-specific data in the first time period.

[0080] In the step 104, one or more responses may be initiated. For example, as further described in the following embodiments, a response may include automatic transmission of one or more messages or reports to a patient or caregiver, organization of a report identifying one or more muscle -activity excursions, triggering activation of one or more additional sensors or execution of one or more additional data collection routines, instructing a caregiver or patient to take one or more medications, instructing a patient to engage in a low-physical -stress activity, adjustment of one or more treatment responses to a detected seizure, scheduling of one or more medical tests, or any combinations thereof.

[0081] For example, in an embodiment of the method 90, scaled muscle -activity data may be processed to characterize patient muscle activity as part of a monitoring regimen for identifying patient seizure activity, and one or more messages or reports may be automatically sent to a patient or caregiver. The patient may be monitored for seizure activity routinely during daily living, and EMG signal may be regularly collected, such as using a minimally intrusive mobile monitoring unit 12. Periodically, such as at regular intervals, muscle-activity data determined from the EMG signal may be analyzed. Regular intervals may refer to an analysis executed monthly, weekly, daily, hourly, or at some other suitable interval. For example, monitoring unit 12 may download collected EMG signal for processing of muscle-activity data when the monitoring unit 12 is plugged in or charging, such as may be executed through a hard wire connection about every 2 to 5 days. In that case, the monitoring unit 12 may advantageously provide collected EMG signal to a processor configured for review of muscle -activity data, such as may be included in the base station 14, without using power resources useful in mobile detection applications. Alternatively, collected EMG signal may be transmitted wirelessly (e.g., at some desired rate or interval) from monitoring unit 12 to another device and accessed for review. Or, monitoring unit 12 may itself be configured for processing collected EMG signal and characterizing patient muscle activity. And, at regular intervals, the collected EMG signal may be internally processed within monitoring unit 12 in order to analyze muscle activity for a patient.

[0082] One or more scaled muscle-activity values, such as may include an overall scaled muscle -activity value, may be compared to one or more muscle-activity thresholds in order to characterize muscle activity for said patient. If one or more scaled muscle-activity values meets a muscle -activity threshold (e.g., if some number of consecutive scaled muscle -activity values over a collection period meets a muscle-activity threshold), a message may be automatically organized and transmitted to one or more caregiver devices, such as devices 22, 24. Alternatively, a message may be sent to a patient or local caregiver who may, for example, be encouraged to contact the patient’s doctor to schedule a visit based on the detected muscle activity. Thus, the embodiment herein may, for example, be used to track a patient for changes in muscle activity and to encourage caregivers, patients or both to take appropriate actions.

[0083] Notably, the algorithms used herein may be readily automated. Thus, large amounts of muscle-related data may be collected and processed routinely. Accordingly, patients whose motor condition(s) may be changing may be identified rapidly. Appropriate actions, such as a change in a care strategy, may then be made in a timely manner. Moreover, sporadic conditions that may be difficult to identify during visits to a clinic may be diagnosed more effectively. Based on

identification of changes in muscle activity one or more additional diagnostic tests may be scheduled. For example, included among one or more additional diagnostic tests that may be scheduled based on identification of changes in muscle activity include a functional magnetic resonance imaging (fMRI) test, a computerized tomography (CT) test, a positron emission tomography (PET) test, additional electromyographic testing under controlled supervision at a medical facility, other tests and combinations thereof.

[0084] In another embodiment of the method 90, scaled muscle-activity data from a selected part of a collected EMG signal may be processed to characterize patient muscle activity, and one or more messages or reports may be automatically sent to a patient or caregiver. The patient may, again, be monitored for seizure activity routinely during daily living, and EMG signal may be regularly collected, such as using a minimally intrusive mobile monitoring unit 12. In this embodiment, one or more portions of EMG signal may be selected for analysis of patient muscle activity. It may be noted that Fig. 4 describes selection of EMG signal before processing to generate muscle-activity data. However, a selection step may alternatively be executed following partitioning, after scaling of data, or at other stages of analysis. Analysis may again be executed periodically, such as at regular interval. For example, on a weekly basis or at another rate, collected EMG signals or muscle-activity data may be selected for processing.

[0085] Selected EMG signal may be signal collected during one or more intermediate periods between recorded seizures. Selected EMG signal may also be signal collected within a period preceding a start of a detected seizure, such as about 2 minutes or about 60 minutes before a recorded seizure. Selected EMG signal may also be signal collected within about 10 minutes or about 60 minutes following completion of a detected seizure. For example, muscle -activity data from one or more of the above collection periods may be partitioned, scaled, and compared to one or more thresholds. Thresholds may be customized for the type of EMG signal or muscle-activity data selected. If one or more scaled muscle-activity value meets a muscle -activity threshold (e.g., if some number of consecutive scaled muscle -activity values meets a muscle -activity threshold), a message may be automatically transmitted to one or more caregiver devices, such as devices 22, 24.

[0086] Examination of muscle-activity data that may precede the start of a seizure may sometimes be used to identify one or more triggers that may cause a seizure. In some cases, a caregiver may further select one or more additional sensor types other than EMG that may be useful in helping the caregiver understand one or more pre-seizure triggers. For example, an additional sensor may sometimes comprise a chemical sensor, such as may be used to detect levels of patient blood glucose or sugar. In some cases, an additional sensor may comprise a saturated oxygen sensor. Data for the aforementioned sensors or other sensors may, for example, be collected along with pre seizure scaled muscle-activity data. In some cases, scaled muscle -activity values may be determined at least once every two hours or at some other interval. If a scaled-muscle activity value is found to meet one or more thresholds, one or more additional sensor collection routine may be adjusted or selectively executed (e.g., a rate of data collection or data collection itself“turning on a sensor” may be based on a level of scaled-muscle activity). Thus, collection of EMG signal and processing herein may sometimes be used as a trigger to control data collection using other sensors, such as to minimize power resources used for additional sensor data collection.

[0087] Examination of muscle-activity data and/or other data that may be present after completion of a seizure may sometimes be used to identify unusual recovery from a seizure. For example, embodiments herein may provide a protocol for automatically flagging or identifying data that may be useful for further review, such as may be used to identify that patient recovery from seizures has changed. For example, the protocol may be used to identify that a medication the patient may be taking may generally improve how a patient recovers from a seizure.

[0088] In another embodiment of the method 90, scaled muscle-activity data from a collected EMG signal may be processed to characterize patient muscle activity, and one or more therapeutic actions may be initiated based on the patient’s muscle activity. EMG signal may be collected over time and compared to one or more muscle-activity thresholds. If a scaled muscle-activity value is found to meet one or more thresholds, one or more therapeutic actions may be initiated. For example, it may be deemed based on an excursion (or some number of repeated excursions) wherein a scaled muscle -activity value meets one or more thresholds that a patient is experiencing abnormal muscle activity. Accordingly, a warning message may be sent to the patient or local caregiver. For example, a warning message may be sent to monitoring unit 12 or to base station 14. The message may, for example, suggest that a caregiver engage in a low stress activity, that a patient or local caregiver administer a medication, or both. For example, a message may suggest that it may be useful to provide the patient with one or more sedatives, such as a therapeutic dose of a benzodiazepine, such as diazepam, in a dose of about 0.5 mg to about 25 mg or about 2.5 mg to about 25 mg, which may be rectally, orally, or otherwise administered. A warning message may sometimes encourage a patient to engage in a low-stress or resting activity. Such may, for example, allow a monitoring unit 12 to more accurately establish a muscle-activity level for a patient. After engaging in a resting activity, scaled muscle -activity values may again be compared to one or more thresholds, and a decision may be made regarding if the patient may benefit from receiving a sedative or if another care action should be executed.

[0089] In another embodiment of the method 90, scaled muscle-activity data may again be processed as part of a protocol for monitoring a patient for seizure activity. In this embodiment, data may further be collected during one or more dedicated periods wherein a patient is known to be engaging in a particular behavior. For example, in some embodiments, a patient or local caregiver may regularly (or when prompted) access one or more profile settings, such as may be included on monitoring unit 12 or base station 14. A profile setting may be used to identify that the patient is resting calmly such as while reading a book, watching television, or engaged in some other low- physical-stress activity. One or more device settings (e.g., detection gain setting or dynamic range setting) may sometimes be adjusted when a patient or local caregiver has selected the one or more profile settings. For some patients, muscle-activity data collected in this more controlled manner may be used to more accurately track changes in an underlying motor condition. One or more scaled muscle -activity values, such as an overall scaled muscle-activity value, may again be compared to one or more muscle-activity thresholds in order to characterize muscle activity for the patient. If an overall scaled muscle -activity value meets a muscle -activity threshold or if some number of consecutive scaled muscle -activity values meets a muscle -activity threshold, a message or report indicating such may be generated, such as may be automatically transmitted to one or more caregiver devices. Thus, a patient may routinely engage in one or more activities as part of a protocol for monitoring the patient for seizures and for identifying changes in patient muscle activity that may reflect changes in muscle activity.

[0090] Figure 6 is a flowchart of the method 110 for monitoring a patient using

electromyography and characterizing patient muscle activity. The method 110 may again be executed using system 10 or another suitable system may be used. As also described in relation to method 90, method 110 may be used to identify changes in muscle activity that may indicate a change in a patient condition. For example, unless the context indicates otherwise, any of the embodiments of method 90 that include comparison of one or more scaled muscle-activity values determined from at least one partitioned data set to one or more thresholds may be combined with embodiments of method 110. However, in the method 110, muscle-activity data is partitioned into at least two partitioned data sets, and scaled muscle-activity values between the at least two data sets may then be compared. For example, a plurality of partitioned data sets may be generated, each of the plurality of partitioned data sets including a different duration width. Differences in scaled muscle-activity values between two or more partitioned data sets may then be determined. It has been found that differences in scaled muscle -activity values between these data sets may be used to identify patent’s who meet one or more muscle -activity profiles, such as profiles that may indicate the presence of one or more motor disorders. Other data may also be used to define a muscle-activity profile. For example, in addition to differences between scaled muscle-activity values (e.g., differences in scaled muscle-activity between two or more partitioned data sets), detection of or knowledge of seizure activity, overall muscle activity level, and demographic or other information for a patient may be used to define a muscle -activity profile.

[0091] Muscle activity profiles may be used to identify patients with different motor conditions without having to monitor a patient over extended periods of time. Accordingly, by looking at different partitioned data sets as in method 110, patients that may have an underlying motor condition causing or concomitantly present together with seizure activity may sometimes be identified more efficiently than in the method 90. In some situations, method 110 may be used to screen EMG data collected from patients who may be susceptible to seizures for the presence of one or more underlying motor condition that may be in addition to epilepsy. In some cases, patients incorrectly diagnosed or suspected of having one motor disorder may be identified as having a different motor disorder.

[0092] At least some of the steps in method 110 are similar to steps in the method 90. For example, steps 112-116, which, generally, involve collection of EMG signal, detection of seizures, selection of EMG signal, and processing of EMG signal to provide muscle-activity data over time, apply to both methods. In the sake of brevity, a detailed description of those steps and has not been repeated. Moreover, various protocols for accessing collected EMG signal or data for periodic or regular processing are described in relation to method 90. Generally, unless the context indicates otherwise, those protocols may be applied in any of the embodiments described in relation to method 110. As shown in step 118 of method 110, muscle-activity data is partitioned into at least two partitioned data sets. In step 120, the at least two data sets may be scaled to determine scaled muscle- activity values for the at least two partitioned data sets. In step 122, scaled muscle-activity values for the at least two partitioned data sets may be compared. And, based on the comparison, one or more responses may be initiated as shown in step 124.

[0093] Referring to step 118, muscle -activity data may be partitioned into a first partitioned data set including data in one or more parts or time -windows of a first duration width. Muscle activity data may also be partitioned into a second partitioned data set including data in one or more parts or time-windows of a second duration width. For example, the first duration width may be from about 5 seconds to about 50 seconds. The first duration width may include a lower duration width boundary of about 5 seconds, about 10 seconds, or about 20 seconds. The first duration width range may include an upper duration width boundary of about 50 seconds or about 25 seconds. The second duration width may be about 200 seconds to about 600 seconds. The second duration width may include a lower duration width boundary of about 200 seconds, about 250 seconds, or about 300 seconds. The second duration width may include an upper duration width boundary of about 600 seconds, about 500 seconds, or about 400 seconds.

[0094] In step 120, each of the at least two partitioned data sets may be scaled. For example, scaling of data sets may comprise scaling data against or using a local-maximum-muscle-activity value, a global-maximum-muscle-activity value, or a reference muscle-activity value. One or more overall scaled muscle -activity value may also be determined for each of the at least two partitioned data sets. As described previously, an overall scaled muscle -activity value may comprise an average scaled muscle-activity value determined by averaging the results of individual scaled muscle-activity value calculations for each of a plurality of parts of a partitioned data set over an entire collected monitoring period or part thereof.

[0095] As shown in step 122, scaled muscle -activity values for at least two partitioned data sets may be compared. A comparison of scaled muscle-activity values may be used to determine a measure of difference between the at least two partitioned data sets. A measure of difference may, for example, be expressed as a percentage or ratio between two different values. Alternatively, another suitable measure of difference may be used. In some embodiments, this measure of difference may then be tracked over time and compared to one or more thresholds to monitor a patient for changes in muscle activity. A measure of difference may also be compared to one or more thresholds to determine a muscle -activity profile for a patient.

[0096] The at least two partitioned data sets may comprise parts of different duration widths. For example, a first partitioned data set may include parts of a first duration width of about 5 seconds to about 50 seconds, which is less than a second duration width of about 200 seconds to about 600 seconds for parts of a second partitioned data set. Notably, muscle activity derived from patients with different types of motor disorders may vary to different degrees as one examines scaled muscle- activity values as a function of duration width. Accordingly, comparing scaled muscle-activity values from partitioned data sets including parts of different duration widths may be used for identifying patients whose muscle activity meets one or more muscle-activity profiles, such as a muscle-activity profile indicative of an underlying or more specific disorder that may be causing or present together with epilepsy. Alternatively, a muscle -activity profile may be established for a motor disorder distinct from epilepsy. Muscle-activity profiles may also be established for persons who may not have a motor disorder. For example, a patient may be monitored for muscle -activity and found to match a profile for normal muscle activity.

[0097] For example, some patients who may suffer from a common form of epilepsy may experience seizures but otherwise may exhibit substantially normal motor activity when not having a seizure. A muscle-activity profile may be established to identify those patients. Muscle activity for such patients, may, generally, show levels of scaled muscle activity that show significant difference as one increases a partition duration width. Other patients may also be diagnosed with epilepsy. For example, some patients may be diagnosed with epilepsy and may exhibit increased involuntary activation of muscle. Other patients may also be diagnosed with epilepsy but may exhibit decreased involuntary activation of muscle. Muscle -activity profiles may also be used to identify those patients.

[0098] One or more patient profiles may also be defined for a motor condition that is different from epilepsy. For example, muscle-activity profiles may be used to describe patient’s with motor disorders that cause increased involuntary activation of muscle fibers, such as cerebral palsy, Parkinson’s disease, and at least some conditions associated with traumatic brain injuries. Those muscle -activity profiles may, over some duration width ranges, show lesser variation or difference with changing duration width for different partitioned data sets than profiles for persons without a motor disorder.

[0099] As shown in step 124, one or more responses may be initiated. For example, as further described in the following embodiments, a response may include automatic transmission of one or more messages or reports to a patient or caregiver, organization of a report identifying one or more muscle -activity excursions or identification of a new muscle -activity profile for a patient, triggering activation of one or more additional sensors or execution of one or more additional data collection routines, instructing a caregiver or patient to take one or more medications, instructing a patient to engage in a low-physical -stress activity, adjustment of one or more treatment responses to a detected seizure, scheduling of one or more medical tests, or any combinations thereof.

[0100] In one embodiment of method 110, EMG signal for a patient may be used to identify one or more muscle-activity profile that may indicate that the patient has a form of epilepsy without an underlying or more specific motor condition that may significantly affect muscle activity during times between seizures. For example, a patient, such as a patient known to have had seizures or otherwise suspected to be susceptible to seizures, may be monitored for seizure activity routinely during daily living, and EMG signal may be collected, such as using a mobile monitoring unit 12. Periodically, such as at regular intervals, collected EMG signal may be processed to identify a muscle -activity profile. Additionally, one or more levels of difference between scaled muscle -activity values may also be determined and used to track a patient condition. In addition to a level of difference between scaled muscle-activity values one or more additional factors may be used to identify the profile. For example, the one or more additional factors used to identify the muscle- activity profile may include a number of detected seizures, an overall muscle activity for the patient, demographic information for the patient, the time or presence of activities engaged in by the patient when one or more forms of muscle activity are identified, or combinations of the factors thereof.

[0101] Processing of a collected EMG signal may, for example, be executed after downloading EMG signal (or muscle-activity data processed therefrom) as may be executed when charging monitoring unit 12. Alternatively, processing of a collected EMG signal (or processed muscle -activity data) may be executed after transmitting EMG signal wireless from monitoring unit 12 to one or more other devices of the system 10, such as the devices 14, 32. Still in other cases,

EMG signal data and/or other data may be stored in a unit of memory and the unit of memory may be physically shipped to a caregiver for external analysis. Once downloaded, EMG data may then be partitioned into at least two partitioned data sets and scaled as described herein. Alternatively, monitoring unit 12 may itself be configured to process EMG signal by generating at least two partitioned data sets and scaling data as described herein.

[0102] For example, a first partitioned data set may be characterized by a duration width of between about 5 seconds to about 50 seconds. A second partitioned data set may be characterized by a duration width of between about 200 seconds to about 600 seconds. One or more scaled muscle- activity values from the first partitioned data set may be compared to one or more scaled muscle- activity values from the second partitioned data set. If the scaled muscle-activity values levels in the two data sets differ by greater than about 40% to about 100%, the patient may be identified as meeting a muscle -activity profile consistent with epilepsy and without a more specific motor disorder that may significantly affect muscle activity. For example, for such a muscle-activity profile, a difference may be greater than about 40%, greater than about 60%, or greater than about 100%. If the one or more scaled muscle-activity values levels in the two data sets differ by lesser than about 20% to about 40%, the patient may be identified as meeting a muscle-activity profile consistent with them having an underlying or more specific motor condition.

[0103] A caregiver may receive information identifying one or more muscle-activity profiles for a patient. For example, if a patient profile changes a caregiver may be provided a report indicating the change in profile. This report (or a related message) may sometimes be provided automatically in response to a change in a muscle -activity profile. Alternatively, a report may be generated when a caregiver requests the information. For example, a caregiver may request a report providing muscle activity data for a patient in advance of a scheduled visit with the patient or at some other time. The report may include one or more charts or other indications showing the patient’s muscle activity profile. A caregiver may also generate a report providing muscle activity data for a patient. For example, if a caregiver has access to one or more of the computer programs described herein, a caregiver may, for example, select, partition, scale, and compare data sets as described herein to generate a report providing an indication of a muscle-activity profile identified for a patient. [0104] In another embodiment of method 110, EMG signal may be collected and used to identify if a patient’s muscle activity meets one or more muscle-activity profiles, the one or more muscle -activity profiles based on meeting a first condition or preferably meeting a first condition and at least one other additional condition. A first condition may be based on whether one or more measures of difference between at least two partitioned data sets meets one or more thresholds. One or more additional condition may further be used to more fully characterize the patient. An additional condition may, for example, be based on a magnitude or relative magnitude of muscle activity for a patient.

[0105] A first condition may, for example, be used to identify a muscle-activity profiles to identify patients with epilepsy or a muscle -activity profile may identify patients with epilepsy and without an underlying motor disorder affecting resting muscle or involuntary muscle activity. A first condition may be met if a level of difference, such as a percentage difference, between scaled muscle- activity values in at least two partitioned data sets meets one or more of a maximum threshold, a minimum threshold, or both. For example, if a level of difference for a patient is within an expected range between the maximum and the minimum thresholds the patient muscle -activity may be deemed consistent with a muscle-activity profile for a patient with epilepsy and without a more specific motor disorder causing abnormal activation of muscle activity from involuntary muscle activity. One or more additional conditions including, for example, detection of seizure activity or knowledge of seizure activity, overall muscle activity, and demographic or other information for a patient may also be used to define the aforementioned muscle-activity profiles.

[0106] A first condition may, for example, be used to identify one or more muscle -activity profiles, such as may be used to identify patients with one or more motor disorders. For example, if a level of difference, such as a percentage difference, between scaled muscle -activity values in two or more partitioned data sets differ by less than about 20% to about 40%, a patient may be identified as meeting a muscle-activity profile consistent with one or more motor disorders that tend to cause increased involuntary activation of muscle fibers, such as cerebral palsy, Parkinson’s disease, and some conditions associated with a traumatic brain injury. For example, for such a muscle -activity profile, a difference may be lesser than about 40%, lesser than about 30%, or lesser than about 20%.

If a level of difference, such as a percentage difference, between scaled muscle-activity values in two or more partitioned data sets differ by greater than about 40% to about 100% the patient may meet a first condition of a muscle-activity profile consistent with epilepsy. A difference may, for example, be greater than about 40%, greater than about 60%, or greater than about 100%. One or more additional conditions including, for example, detection of seizure activity or knowledge of seizure activity or lack thereof, and demographic or other information for a patient may again be used to define the aforementioned muscle-activity profiles.

[0107] For example, an additional condition to define a muscle -activity profile may be a magnitude or relative magnitude of muscle activity for a patient. A magnitude of muscle activity may be determined and expressed as one or more of an amplitude, power content, or other standard of measurement of muscle activity for a patient. Muscle activity may be determined as described herein with respect to step 118 of the method 110 and in related step 96 of the method 90. A relative magnitude of muscle activity may be determined based on a comparison of one or more standards of measurement of muscle activity to one or more reference values or value ranges.

[0108] A reference value may, for example, be obtained empirically and/or by pooling data collected from a test group of patients, such as a test group characterized as associated with one or more patient demographics. For example, members of a test group may be defined by various characteristics including, for example, any combination of age, gender, ethnicity, weight, level of body fat, fat content in the arms, fat content in the legs, fitness level, medical history, or members of a test group may be defined by other characteristics.

[0109] Overall magnitudes of muscle activity and variation therein (e.g., standard deviations) may be determined for a test group. For example, an overall magnitude and standard deviation of one or more of an amplitude, power content, or other standard of measurement of muscle activity for an EMG signal may be determined from EMG signal collected for a test group of patients. If a patient is determined to have a magnitude of muscle activity that is greater than an average magnitude of muscle activity for the test group by some threshold amount (e.g., an amount greater than about 1 to about 3 standard deviations from an average or mean magnitude value) a patient may be characterized as having a high overall magnitude of muscle activity. For some muscle-activity profiles, an additional condition may be met if a muscle activity for a patient is greater than a reference level of muscle activity by some threshold amount, such as an amount greater than about 1 to about 3 standard deviations from an average or mean magnitude of a muscle-activity value of a test group. For example, a patient may be shown to have a higher magnitude of muscle activity than other persons of comparable age, gender, weight and fitness level. Likewise, if an individual patient is determined to have a magnitude of muscle activity that is lesser than an average magnitude of muscle activity for the test group by some threshold amount (e.g., an amount greater than about 1 to about 3 standard deviations from an average or mean magnitude value) a patient may be characterized as having a low overall magnitude of muscle activity. For some muscle-activity profiles, an additional condition may be met if a muscle activity for a patient is lesser than a reference level of muscle activity by some threshold amount, such as an amount more than about 1 to about 3 standard deviations from an average or mean magnitude value of a test group.

[0110] Alternatively, one or more reference values for a magnitude of muscle activity may be obtained by pooling data collected from a test group of patients known to have one or more motor disorders. If such reference values are available, a muscle-activity profile may be defined based on either of a level of similarity or difference with the reference value. For example, an additional condition for matching a muscle -activity profile may be met if a muscle activity for a patient is similar to a reference level of muscle activity, such as an amount within about 1 to about 3 standard deviations from an average or mean magnitude value of a test group.

[0111] One or more test groups may comprise children at one or more stages of

development. For example, one or more test groups of children may be used to determine a reference magnitude for muscle activity for patients during one or more stages of development. Patient’s exhibiting magnitudes of muscle activity different from those of a healthy test group may be identified. Particularly for this group, the presence of abnormal magnitudes of EMG signal may, by itself, be insufficient to warrant flagging a patient as having a condition consistent with a motor disorder. However, where magnitudes of muscle activity are significantly higher than a test group and variation of levels of scaled muscle activity vs. duration width are low (e.g., as shown for Muscle Activity State B in Table 1 shown below) a patient may be flagged as showing signs consistent with the presence of a condition associated with impaired muscle activity, such as cerebral palsy. Where such activity is consistently found for EMG signals selected during periods of rest or substantially unbiased by patient seizures, correlation with a cerebral palsy condition may be enhanced.

[0112] Also, by way of example, one or more test groups may include one or more groups of elderly persons. Elderly patients exhibiting magnitudes of muscle activity different from those of a healthy test group of elderly patients may be identified. Where magnitudes of muscle activity are significantly higher than a test group and variation of levels of scaled muscle activity vs. duration width are low (e.g., as again shown for Muscle Activity State B in Table 1) a patient may be flagged as showing signs consistent with the presence of a condition associated with impaired muscle activity, such as Parkinson’s disease. And, where such activity is consistently found for EMG signals selected during periods of rest or substantially unbiased by patient seizures, correlation with Parkinson’s disease condition may be enhanced.

[0113] Based on an analysis of muscle activity and level of difference or variation between scaled muscle-activity values from different partitioned data sets a patient may be characterized as shown below in Table 1. It may be noted that use of the terms below (e.g.,“high,”“normal,” and “low”) may carry a meaning that is dependent on the test group. For example, while both Parkinson’s disease and cerebral palsy may both be described by muscle activity profile B in Table 1, the test groups for the two populations may be much different. That is, of course, reference levels of muscle activity for young children and elderly may be significantly different. Reference levels for those demographic populations may be readily determined based on empirical analysis as described herein and set for a given EMG sensor with a particular amplifier, gain, or other device setting.

[0114] Table 1

[0115] As described above, various muscle-activity profiles may be established for different patients, such as based on meeting one or more conditions for a patient. Further based on identification of a muscle-activity profile for a patient one or more additional responses may be initiated. For example, collected EMG signal may be processed and used to identify a muscle-activity profile for a patient. If a change in muscle -activity profile is identified, one or more reports or messages may be sent to a caregiver. For example, in response to identification of a changed or new profile, one or more additional tests may be scheduled to further evaluate the patient. For example, included among one or more additional diagnostic tests that may be scheduled based on a change in muscle -activity profile include a functional magnetic resonance imaging (fMRI) test, a computerized tomography (CT) test, a positron emission tomography (PET) test, additional electromyographic testing under controlled supervision at a medical facility, other tests and combinations thereof.

[0116] In another embodiment of method 110, EMG signal for a patient may be used to identify one or more muscle -activity profiles based on a plurality of different data partitions. A plurality of partitioned data sets may be generated from muscle -activity data over time. Scaled muscle -activity data, such as an overall scaled muscle -activity value, may be determined for each of the plurality of partitioned data sets. Thus, an overall scaled muscle -activity for each of the plurality of partitioned data sets may be established. And, an overall scaled muscle-activity dependence on duration width may be determined. Accordingly, as shown by the data curve in Figure 7, scaled muscle -activity may be plotted against the varying duration widths used for partitioning of the plurality of partitioned data sets.

[0117] For example, as shown in Fig. 7 one or more slopes (shown as dashed line 300) may be calculated from data for a plurality of partitioned data sets, such as may be defined over a range of range of duration widths, may be determined. The one or more slopes may be compared to one or more threshold slopes as may be used to identify one or more muscle -activity profiles. For example, if one or more slopes exceeds one or more maximum slope thresholds a muscle -activity profile may be identified. The muscle activity profile may, for example, be consistent with muscle activity for a patient with epilepsy. If one or more slopes is lesser than one or more minimum slope thresholds a muscle -activity profile may be identified. The muscle activity profile may, for example, be consistent with a motor disorder such as cerebral palsy or Parkinson’s disease.

[0118] In some cases, one or more reference functions or reference curves indicating or showing a dependence of scaled muscle activity with duration width may be determined for patients of a reference group. For example, a reference group may comprise healthy patients without a motor disorder or a reference group may comprise patients already diagnosed with a motor disorder. Muscle activity data may be determined for members of the reference group and used to generate data for scaled muscle-activity as a function of duration width. For example, a reference curve or reference function may be determined, the reference curve or reference function used to define a muscle-activity profile. One or more suitable mathematical methods for estimating or modeling a difference between data and the reference curve or functions (e.g., between a first data set for a particular patient and a second or reference curve or function) may then be used to determine if the patient data meets a muscle -activity profile. For example, if scaled muscle-activity data for a plurality of partitions over duration width for a certain patient matches a reference curve or function defined for epilepsy, the patient may be identified as having a muscle-activity profile consistent with epilepsy. Likewise, if scaled muscle-activity data for a plurality of partitions over duration width for a certain patient matches a reference curve or function defined for cerebral palsy, the patient may be identified as having a muscle -activity profile consistent with cerebral palsy. Likewise, if scaled muscle-activity data for a plurality of partitions over duration width for a certain patient matches a reference curve or function defined for Parkinson’s disease, the patient may be identified as having a muscle -activity profile consistent with Parkinson’s disease. As also described above, one or more additional conditions may be also be used to define one or more of the above muscle-activity profiles.

[0119] Thus, various muscle-activity profiles may be established for different motor disorders, such as based on one or more conditions. Further based on identification that patient data matches a muscle -activity profile, one or more responses may be initiated. For example, if patient data is collected over time and a matched muscle -activity profile changes (e.g., a new profile is found for a patient), one or more reports or messages may be sent to a caregiver. For example, in response to identification of a changed or new profile, one or more additional tests may be scheduled to further evaluate the patient. For example, included among one or more additional diagnostic tests that may be scheduled based on a change in muscle -activity profile include a functional magnetic resonance imaging (fMRI) test, a computerized tomography (CT) test, a positron emission tomography (PET) test, additional electromyographic testing under controlled supervision at a medical facility, other tests and combinations thereof.

[0120] In another embodiment of method 110, EMG signal for a patient may be used to identify one or more muscle -activity profile that may indicate that the patient suffers from one or more sleep disorders associated with parasomnia. To facilitate understanding of parasomnia, EMG signal may be collected, and a portion of EMG may be selected that corresponds with a monitoring period when the patient was sleeping.

[0121] Figure 8 is a flowchart of the method 130 for characterizing patient muscle activity. The method 130 may be executed using system 10 and/or elements described in reference therein or another suitable system may be used. For example, EMG signals may be collected using monitoring unit 12, wherein the unit is configured as a data recorder, such as without seizure detection capability (or without enabling seizure detection or alarming capability of the device). Alternatively, monitoring unit 12 may include seizure detection capability but the method 130 may create one or more partitions and scale partitioned data without selecting portions of the data based on seizure detection. The method 130 may, for example, be used for monitoring patient muscle activity in patient’s who may not have had or who may not be expected to have seizures but who may be monitored for other motor disorders.

[0122] Referring to step 132, an EMG signal may be collected. For example, EMG signal may be collected using one or more monitoring units 12, such as may be disposed on or near one or more patient muscles, such as the patient’s biceps, triceps, hamstrings, quadriceps, frontalis, temporalis, or wrist flexor muscles. Other monitoring units 12, such as may simply comprise EMG electrodes and means for storing or transmitting EMG signals, may be attached near one or more patient joints. As shown in step 134, collected EMG signal may be processed to determine an amplitude, magnitude, power content, or other standard of muscle activity in order to provide muscle- activity data over time. In step 136, muscle-activity data may be partitioned to provide at least one partitioned data set. In step 138, the at least one partitioned data set may be scaled. And, as shown in step 140, one or more scaled muscle-activity values may be compared to one or more thresholds and/or other scaled muscle-activity values. In step 142, one or more responses may be initiated based on this comparison.

[0123] For example, in one embodiment, a patient may be monitored for early signs of one or more motor disorders. At some desired rate, collected EMG signal may be routed for analysis. For example, any of the various protocols for accessing collected EMG signal or data for periodic or regular processing as described above may be used. For example, EMG signal may be downloaded for review when a monitoring unit 12 is connected to a power source during charging. Routed EMG signal may be partitioned into a first partitioned data set including a duration width from about 5 seconds to about 200 seconds and a second partitioned data set including a duration width from about 300 seconds to about 600 seconds. Alternatively, a plurality of partitioned data sets may be generated, such as described in relation to Fig. 7 and method 110.

[0124] Scaled muscle-activity values may be determined for each of the first and second partitioned data sets (or for a plurality of partitioned data sets) and scaled-muscle activity values between different partitions may then be compared to establish a level of difference. This level of difference (e.g., a percentage or other metric of difference) may be compared to one or more expected or threshold levels of difference. If the level of difference meets one or more difference thresholds, a warning message or report may be sent to a caregiver. Or, a report may be generated, such as weekly or monthly, and automatically sent to a caregiver. Excursion values wherein the level of difference meets one or more thresholds may further be marked or listed. A caregiver may further access collected EMG signal for each muscle and select to view one or more metrics suitable for showing a level of coherence for signals collected from the two muscles.

[0125] Two or more scaled muscle-activity values may also be determined and used to determine if patient muscle activity matches one or more muscle -activity profile. For example, as described in relation to the various embodiments described in method 110, muscle-activity profiles may be based on a percentage difference between scaled muscle-activity values in two or more partitioned data sets, based on a plurality of different partitions over a range of duration widths (e.g., as may be characterized by one or more slopes or curve matching techniques), based on one or more conditions (e.g., as may include scaled muscle -activity values and one or more of a muscle activity or relative muscle activity), or combinations thereof.

[0126] Muscle-activity profiles for which a patient data matches may be tracked over time such as may be used to initiate one or more responses. For example, based on identification of a muscle -activity profile for a patient one or more additional responses may be initiated. For example, collected EMG signal may be processed and used to identify a muscle -activity profile for a patient. If a change in muscle-activity profile is identified, one or more reports or messages may be sent to a caregiver. For example, in response to identification of a changed or new profile, one or more additional tests may be scheduled to further evaluate the patient. For example, included among one or more additional diagnostic tests that may be scheduled based on a change in muscle-activity profile include a functional magnetic resonance imaging (fMRI) test, a computerized tomography (CT) test, a positron emission tomography (PET) test, additional electromyographic testing under controlled supervision at a medical facility, other tests and combinations thereof.

[0127] Figure 9 is a flowchart of the method 150 for characterizing patient muscle activity.

In the method 150, stored data for one or more collected EMG signals may be accessed from one or more units of memory. For example, with reference to the system 10, various collected EMG signals may be stored in database 26 or in another unit of memory. In execution of the method 150, a user may access data from the database 26 or other unit of memory and process the data to examine patient muscle activity. For example, a user device 22, 24, and/or 32 may include an installed program including instructions for accessing stored EMG data and analyzing the data to determine levels of patient muscle activity. Once EMG data is accessed for processing, the data may be analyzed as described in the aforementioned embodiments of methods 90, 110, 130, and 150. For example, scaled muscle -activity values from one or more partitioned data sets may be compared to one or more thresholds as may be used to monitor a patient condition. Alternatively, a difference value between scaled muscle-activity values obtained from two or more partitioned data sets may be used to track patient muscle activity and monitor a patient condition. Accessed EMG data may also be used to identify that patient muscle activity meets the conditions of one or more muscle-activity profdes.

[0128] Muscle-activity profdes for which a patient data matches may be tracked over time such as may be used to initiate one or more responses. For example, based on identification of a muscle -activity profile for a patient one or more additional responses may be initiated. For example, collected EMG signal may be processed and used to identify a muscle -activity profile for a patient. If a change in muscle-activity profile is identified, one or more reports or messages may be sent to a caregiver. For example, in response to identification of a changed or new profile, one or more additional tests may be scheduled to further evaluate the patient. For example, included among one or more additional diagnostic tests that may be scheduled based on a change in muscle-activity profile include a functional magnetic resonance imaging (fMRI) test, a computerized tomography (CT) test, a positron emission tomography (PET) test, additional electromyographic testing under controlled supervision at a medical facility, other tests and combinations thereof.

[0129] For example, in step 152, a patient may access stored EMG data such as may be used to obtain muscle-activity data over time for a patient. In step 154, muscle-activity data may be partitioned in order to obtain one or more partitioned data set. In step 156, the one or more partitioned data sets may be scaled. For example, scaling of data sets may comprise scaling data against or using a local-maximum-muscle-activity value, a global-maximum-muscle-activity value, or a reference muscle -activity value. An overall scaled muscle -activity value may also be determined for each of the at least two partitioned data sets. As described previously, an overall scaled muscle -activity value may comprise an average scaled muscle-activity value determined by averaging the results of individual scaled muscle -activity value calculations for each of a plurality of parts of a partitioned data set. In step 158, one or more scaled muscle -activity values may be compared to one or more thresholds and/or other scaled muscle -activity values.

[0130] In some embodiments, one or more computer programs may be used to execute the methods herein. For example, one or more processors in any of the devices 12, 14, 22, 24, or 32 may be configured with instructions for processing collected signals to characterize patient muscle activity. For example, one or more of the devices 14, 22, 24, or 32 may access muscle activity data and any associated meta data over time when the signal or data is transmitted to the device or downloaded to the device via a hardwire connection. Out of clarity, reference herein may be made to muscle -activity data. This terminology may specifically focus on embodiments wherein amplification, analog-to- digital conversion, rectification or other steps to calibrate or normalize a collected EMG signal have been executed before data is accessed by the devices 14, 22, 24, or 32. However, it should be understood that collected EMG signal or data in another form may also be received in other embodiments.

[0131] A computer program in one or more of the devices 12, 14, 22, 24, or 32 may include instructions for selecting one or more portions of muscle activity data over time and generating one or more partitioned data sets. Alternatively, one or more partitioned data sets may be generated without a specific selection step. Selection of one or more portions of muscle activity data may be accomplished by scanning muscle -activity data and associated meta data for one or more time-stamps identifying a detected seizure. For example, the data may already be time-stamped to identify when a recorded seizure was detected, such as may be encoded in meta data. For example, time-stamping may have been executed in monitoring unit 12, such as may have been executed in real-time detection of seizures. Alternatively, a computer program may be programmed to process muscle activity data and execute one or more seizure -detection algorithms to identify one or more seizure events. For example, any of the seizure-detection routines described herein or other suitable routines may be used to identify a recorded seizure in accessed muscle-activity data.

[0132] A computer program in one or more of the devices 12, 14, 22, 24, or 32 may further include instructions for automatically partitioning data, scaling partitioned data, and comparing scaled muscle -activity data to one or more thresholds. For example, one or more routines configured with instructions for executing the aforementioned steps may be stored in memory. Likewise, thresholds and/or other values used therein may be stored and accessed from memory as needed. Routines and memory may be stored in one or more local units of memory in the devices 12, 14, 22, 24, or 32. Alternatively, instructions and or other stored values may be accessed or downloaded from a remote location. For example, the one or more devices 12, 14, 22, 24, or 32 may access instructions and or stored values from remote database 26.

[0133] One or more automated messages or reports may be generated in some of the embodiments herein. For example, a message may be sent to a caregiver that one or more monitored values (e.g., scaled muscle activity values, difference values of scaled-muscle activity between two or more partitions, or slopes) have met one or more thresholds. Such a message may, for example, be sent as a text message to a caregiver device together with a link to access one or more control charts. Instructions to send the message may be automatically encoded in the programs herein. For example, if at least some number of excursions are identified over time or if some rate of excursions is identified, a message may be transmitted. A control chart may also be sent to a caregiver. For example, as shown in Figure 10, one or more excursions 200 may be identified in a control chart.

[0134] Although the methods, systems, apparatuses, computer programs disclosed, and their advantages, have been described in detail, it should be understood that various changes, substitutions and alterations may be made herein without departing from the invention as defined by the appended claims. Moreover, the scope of the present application is not intended to be limited to the particular embodiments of the methods, systems, apparatuses, computer programs and steps described in the specification. Use of the word "include," for example, should be interpreted as the word "comprising" would be, i.e., as open-ended. As one will readily appreciate from the disclosure, processes, machines, manufactures, compositions of matter, means, methods, or steps presently existing or later to be developed that perform substantially the same function or achieve substantially the same result as the corresponding embodiments described herein may be utilized. Accordingly, the appended claims are intended to include within their scope such processes, machines, manufactures, compositions of matter, means, methods or steps.

Claims

CLAIMS We claim:

1. A method of monitoring a patient susceptible to seizures for seizure activity and

characterizing muscle activity for the patient using electromyography (EMG), the method comprising:

disposing an EMG monitoring unit on or near one or more muscles of the patient and collecting an EMG signal;

executing one or more seizure-detection routines configured for processing said EMG signal and identifying portions of said EMG signal wherein the patient is having a seizure or has had a seizure;

selecting one or more remaining portions of said EMG signal, the remaining portions of said EMG signal excluding said portions of said EMG signal wherein a patient is having a seizure or has had a seizure;

processing said one or more remaining portions of said EMG signal to provide muscle- activity data over a time period;

partitioning said muscle activity data to create a first partitioned data set, the first

partitioned data set including a plurality of data parts of a first duration width;

scaling said plurality of data parts of said first partitioned data set in order to provide scaled muscle-activity values for said plurality of data parts, the scaling of said plurality of data parts comprising determining an average value of muscle -activity data for each of said plurality of data parts to provide a plurality of average muscle- activity values and then dividing each of said plurality of average muscle -activity values by a maximum-muscle-activity value for said patient;

comparing one or more of said scaled muscle -activity values to one or more muscle- activity thresholds; and

initiating one or more responses based on the comparing of one or more of said scaled muscle -activity values to one or more of said muscle -activity thresholds, the one or more responses including at least one of adjusting data collection for one or more sensors, automatically instructing a caregiver or patient to take one or more medications or to engage in a low-physical -stress activity, and transmitting a message identifying one or more muscle -activity excursions in which said scaled muscle- activity value may have met said one or more muscle-activity thresholds.

2. The method of claim 1, said EMG monitoring unit being disposed on or near one or more of the patient’s biceps, triceps, hamstrings, quadriceps, frontalis, temporalis, or wrist flexor muscles.

3 The method of claim 1 further comprising collecting one or more EMG signals from electrodes disposed on or near a pair of muscle controlling movement of a joint of the patient or on proximal and distal sides of said joint of the patient.

4. The method of claim 1, said one or more seizure-detection routines include at least one of a routine configured to calculate a T-squared value, a routine configured to calculate a principal component value, a routine configured to calculate a number of zero-crossings exhibiting a hysteresis, and a routine configured to calculate a threshold seizure-detection ratio between signals in two different frequency bands.

5. The method of claim 1, said one or more remaining portions of said EMG signal comprise signal collected during a pre-seizure time period.

6. The method of claim 5, said one or more remaining portions of said EMG signal comprising data collected within about 2 minutes to about 60 minutes preceding a start of a detected seizure.

7. The method of claim 1, said one or more muscle-activity thresholds comprising values that are about 3 standard deviations from historical limits of muscle -activity achieved by said patient.

8. The method of claim 1, said one or more muscle-activity thresholds comprising values based on collected muscle-activity data for patients of a particular patient demographic.

9. The method of claim 1, the maximum -muscle-activity-value being a local -maximum -muscle- activity value achieved by the patient in each of said plurality of parts of said first partitioned data set.

10. The method of claim 1, the maximum -muscle-activity value being a global -maximum- muscle -activity value achieved by the patient over said time period.

11. A method of characterizing a patient’s muscle activity and identifying one or more muscle- activity profiles for the patient, the method comprising:

processing at least a portion of said EMG signal to determine muscle -activity data over a time period;

partitioning said muscle -activity data to create a first partitioned data set, the first

partitioning said muscle -activity data to create a second partitioned data set, the second partitioned data set including a plurality of data parts of a second duration width; scaling said plurality of data parts in each of said first partitioned data set and said second partitioned data set in order to provide scaled muscle-activity values for said first partitioned data set and scaled muscle -activity values for said second partitioned data set;

the scaling of said plurality of data parts, for each of said first partitioned data set and said second partitioned data set, comprising determining an average value of muscle- activity data for each of a plurality of data parts to provide a plurality of average muscle -activity values and then dividing each of said plurality of average muscle- activity values by a maximum muscle -activity achieved by said patient; comparing one or more of said scaled muscle -activity values for said first partitioned data set with one or more of said scaled muscle-activity values for said second partitioned data set in order to determine a difference between scaled muscle -activity values for the first partitioned data set and the second partitioned data set; and

identifying a muscle-activity profile for said patient based on said difference between scaled muscle-activity values for the first partitioned data set and the second partitioned data set.

12. The method of claim 11 further comprising monitoring the patient over a second time period and determining a change in said muscle -activity profile for the patient; and

sending a message or report to a caregiver or to the patient indicating that muscle activity for the patient has changed.

13. The method of claim 11 further comprising instructing a caregiver or patient to execute one or more care actions based on said muscle -activity profile;

said instructing being mediated via one or more visual messages or audible messages provided using one or more of said monitoring unit or a base station; said one or more care actions including at least one of instructing a caregiver or patient to take one or more medications, instructing a patient to engage in a low-physical -stress activity, or both.

14. The method of claim 13 wherein said one or more medications comprises a benzodiazepine in a dose of about 0.5 mg to about 25 mg.

15. The method of claim 11 the comparing of one or more of said scaled muscle-activity values for said first partitioned data set with one or more of said scaled muscle -activity values for said second partitioned data set comprising determining a percentage difference between scaled muscle-activity values.

16. The method of claim 11 wherein the muscle-activity profile is a muscle-activity profile

indicating that the patient has one of epilepsy, cerebral palsy, Parkinson’s disease, or parasomnia.

17. The method of claim 11 wherein the muscle-activity profiles is a muscle-activity profile indicating that the patient has a form of epilepsy without an underlying or more specific motor condition that may significantly affect muscle activity during times between seizures.

18. The method of claims 11-17 further comprising determining one or more additional condition to define the muscle -activity profile, the one or more additional condition including at least one of a number of detected seizures, an overall muscle activity for the patient, demographic information for the patient or combinations thereof.

19. The method of claim 11 further comprising executing one or more seizure-detection routines configured for processing said EMG signal and identifying parts of said EMG signal wherein the patient is having a seizure.

20. The method of claim 19 further comprising selecting said portion of said EMG signal based on whether said portion of said EMG signal was collected during a time period without a recorded seizure, based on whether said portion of said EMG signal was collected during a pre-seizure time period, or based on whether said portion of said EMG signal was collected during a post-seizure recovery period.

21. The method of claim 20, said pre-seizure time period being collected within about 2 minutes to about 60 minutes preceding a start of a detected seizure.

22. The method of claim 20, said post-seizure recovery period collected within a period of about 10 minutes to about 60 minutes following completion of a detected seizure.

23. The method of claim 11, said first duration width being about 5 seconds to about 50 seconds and said second duration width being about 200 seconds to about 600 seconds.

24. The method of claim 11 wherein said difference value is a percentage difference of lesser than about 40%, lesser than about 30%, or lesser than about 20%.

25. The method of claim 24 further comprising generating a report or message and sending the report or the message to a caregiver device indicating that the patient muscle activity is indicative of a motor disorder indicative of cerebral palsy.

26. The method of claim 11 wherein said difference value is a percentage difference of greater than about 40%, greater than about 100%.

27. The method of claim 26 further comprising generating a report or message and sending the report or the message to a caregiver device indicating that the patient muscle activity is indicative of a motor disorder indicative of Parkinson’s disease.

28. A method of characterizing a patient’s muscle activity and identifying one or more muscle- activity profiles for the patient, the method comprising:

partitioning said muscle -activity data to create a plurality of data sets, the plurality of data sets each including a plurality of data parts of a duration width, the plurality of data sets characterized by different duration widths;

scaling each of said plurality of data sets to provide scaled muscle-activity data for each of the plurality of data sets and using the scaled muscle-activity data to determine at least one overall scaled muscle-activity value for each of said plurality of data sets; and

identifying a muscle-activity profile for said patient based on a dependence of said at least one overall scaled muscle-activity values versus duration width.

29. The method of claim 28, the scaling of each of said plurality of data sets comprising

determining an average value of muscle -activity data in each of a plurality of parts of a data set among the plurality of data sets and dividing each part of the data set by a maximum muscle -activity achieved by said patient.

30. The method of claim 28, the identifying of said muscle-activity profile including comparing one or more slopes determined from the dependence of said at least one overall scaled muscle -activity values versus duration width with one or more slope thresholds.

31. The method of claim 28 wherein the muscle-activity profile is a muscle-activity profile

32. The method of claim 28 wherein the muscle-activity profiles is a muscle-activity profile

indicating that the patient has a form of epilepsy without an underlying or more specific motor condition that may significantly affect muscle activity during times between seizures.

33. The method of claims 28-32 further comprising determining one or more additional condition to define the muscle -activity profile, the one or more additional condition including at least one of a number of detected seizures, an overall muscle activity for the patient, demographic information for the patient or combinations thereof.

34. A method of monitoring a patient susceptible to seizures for seizure activity and

characterizing muscle activity for the patient, the method comprising:

accessing electromyography (EMG) data from one or more units of computer memory; executing one or more seizure-detection routines configured for processing said EMG data and identifying portions of said EMG data wherein the patient has had a seizure; selecting one or more remaining portions of said EMG data, the remaining portions of said EMG data excluding said portions of said EMG data wherein a patient has had a seizure;

processing said one or more remaining portions of said EMG data to provide muscle- activity data over a time period;

35. The method of claim 34, said one or more seizure-detection routines include at least one of a routine configured to calculate a T-squared value, a routine configured to calculate a principal component value, a routine configured to calculate a number of zero-crossings exhibiting a hysteresis, and a routine configured to calculate a threshold seizure-detection ratio between signals in two different frequency bands.

36. The method of claim 34, said one or more remaining portions of said EMG data comprise data collected during a pre-seizure time period.

37. The method of claim 36, said one or more remaining portions of said EMG data comprising data collected within about 2 minutes to about 60 minutes preceding a start of a detected seizure.

38. The method of claim 34, said one or more muscle -activity thresholds comprising values that are about 3 standard deviations from historical limits of muscle -activity achieved by said patient.

39. The method of claim 34, said one or more muscle -activity thresholds comprising values based on collected muscle-activity data for patients of a particular patient demographic.

40. The method of claim 34, the maximum-muscle -activity-value being a local-maximum- muscle -activity value achieved by the patient in each of said plurality of parts of said first partitioned data set.

41. The method of claim 34, the maximum-muscle -activity value being a global -maximum- muscle -activity value achieved by the patient over said time period.

42. A method of characterizing a patient’s muscle activity and identifying one or more muscle- activity profiles for the patient, the method comprising:

accessing electromyography (EMG) data from one or more units of computer memory; processing at least a portion of said EMG data to determine muscle -activity data over a time period;

partitioned data set including a plurality of data parts of a first duration width; partitioning said muscle -activity data to create a second partitioned data set, the second partitioned data set including a plurality of data parts of a second duration width; scaling said plurality of data parts in each of said first partitioned data set and said second partitioned data set in order to provide scaled muscle-activity values for said first partitioned data set and scaled muscle -activity values for said second partitioned data set;

the scaling of said plurality of data parts, for each of said first partitioned data set and said second partitioned data set, comprising determining an average value of muscle- activity data for each of a plurality of data parts to provide a plurality of average muscle -activity values and then dividing each of said plurality of average muscle- activity values by a maximum muscle -activity achieved by said patient; comparing one or more of said scaled muscle -activity values for said first partitioned data set with one or more of said scaled muscle-activity values for said second partitioned data set in order to determine a difference between scaled muscle -activity values for the first partitioned data set and the second partitioned data set; and identifying a muscle-activity profile for said patient based on said difference between scaled muscle-activity values for the first partitioned data set and the second partitioned data set.

43. The method of claim 42 further comprising processing said EMG data to determine

muscle -activity data over a second time period and determining a change in said muscle- activity profile for the patient; and

44. The method of claim 42 further comprising instructing a caregiver or patient to execute one or more care actions based on said muscle -activity profile;

45. The method of claim 44 wherein said one or more medications comprises a benzodiazepine in a dose of about 0.5 mg to about 25 mg.

46. The method of claim 42 the comparing of one or more of said scaled muscle-activity values for said first partitioned data set with one or more of said scaled muscle -activity values for said second partitioned data set comprising determining a percentage difference between scaled muscle-activity values.

47. The method of claim 42 wherein the muscle-activity profile is a muscle-activity profile indicating that the patient has one of epilepsy, cerebral palsy, Parkinson’s disease, or parasomnia.

48. The method of claim 42 wherein the muscle-activity profiles is a muscle-activity profile indicating that the patient has a form of epilepsy without an underlying or more specific motor condition that may significantly affect muscle activity during times between seizures.

49. The method of claims 42-48 further comprising determining one or more additional condition to define the muscle -activity profile, the one or more additional condition including at least one of a number of detected seizures, an overall muscle activity for the patient, demographic information for the patient or combinations thereof.

50. The method of claim 42 further comprising executing one or more seizure-detection routines configured for processing said EMG data and identifying parts of said EMG data wherein the patient is having a seizure.

51. The method of claim 50 further comprising selecting said portion of said EMG data based on whether said portion of said EMG data was collected during a time period without a recorded seizure, based on whether said portion of said EMG data was collected during a pre-seizure time period, or based on whether said portion of said EMG data was collected during a post seizure recovery period.

52. The method of claim 51, said pre-seizure time period being collected within about 2 minutes to about 60 minutes preceding a start of a detected seizure.

53. The method of claim 51, said post-seizure recovery period collected within a period of about 10 minutes to about 60 minutes following completion of a detected seizure.

54. The method of claim 42, said first duration width being about 5 seconds to about 50 seconds and said second duration width being about 200 seconds to about 600 seconds.

55. The method of claim 42 wherein said difference value is a percentage difference of lesser than about 40%, lesser than about 30%, or lesser than about 20%.

56. The method of claim 55 further comprising generating a report or message and sending the report or the message to a caregiver device indicating that the patient muscle activity is indicative of a motor disorder indicative of cerebral palsy.

57. The method of claim 42 wherein said difference value is a percentage difference of greater than about 40%, greater than about 100%.

58. The method of claim 57 further comprising generating a report or message and sending the report or the message to a caregiver device indicating that the patient muscle activity is indicative of a motor disorder indicative of Parkinson’s disease.

59. A method of characterizing a patient’s muscle activity and identifying one or more muscle- activity profiles for the patient, the method comprising:

60. The method of claim 59, the scaling of each of said plurality of data sets comprising

61. The method of claim 59, the identifying of said muscle-activity profile including comparing one or more slopes determined from the dependence of said at least one overall scaled muscle -activity values versus duration width with one or more slope thresholds.

62. The method of claim 59 wherein the muscle-activity profile is a muscle-activity profile

63. The method of claim 59 wherein the muscle-activity profiles is a muscle-activity profile

64. The method of claims 59-63 further comprising determining one or more additional condition to define the muscle -activity profile, the one or more additional condition including at least one of a number of detected seizures, an overall muscle activity for the patient, demographic information for the patient or combinations thereof.

65. A system for monitoring a patient using electromyography and detecting one or more muscle- activity-excursion events, the system comprising:

an electromyography monitoring unit, the electromyography monitoring unit being

portable and designed for daily wear by the patient and configured for collection of an electromyography signal when the electromyography monitoring unit is disposed on the skin of a patient near one or more of a patient’s muscles; and a processor coupled to said electromyography monitoring unit, said processor configured to execute a routine for analyzing the electromyography signal for said one or more excursion events, the routine including:

processing at least a portion of the collected electromyography signal to determine a scaled muscle-activity value for the patient, the scaled muscle-activity value comprising a measure of muscle activity scaled based on a maximum muscle -activity values achieved by the patient;

comparing the scaled muscle-activity value to one or more muscle -activity thresholds; and

identifying a muscle-activity-excursion event if the scaled muscle -activity value meets the one or more muscle-activity thresholds.

66. The system of claim 65 further comprising a transmitter coupled to said processor the

transmitter configured to transmit a report or message to a caregiver in response to the identifying of said muscle -activity-excursion event.

67. The system of claim 65 further comprising a base station, said processor is part of said base station, the base station coupled to said electromyography monitoring unit via a wireless connection, the base station configured to send a report or message to a remote caregiver in response to the identifying of said muscle-activity-excursion event.

68. The system of claim 65 further comprising a base station, said processor is part of said base station, the base station coupled to said electromyography monitoring unit via a wireless connection, the base station including a display screen configured for displaying instructions to a local caregiver or responsive patient to execute one or more care actions in response to detection of said excursion event.

69. The system of claim 65 further comprising a base station, said base station coupled to said processor and configured to receive a message indicating detection of said excursion event, the base station including a display screen configured for displaying instructions to a local caregiver or responsive patient to execute one or more care actions in response to detection of said excursion event.

70. The system of claim 65 wherein said processor is a part of said electromyography monitoring unit.

71. The system of claim 65, said monitoring unit including a monitoring unit processor

configured for processing said electromyography signal and identifying portions of said electromyography signal wherein the patient is having or has had a seizure and to time-stamp the collected electromyography signal;

said routine further including:

selecting said portion of the collected electromyography signal based on said time-stamp, said portion of the collected electromyography signal being a portion collected without a recorded seizure.

72. The system of claim 65, said monitoring unit including a monitoring unit processor

said routine further including:

selecting said portion of the collected electromyography signal based on said time-stamp, said portion of the collected electromyography signal being a pre-seizure portion collected within about 2 minutes to about 60 minutes preceding a detected seizure.

73. The system of claim 65, said monitoring unit including a monitoring unit processor

said routine further including:

selecting said portion of the collected electromyography signal based on said time-stamp, said portion of the collected electromyography signal being a post-seizure portion collected within about 10 minutes to about 60 minutes following a detected seizure.

74. The system of claim 65, said processor further configured for processing said

electromyography signal and identifying portions of said electromyography signal wherein the patient is having or has had a seizure,

said routine further including:

selecting said portion of the collected electromyography signal based on one or more detected seizures, said portion of the collected electromyography signal being one of a pre-seizure portion, a portion of the collected electromyography signal collected without a recorded seizure, a portion of the collected electromyography signal collected following a detected seizure.

75. The system of claim 65 further comprising a second electromyography monitoring unit, said electromyography monitoring unit disposed and said second electromyography monitoring unit disposed on an agonist and antagonist pair of muscles for which the patient has identified an abnormal or painful level of flexion during movement.

76. The system of claim 65 further comprising a storage database, the storage database coupled to each of said monitoring unit and said processor, the storage database configured to receive and store said collected EMG signal.

77. A system for monitoring a patient using electromyography and identifying a muscle-activity profile for a patient, the system comprising:

an electromyography monitoring unit, the electromyography monitoring unit being

portable and designed for daily wear by the patient and configured for collection of an electromyography signal when the electromyography monitoring unit is disposed on the skin of a patient near one or more of a patient’s muscles; and a processor coupled to said electromyography monitoring unit, said processor configured to execute a routine for analyzing the electromyography signal and identifying said muscle -activity profile for the patient, the routine including:

processing at least a portion of the collected electromyography signal to determine one or more scaled muscle -activity values for the patient for each of two or more partitioned data sets, the one or more scaled muscle -activity values for each of the two or more data sets comprising a measure of muscle activity scaled based on a maximum- muscle -activity value achieved by the patient;

comparing said one or more scaled muscle -activity values for the patient for each of said two or more partitioned data sets to determine a difference between the one or more scaled muscle-activity values for said two or more partitioned data sets; and identifying said muscle-activity profile for said patient based on said difference between the one or more scaled muscle-activity values for said two or more partitioned data sets.

78. The system of claim 77 further comprising a transmitter coupled to said processor the

transmitter configured to transmit a report or message to a caregiver in response to the identifying of said muscle -activity profile.

79. The system of claim 77 further comprising a base station, said processor is part of said base station, the base station coupled to said electromyography monitoring unit via a wireless connection, the base station configured to send a report or message to a remote caregiver in response to the identifying of said muscle-activity profile.

80. The system of claim 77 further comprising a base station, said processor is part of said base station, the base station coupled to said electromyography monitoring unit via a wireless connection, the base station including a display screen configured for displaying instructions to a local caregiver or responsive patient to execute one or more care actions in response to the identifying of said muscle -activity profile.

81. The system of claim 77 further comprising a base station, said base station coupled to said processor and configured to receive a message indicating detection of said excursion event, the base station including a display screen configured for displaying instructions to a local caregiver or responsive patient to execute one or more care actions in response to the identifying of said muscle -activity profile.

82. The system of claim 77 wherein said processor is a part of said electromyography monitoring unit.

83. The system of claim 77, said monitoring unit including a monitoring unit processor

said routine further including:

84. The system of claim 77, said monitoring unit including a monitoring unit processor

said routine further including:

85. The system of claim 77, said monitoring unit including a monitoring unit processor

said routine further including:

86. The system of claim 77, said processor further configured for processing said

said routine further including:

selecting said portion of the collected electromyography signal based on one or more detected seizures, said portion of the collected electromyography signal being one of a pre-seizure portion, a portion of the collected electromyography signal collected without a recorded seizure, or and/or a portion of the collected electromyography signal collected following a detected seizure.

87. The system of claim 77 further comprising a second electromyography monitoring unit, said electromyography monitoring unit disposed and said second electromyography monitoring unit disposed on an agonist and antagonist pair of muscles for which the patient has identified an abnormal or painful level of flexion during movement.

88. The system of claim 77 further comprising a storage database, the storage database coupled to each of said monitoring unit and said processor, the storage database configured to receive and store said collected EMG signal.

89. A method of monitoring a patient for changes in muscle activity, the method comprising:

processing at least a portion of a collected electromyography signal to determine a scaled muscle -activity value for the patient, the scaled muscle-activity value comprising a measure of muscle activity scaled based on a maximum muscle-activity values achieved by the patient;

90. The method of claim 89 further comprising automatically sending a report or message to a remote caregiver, the report or message indicating said muscle -activity-excursion event.

91. The method of claim 89 further comprising displaying instructions to a local caregiver or responsive patient to execute one or more care actions in response to detection of said muscle- activity-excursion event.

92. A method of monitoring a patient for changes in muscle activity and matching muscle activity for the patient to a muscle-activity profde, the method comprising:

processing at least a portion of collected electromyography (EMG) data to determine one or more scaled muscle-activity values for the patient for each of two or more muscle- activity data sets, the one or more scaled muscle -activity values for each of the two or more muscle -activity data sets comprising a measure of muscle activity scaled based on a maximum-muscle-activity value achieved by the patient;

comparing said one or more scaled muscle -activity values for the patient for each of said two or more partitioned data sets to determine a difference between the one or more scaled muscle-activity values for said two or more partitioned data sets; and identifying a muscle-activity profile for said patient based on said difference between the one or more scaled muscle -activity values for said two or more partitioned data sets.

93. The method of claim 92 further comprising automatically sending a report or message to a remote caregiver, the report or message indicating the identifying of said muscle -activity profile.

94. The method of claim 89 further comprising displaying instructions to a local caregiver or responsive patient to execute one or more care actions in response to the identifying of said muscle -activity profile.

95. The method of claim 92 further comprising processing said EMG data to determine muscle- activity data over a second time period and determining a change in said muscle- activity profile for the patient; and

96. The method of claim 92 further comprising instructing a caregiver or patient to execute one or more care actions based on said muscle -activity profile;

97. The method of claim 96 wherein said one or more medications comprises a benzodiazepine sedative in a dose of about 0.5 mg to about 25 mg.

98. The method of claim 92 the comparing of said one or more scaled muscle -activity values for the patient for each of said two or more partitioned data sets comprising determining a percentage difference between scaled muscle -activity values.

99. The method of claim 92 wherein said difference is a percentage difference.

100. The method of claim 92 wherein the muscle-activity profile is a muscle-activity profile

101. The method of claim 92 wherein the muscle-activity profiles is a muscle-activity profile indicating that the patient has a form of epilepsy without an underlying or more specific motor condition that may significantly affect muscle activity during times between seizures.

102. The method of claims 93-101 further comprising determining one or more additional

condition to define the muscle -activity profile, the one or more additional condition including at least one of a number of detected seizures, an overall muscle activity for the patient, demographic information for the patient or combinations thereof.

103. The method of claim 92 further comprising executing one or more seizure-detection routines configured for processing said EMG data and identifying parts of said EMG data wherein the patient is having a seizure.

104. The method of claim 103 further comprising selecting said portion of said EMG data based on whether said portion of said EMG data was collected during a time period without a recorded seizure, based on whether said portion of said EMG data was collected during a pre-seizure time period, based on whether said portion of said EMG data was collected during a post seizure recovery period, or based on whether said portion of said EMG data was collected while a patient was at rest or sleeping.

105. The method of claim 104, said pre-seizure time period being collected within about 2 minutes to about 60 minutes preceding a start of a detected seizure.

106. The method of claim 104, said post-seizure recovery period collected within a period of about 10 minutes to about 60 minutes following completion of a detected seizure.

107. The method of claim 92 wherein said difference value is a percentage difference of lesser than about 40%, lesser than about 30%, or lesser than about 20%.

108. The method of claim 107 further comprising generating a report or message and sending the report or the message to a caregiver device indicating that the patient muscle activity is indicative of a motor disorder indicative of cerebral palsy.

109. The method of claim 92 wherein said difference value is a percentage difference of greater than about 40%, greater than about 100%.

110. The method of claim 109 further comprising generating a report or message and sending the report or the message to a caregiver device indicating that the patient muscle activity is indicative of a motor disorder indicative of Parkinson’s disease.

111. A computer program comprising instructions which, when the program is executed by the computer, cause the computer to carry out a method of monitoring a patient for changes in muscle activity and matching muscle activity for the patient to a muscle -activity profde, the method comprising:

112. The computer program of claim 111 further comprising instructions for automatically sending a report or message to a remote caregiver, the report or message indicating the identifying of said muscle-activity profile.

113. The computer program of claim 111 further comprising instructions for displaying

instructions to a local caregiver or responsive patient to execute one or more care actions in response to the identifying of said muscle-activity profile.

114. The computer program of claim 111 further comprising instructions for processing said EMG data to determine muscle -activity data over a second time period and determining a change in said muscle- activity profile for the patient; and

115. The computer program of claim 111 further comprising instructions for providing one or more audible or visual prompts directing a caregiver or patient to execute one or more care actions based on said muscle -activity profile;

said one or more care actions including at least one of instructing a caregiver or patient to take one or more medications, instructing a patient to engage in a low-physical -stress activity, or both.

116. The computer program of claim 111 further comprising instructions for comparing of said one or more scaled muscle-activity values for the patient for each of said two or more partitioned data sets comprising determining a percentage difference between scaled muscle-activity values.

117. The computer program of claim 111 wherein said difference is a percentage difference.

118. The computer program of claim 111 wherein the muscle -activity profde is a muscle-activity profde indicating that the patient has one of epilepsy, cerebral palsy, or Parkinson’s disease.

119. The computer program of claim 111 wherein the muscle -activity profdes is a muscle-activity profde indicating that the patient has a form of epilepsy without an underlying or more specific motor condition that may significantly affect muscle activity during times between seizures.