WO2017013655A1

WO2017013655A1 - System and method for measuring icp

Info

Publication number: WO2017013655A1
Application number: PCT/IL2016/050794
Authority: WO
Inventors: Guy Weinberg; Surik Papyan; Alexandra LEVINSKY
Original assignee: Headsense Medical Ltd.
Priority date: 2015-07-22
Filing date: 2016-07-21
Publication date: 2017-01-26
Also published as: EP3324841A1; CN107949322A; CA2992961A1

Abstract

A system for determining an intracranial pressure value is provided. The system includes a computing platform configured for determining an intracranial pressure value from an energy, energy spectral density or amplitude of a captured intracranial acoustic signal.

Description

SYSTEM AND METHOD FOR MEASURING ICP

FIELD AND BACKGROUND OF THE INVENTION

The present invention relates to a system for measuring ICP and a method of using same and, more particularly, to a system capable of deriving absolute ICP values from natural acoustic signals captured from a head of a subject. The present invention also relates to a model for correlating ICP values to parameters derived from naturally occurring intracranial acoustic signals.

The brain is encased by a hard bony structure - the skull - and suspended in a fluid-cerebro spinal fluid (CSF, also referred to as liquor). CSF flows around and in the brain tissues circulating under the cranial Dura mater. In addition to serving as a dampening medium which protects the brain from trauma, CSF also functions in supporting homeostatic equilibrium of neural tissue, feeding these tissues with nutritional substances and removing metabolic end products.

CSF continuously circulates, it is excreted by certain brain regions and then flows through specialized ducts to other brain regions, where it is absorbed into the blood stream. On average, full regeneration of CSF takes place 7 times a day.

Intracranial pressure (ICP) is the pressure inside the skull, brain tissue and cerebrospinal fluid (CSF). The body regulates ICP, with CSF pressures varying by about 1 mmHg in normal adults through shifts in production and absorption of CSF. Normal ICP of a supine adult at rest is 7-15 mmHg; ICP can increase transiently due to increases in intrathoracic pressure caused by, for example, coughing.

ICP is maintained by dynamic balance between cerebral blood flow, cerebrospinal fluid volumes and the brain tissue. Normal intracranial pressure is an essential prerequisite for adequate cerebral blood supply and neuronal tissue metabolism and function.

Under normal physiological conditions, a change in one of the factors influencing maintenance of intracranial pressure triggers compensatory mechanisms for normalization. For example, increases in average systemic arterial pressure (AP) causes rapid dilation of cerebral vessels and vice versa resulting in no significant changes to cerebral blood flow and intracranial pressure. Intracranial hypertension, commonly abbreviated IH, IICP or raised ICP, is intracranial pressure higher than 20-25 mm Hg, the upper limit of normal ICP. Intracranial hypertension can be idiopathic or caused by head trauma, aneurysm, tumor or one of a variety of other causes.

ICP monitoring is typically performed in cases of severe head injury or brain/nervous system disease using invasive approaches. Due to the risks associated with invasive monitoring, the high costs of the procedure, and the limited access to trained personnel, ICP monitoring is rarely a part of clinical management of patients with non- critical neurological conditions.

Due to the limitations of invasive monitoring, approaches for non-invasively monitoring of ICP have been sought.

Such approaches typically utilize an interrogation signal for generating a quantifiable return signal that can be correlated to ICP values.

While reducing the present invention to practice, the present inventors identified and isolated naturally occurring intracranial acoustic signals that can be processed to generate signal-related data which accurately reflects ICP values.

SUMMARY OF THE INVENTION

According to one aspect of the present invention there is provided a system for determining an intracranial pressure value comprising a computing platform configured for: (a) capturing an intracranial acoustic signal resultant from fluid flow in a head of a subject; (b) isolating acoustic signals at one or more frequency ranges; (c) determining an energy, energy spectral density or amplitude of at least one of the acoustic signals or a portion thereof; and (d) deriving an intracranial pressure value from the energy, or energy spectral density or the amplitude.

According to further features in preferred embodiments of the invention described below, the system further comprises transforming the acoustic signals from a time domain to a frequency domain.

According to still further features in the described preferred embodiments (d) is effected using the energy level, the energy spectral density level or the amplitude values following non-linear transformation. According to still further features in the described preferred embodiments the amplitude of (c) is determined from a representation of a time domain of the at least one of the acoustic signals or the portion thereof.

According to still further features in the described preferred embodiments the amplitude of (c) is determined from a representation of in a frequency domain of the at least one of the acoustic signals or the portion thereof.

According to still further features in the described preferred embodiments the energy level is the total energy of at least one of the acoustic signals or the portion thereof.

According to still further features in the described preferred embodiments the one or more frequency ranges are selected from the group consisting of 0.5-15 Hz, 0.5- 25 Hz, 0.5-45 Hz, 0.5-75 Hz, 15-25 Hz, 25-45 Hz, 45-75 Hz and 75-90 Hz.

According to still further features in the described preferred embodiments the computing platform stores a model constructed from weighted sum of non-linearly transformed energy level, energy spectral density level and amplitude of the acoustic signals.

According to still further features in the described preferred embodiments the invasive ICP values from the Gold Standard ICP measuring device are utilized in the model to assign weights to the non-linearly transformed energy level, energy spectral density level and amplitude of the acoustic signals.

According to still further features in the described preferred embodiments (d) is effected by applying the model to the non-linearly transformed energy level, energy spectral density level and amplitude of the acoustic signals or the portion thereof.

According to still further features in the described preferred embodiments (a) is effected by an in-ear device including at least one microphone.

According to still further features in the described preferred embodiments the at least one microphone is a MEMS microphone.

According to still further features in the described preferred embodiments the MEMS microphone has a linear response of up to 130 dB sound pressure level (SPL).

According to still further features in the described preferred embodiments at least one microphone is characterized by low frequency extension to 6 Hz. According to still further features in the described preferred embodiments the in- ear device is capable of filtering out non-intracranial acoustic signals.

According to still further features in the described preferred embodiments the system further comprises applying a non-linear function to the energy level or energy spectral density level or amplitude prior to (d).

According to still further features in the described preferred embodiments the energy spectral density level is the total energy of at least one of the acoustic signals or the portion thereof transformed to a frequency domain.

According to another aspect of the present invention there is provided a method of modeling a relationship between naturally occurring intracranial acoustic signals and ICP values, the method comprising: (a) capturing an intracranial acoustic signal resultant from fluid flow in a head of each of a plurality of subjects; (b) isolating acoustic signals at one or more frequency ranges from each intracranial acoustic signal captured; (c) transforming the acoustic signals from a time domain to a frequency domain to thereby obtain transformed acoustic signals; (d) determining an energy level or energy spectral density level or amplitude of the acoustic signals; (e) obtaining ICP values from the plurality of subjects; and (f) correlating the non-linearly transformed energy level or energy spectral density level or amplitude of the acoustic signals to the ICP values to thereby derive a weighted function correlating the non-linearly transformed energy level or energy spectral density level or amplitude of an intracranial acoustic signal to an ICP value.

According to yet another aspect of the present invention there is provided a method of determining an intracranial pressure value comprising: (a) capturing an intracranial acoustic signal resultant from fluid flow in a head of a subject; (b) isolating acoustic signals at one or more frequency ranges; (c) determining an energy, energy spectral density or amplitude of at least one of the acoustic signals or a portion thereof; and (d) deriving an intracranial pressure value from the energy, or energy spectral density or the amplitude.

The present invention successfully addresses the shortcomings of the presently known configurations by providing a system for assessing and monitoring ICP levels in a subject. The present system does not require use of an interrogation signal and is thus simpler and easier to employ. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, suitable methods and materials are described below. In case of conflict, the patent specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

Implementation of the method and system of the present invention involves performing or completing selected tasks or steps manually, automatically, or a combination thereof. Moreover, according to actual instrumentation and equipment of preferred embodiments of the method and system of the present invention, several selected steps could be implemented by hardware or by software on any operating system of any firmware or a combination thereof. For example, as hardware, selected steps of the invention could be implemented as a chip or a circuit. As software, selected steps of the invention could be implemented as a plurality of software instructions being executed by a computer using any suitable operating system. In any case, selected steps of the method and system of the invention could be described as being performed by a data processor, such as a computing platform for executing a plurality of instructions. BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The invention is herein described, by way of example only, with reference to the accompanying drawings. With specific reference now to the drawings in detail, it is stressed that the particulars shown are by way of example and for purposes of illustrative discussion of the preferred embodiments of the present invention only, and are presented in the cause of providing what is believed to be the most useful and readily understood description of the principles and conceptual aspects of the invention. In this regard, no attempt is made to show structural details of the invention in more detail than is necessary for a fundamental understanding of the invention, the description taken with the drawings making apparent to those skilled in the art how the several forms of the invention may be embodied in practice. In the drawings:

FIG. 1 illustrates components of the present system.

FIG. 2 illustrates the microphone component of the present system.

FIG. 3 is a flowchart illustrating the steps carried out by the present system to obtain ICP values from a subject.

FIG. 4 is a naturally occurring acoustic signal recorded by the present system. FIG. 5 is the signal of Figure 4 following filtering.

FIG. 6 is the spectrum of the signal of Figure 5 following fast Fourier transform

(FFT).

FIGs. 7-11 illustrate the spectrograms, signals and energy curves for frequency bands extracted from the acoustic signal of Figure 4.

FIGs. 12-13 are graphs showing the minimum and maximum value of each complex derived from the acoustic signals (Figure 12) and the corresponding energy curves (Figure 13).

FIG. 14 illustrates the process of identifying the average waveform for each 6 second epoch.

FIGs. 15-16 illustrate changes in ICP as recorded by the Codman device and the system of the present invention.

FIGs. 17 and 18a-f illustrate correlation between bursts in the acoustic signal and sharp changes in ICP.

FIGs. 19a-b illustrate changes that similarly affect all frequency bands.

FIGs. 20a-e illustrate spectrograms with an event detected in a particular frequency band.

FIG. 21 illustrates a spectrogram of a healthy subject.

FIGs. 22-28 illustrate spectrograms of subjects having various pathologies that affect brain fluid flow dynamics.

FIG. 29 illustrates a spectrogram with a normal periodic pattern indicating that shunt functions properly.

FIG. 30 illustrates a spectrogram having a chaotic non-periodic pattern indicating shunt malfunction.

FIGs. 30-31 illustrate spectrograms indicative of iidiopathic intracranial hypertension (IIH). FIG. 32 illustrates normal shunt function and absence of IIH.

FIGs. 33-34 illustrate patterns of minor and moderate vasospasms detectable on a spectrogram of an acoustic signal. DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention is of an ICP monitoring system which can be used to obtain absolute ICP values from naturally-occurring intracranial acoustic signals. The present invention is also of a model which can be used to correlate invasive ICP values with parameters extracted from naturally-occurring intracranial acoustic signals.

The principles and operation of the present invention may be better understood with reference to the drawings and accompanying descriptions.

Before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not limited in its application to the details set forth in the following description or exemplified by the Examples. The invention is capable of other embodiments or of being practiced or carried out in various ways. Also, it is to be understood that the phraseology and terminology employed herein is for the purpose of description and should not be regarded as limiting.

Approaches for non-invasive monitoring of intracranial pressures are well known in the art (see, for example, US20080200832 and US5919144). Such approaches utilize an extracranial interrogation signal to generate a return signal which can be processed to derive ICP values.

In a previously filed patent application (WO2012/046223), the present inventors describe a non-invasive ICP monitoring system which utilizes unique acoustic interrogation signal and signal processing algorithms to derive accurate ICP values.

While continuing to experiment with various approaches for monitoring ICP, the present inventors uncovered that naturally-occurring acoustic signals produced within the head of a subject as a result of fluid flow can be captured and processed to derive ICP values. Such an approach forgoes the use of an interrogation signal and thus simplifies ICP monitoring while enabling use of a system that is more mobile and has lower power requirements. In addition, the portability of the present system enables monitoring of ambulatory patients or mobile individuals on a continuous basis. Thus, according to one aspect of the present invention there is provided a system for determining an intracranial pressure value in a subject such as a human. The system of the present invention can be used in a conscious or unconscious subject to quantify changes in ICP, determine absolute ICP values and monitor ICP values over extended time periods in a hospital, clinic or field setting.

The system employs an acoustic capture device (e.g. microphone) for capturing intracranial acoustic signal resultant from fluid flow in a head of a subject and a computing platform for processing such a signal or signals to isolate acoustic signals at one or more frequency ranges and to derive an intracranial pressure value from the energy, energy spectral density or amplitude of the processed acoustic signal.

The signal is preferably captured by a microphone positioned within the ear canal of the subject. During or following capture, the signal is filtered to remove external noise and the filtered signal is processed to identify and isolate specific frequency bands. These frequency bands are then transformed to a time or time-frequency domains. The transformed frequency band signals are then processed to calculate parameters relating to signal amplitude, energy content and dynamics of the vegetative system (autonomous nervous system) which controls the brain through complex loops of regulation including neurogenic, humoral, myogenic and metabolic mechanisms. Calculating the parameters related to energy content in the ultralow and low frequency ranges (the dynamics of vegetative system) allows to assess the mechanism of brain control.

Following application of a non-linear transformation, such parameters are used to construct a model for ICP calculation. The model includes a weighted linear summation of the calculated parameters, while weights are attributed to the parameter values based on correlation with invasive ICP measurements (using, for example, Codman).

Once the model is constructed it can be applied to any parameters calculated from transformed frequency band signals derived from captured (naturally occurring) intracranial acoustic signals.

The system of the present invention includes the following components:

(i) An acoustic capture device(s) (sensor) containing a pair of microphones placed inside a single housing for optimal recording of signals from the ear (or from both ears, or from one ear and the carotid artery or a cranial bone [in which case four microphones (two for recording and two for canceling) are used] and for neutralization of ambient noise.

(ii) Electronic circuitry for identifying and isolating the desired signal and transmitting it to a processor over a wired or wireless connection.

(iii) A processing unit (also referred to herein as processor) for receiving the captured acoustic signal and evaluating the level of ICP using a dedicated algorithm (iv). The processor can also extract physiological parameters necessary for assessing the patient's overall condition.

(iv) An algorithm that utilizes multiple signal processing techniques to identify the signal from the surrounding noise and other artifacts, and to calculate ICP based on the received cleaned signal as described in more detail hereinafter.

(v) A user interface for receiving processor output and displaying it to an operator using numerical and/or graphical data, either discretely or on a continuous basis, depending on the operating mode (discrete measurement or monitoring). The user interface may be implemented as a personal computer, tablet computer, laptop, smartphone, or a bedside monitor or display console. The user interface also stores the patient's demographic data, and it can also display the previous ICP values in the form of a graph or a histogram.

Signal capture, processing and output of ICP as carried out by the present system is shown in the flowchart of Figure 3 and is described in greater detail hereinbelow with reference to Figures 4-14.

Following is a detailed description of the components of the present system (referred to herein as system 10) and their function starting with an exemplary acoustic capture device.

Referring now to the drawings, Figure 1 illustrates system 10 which includes a microphone 12 (two shown) attached to a processing unit 16 having an interface 18. Microphone 12 can be a low-noise, high SPL MEMS microphone with extended low frequency response of 6 Hz to 20 kHz, a linear response up to 130 dB SPL, 130 dB SPL Acoustic Overload Point and a sensitivity of -45 dBV. Microphone 12 can offer low frequency extension down to 6 Hz, resulting in excellent phase characteristics in the audio range and a low current consumption for long battery life in portable applications. Figure 2 illustrates microphone 12 in greater detail. Microphone 12 includes a phone base 20, a microphone silicone tube 22, an electret condenser microphone 24 a phone left cover 26, a phone holder 28 a cable gland 30, a metal cap 32, and a cap isolator 34.

Microphone 12 can be 4.72 x 3.76 x 3.5 mm in size with a shape suitable for in- ear positioning (preferably matching the auricle and external auditory canal).

When positioned in the ear and in use, microphone 12 can capture intracranial acoustic signals in a frequency range of 0-110 Hz. Such signals can be filtered in- microphone to remove ambient sounds and communicated [via wired or wireless (e.g., Bluetooth Low Energy) to a processing unit 16 (Figure 1)].

The captured acoustic signal results from movement of fluid (e.g. blood, CSF) in vessels and conduits in the cranium. The movement of blood in the brain through blood vessels and the constant circulation of cerebrospinal fluid in the ventricles of the brain, as well as the subarachnoid space of the brain and spinal cord creates vibrations (acoustic sound) that vary over time depending on the state of the brain and ICP. The total vibration of vibrating components inside the cranium that passes through different parts of the head with different levels of resistance, including brain tissue, cerebrospinal fluid, blood, blood vessels, produces a recordable signal.

Movement of fluid through the pipe in general can be laminar or turbulent depending on the liquid density p, its viscosity η, the flow velocity v, and the pipe diameter d. Applying it to the blood flow through brain vessels the character of blood movement is defined by the velocity of the blood flow (v) and the diameter of the vessel (d), while parameters such as blood density and viscosity remain unchangeable. Conditions of transition from laminar to turbulent flow of a fluid are determined by the Reynolds number (Re):

Re =■ ρνά/η

When the Reynolds number does not exceed certain critical value (Rekr), the fluid flow is laminar. If Re> Rekr, flow becomes turbulent with appearance of vortexes. It can be concluded that when the velocity of the blood or the vessel's diameter increase the blood flow can change its character from a laminar to a turbulent. The character of blood flow (laminar or turbulent) can be detected by analyzing the sound generated by this process.

A number of factors influence the character of blood flow through vessels and thus influence the patterns of sound/vibration produced intracranially.

(i) Elasticity, which is mainly maintained by the change in the tonus of vascular smooth muscles, provides the propulsion of blood through the vessels. It acts like a shock absorber to smooth fluctuations in blood flow and pressure. Due to elasticity, a healthy subject exhibits a clear pattern of vibration/sound that is generated due to blood propulsion through the vessels while the frequency range of these vibrations lies in between 0-25 Hz (characterizes a laminar flow).

(ii) Aging or pathological processes may lead to a loss of elasticity, causing the arteries to stiffen. As a result, the vessels expand and contract less with each heartbeat. The sound generated due to blood propulsion through such stiff vessels differs from that of healthy arteries and its frequency shifts to a higher frequency range and lies between 15-45 Hz.

(iii) Vasoconstriction is a narrowing of the blood vessels' lumen, especially arteries, which occurs as a result of constriction of the muscular walls of the arteries. From Bernoulli's equation it can be inferred that vasoconstriction results in an increase in velocity and a drop in pressure. As a result the blood will be sucked through the opening of the constriction which will lead to significant turbulence in the blood flow, occurrence of vortexes before and after the constriction and to vibration of vessel walls, thus generating a high frequency pattern (25-50Hz).

(iv) Stenosis is an abnormal narrowing of a blood vessel due to accumulation of atherosclerotic plaques. This local constriction leads to the occurrence of turbulence in the flow of blood hence causing abnormal acoustic patterns in the frequency range of 25-50Hz.

(v) An extravascular obstruction is caused by an external compression on the vessel by a tumor, hematoma, inflamed tissue and the like. Under such conditions, the vessel lumen changes causing a change in vessel capacity and velocity of blood flow thereby leading to turbulent flow which generates an acoustic pattern in the frequency range of 25-75Hz. (vi) Aneurysms are a result of a weakened blood vessel wall causing swelling of the vessels. Blood flowing from the narrow portion of the vessels into the vasodilated area will change from laminar flow to a more turbulent flow developing swirling streams (vortexes) that have a characteristic acoustic signal. Since the turbulent flow is more chaotic than a laminar flow, the acoustic pattern caused by the turbulent flow due to aneurysm are more energetic and lies in the higher frequency range (25-75Hz) than the laminar flow (0-15Hz).

(vii) In order to maintain the continuity of the blood flow and proper cerebral perfusion pressure (CPP), the autonomous nervous system has three basic loops of regulation, each using neurogenic, humoral, myogenic and metabolic mechanisms which in general regulate the vessels diameter and its throughput. During the process of such complex regulation, there are particular vascular regions where the velocity of blood flow changes. This results in the disruption of laminar flow and turbulence. Turbulent blood flow through the vessels generates an acoustic pattern in the frequency range of 25-50Hz.

A signal including the above described acoustic patterns is recorded by microphone 12 positioned in the ear of the subject. The captured signal is converted into a digital signal by processing unit 16.

Processing unit 16 employs an algorithm for extracting ICP values from the recorded signal. Processing unit can also include a wireless communication module (Bluetooth, WiFi, Satellite) for wireless communication with interface 18.

The recorded acoustic signal (Figure 4) is preferably processed in real time. The algorithm processes several portions of the signal concurrent with recording of the signal. For example, the algorithm can process 6 second portions of the signal concomitant with recording. The processed signal and calculated ICP values are displayed to the user via user interface 18 (Figure 1) as discrete or continuous readings. The minimal length of a recording can be about several seconds (e.g. 6 seconds) with an initial sampling rate of 11025Hz. The recorded signal (SigOO) is down-sampled from 11025Hz to 525Hz or to 1225Hz, depending on the physiological processes of interest and the Nyquist criterion. Lowering of the sampling rate results in less load and can speed the system without loss of precision. Processing of the captured (recorded) signal (SigOO) initiates with removal of artifacts and ectopic contractions using a filter based on Wavelet Packet Decomposition. The signal is decomposed with 'coif2' mother wavelet and filter different nodes via thresholding. The resulting signal (SigO) is shown in Figure 5.

A Fast Fourier Transform (FFT) is then applied to SigO to obtain its discreet

Fourier transform Y00: The spectrum of SigO, Y00 is shown in Figure 6.

SigO is then filtered to remove electrical interference (~50Hz or 60Hz) and is restricted to a frequency range of 0.5Hz - 130Hz.

Several frequency bands are then extracted from SigO:

First band: 0.5-15 Hz - Sigll

Second band: 0.5-25 Hz - Sigl2

Third band: 0.5-45 Hz - Sigl3

Fourth band: 0.5-75 Hz - Sigl4

Fifth band: 75-90 Hz - Sig2

Bands: 0.5-15Hz, 15-25Hz, 25-45Hz, 45-75Hz are also analyzed.

Each frequency band is then represented by its own spectrogram and a corresponding acoustic signal.

Each spectrogram is smoothed and processed to remove additional artifacts, and an energy curve is then calculated for each frequency band spectrogram via summation of spectrogram frequencies per each unit of time.

The resulting curve represents the variation of the total energy over time for each frequency band. Figures 7-11 illustrate the spectrograms, energy curves and acoustic signals for frequency bands Sigl l, Sigl2, Sigl3, Sigl4 and Sig 2 (respectively).

The algorithm then calculates the heart rate (HR) and respiratory rate (RR) and both HR and RR are extracted from analysis of Energy curve of the second band and of the fifth band (respectively).

The algorithm then detects peaks (minimum and maximum value of each complex) in the acoustic signals (Figure 12) and the corresponding energy curves (Figure 13).

Parameters corresponding to amplitudes of acoustic and energy curves in different ranges of frequencies are then calculated. These parameters (following non- linear transformation) (denoted herein as Ampl l, Ampl2, Ampl3, Ampl4, Amp20, S 11, S 12, S 13, S 14, S20) are the main components in calculating ICP.

Additional parameters that are calculated reflect the spectral density content of energy curves themselves in very low and low frequencies (0.1- lOHz) (the parameters are denoted as in_Enl l, in_Enl2, in_Enl3, in_Enl4, in_En2 and Alk) and parameters describing the energy content of in_Enl l, in_Enl2, in_Enl3, in_Enl4, in_En2 in the two following frequency ranges (0.15-0.4Hz, 0.8-2.5Hz) relative to the total energy content in (0.1- lOHz), (the parameters are denoted as pari, par2, par3, par4, par5).

The energy curves are then subjected to morphological analysis to identify all the complexes with peaks lying in the particular range defined by the majority. The average waveform can then be identified for each 6- second window of the signal (Figure 14).

The ICP can then be calculated using a linear model constructed from the non- linearly transformed parameters described above.

The linear model is constructed by correlating known invasive ICP values (see Examples section hereinbelow) with the non-linear parameters obtained by the present algorithm using a weight function.

The model is based on linear combination of non-linearly transformed parameters.

The Ampl l, Ampl2, Ampl3, Ampl4, Amp20 are transformed by the while the rest of the parameters are transformed by the

The model for ICP calculation is the following:

ICPc = km(l) + km(2).*AlK + km(3).*Ampl l + km(4).*Ampl2 + km(5).*Ampl3 + km(6).*Ampl4 + km(7).*Amp20 + km(8).*S l l + km(9).*S 12 + km(10).*S 13 + km(l l).*S 14 + km(12).*S20 + km(13).*in_Enl l + km(14).*in_Enl2 + km(15).*in_Enl3 + km(16).*in_Enl4 + km(17).*in_En2 + km(18).*parl + km(19).*par2 + km(20).*par3 + km(21).*par4 + km(22).*par5;

km is found in the optimization procedure in which the optimization parameter (the price function) is the error between the measurements of the present system and the Gold Standard. The ICP value obtained by the present system can then be outputted to a user via interface 18 (Figure 1) which is wired or wirelessly connected to processing unit 16. ICP levels (expressed in mmHg) are displayed in interface 18 as data, figure or graph; the power curves and filtered acoustic signals can also be displayed. Interface 18 also includes controls for allowing the user to set recording and analysis parameters, display parameters and the length of monitoring.

Since the present system continuously processes acoustic signal portions to extract ICP values, it enables continuous monitoring which is advantageous in a number of applications. Long-term monitoring of ICP is advantageous in many pathological conditions involving the development of intracranial hypertension where the data obtained from other noninvasive diagnostic methods do not provide sufficiently accurate information. Examples include, infectious diseases (tropical malaria), fulminant forms of hepatitis or hepatic encephalopathy, barbiturate-induced coma with intractable status epilepticus, and others.

In addition, the compact size and low power requirements of the present system also make it suitable for use outside the hospital setting.

Thus, the present system can be designed as a portable or stationary continuous ICP monitor which can be used in a variety of applications and under a variety of conditions.

The portability and simplicity of the present system enables use in developed countries as well as less developed countries, where the use of invasive techniques is considered to be cost prohibitive. The present system can be used in various settings such as emergency medicine, including in ambulances and medical helicopters; in emergency first aid departments; and for the long-term care of the sick or elderly people in nursing homes. The present system can be used in settings where special medical equipment is not available, e.g., home care, outdoor areas during natural disasters and the like.

The present system is especially suitable for use in military settings in the field or on military transports and equipment. It can also be used in sports medicine in the field.

The present system can be used to for continuous monitoring of ICP over the course of the day (similar to the Holter monitor). The obtained data can be relayed to a remote physician using wireless communication. Such monitoring allows physicians or other emergency first responders to quickly obtain information about changes in ICP. After the patient arrives at the hospital or emergency room, the ICP history can be accessed by transmitting or uploading data into the hospital's information systems and bedside patient monitors.

Early monitoring of ICP enables physicians to timely diagnose and treat the causes of abnormal ICP values. Often a patient comes to a medical facility without obvious symptoms and is sent home. Later symptoms of increased ICP develop, and the patient requires emergency care.

The present system is also particularly useful for diagnosing asymptomatic patients with head injuries who do not have access to neurological specialists. The present invention allows a physician who is provided with a history of changes in ICP to accurately determine the patient's needs and follow-up care. The present system can also be used following discharge and during a rehabilitation period. This continued period of observation makes it possible to avoid relapses, any further deterioration of the patient's condition and a consequent return to the hospital.

The present system can also be used as a standalone system for continuously monitoring a patient prior to, during and following surgery. This provides doctors with the advantage of being able to monitor ICP during cranial, laparoscopic or other procedures, as well as monitoring the patient following surgery.

As used herein the term "about" refers to ± 10 %.

Additional objects, advantages, and novel features of the present invention will become apparent to one ordinarily skilled in the art upon examination of the following examples, which are not intended to be limiting. EXAMPLES

Reference is now made to the following examples, which together with the above descriptions, illustrate the invention in a non limiting fashion. EXAMPLE 1

Acoustic patterns reflective of changes in ICP

A prototype of the present system was utilized to record an acoustic signal from the ear canal of a subject. Concomitantly, a Codman mounted on the subject's head was used to record an intracranial pressure signal.

The microphone of the present system recorded the generalized sound of brain activity. Changes in brain vessel flow lead to turbulence and a change in the spectrum of the acoustic signal. In addition, cardiac periodicity leads to periodicity in blood flow which results in a periodically varying acoustic signal that reflects the physiological changes in the brain.

"Bursts" of intensity in the captured acoustic signal foretell changes in intracranial pressure. Thus, any change in intracranial pressure is initially accompanied by a signature in the acoustic sound which precedes detection by the Codman device.

Figures 15-16 illustrate changes in ICP as recorded by the Codman device and the system of the present invention. The changes in ICP are due to the affect of pathologies on physiological processes.

In the case of a Pineal Cyst (Figure 15), changes in ICP result from accumulated cerebrospinal fluid which causes vessels to constrict (resulting in changes to blood flow). The energetic content of the acoustic signal shifts to higher frequencies. Periodic fluctuations of energy are observed in the frequency range of 25-45 Hz (Enl3) instead of 5-15Hz or 15-30 Hz (Figure 16 describes normal pressure hydrocephalus).

A hydrocephalus (secondary to brain tumor) results in 9 acoustic artifacts accompanied by disturbances in the ICP (Figure 17). Synchronous (or near synchronous) amplitude bursts indicate artifacts of movement or change of the body position of the subject during recordings.

Figures 17 and 18a-f illustrate correlation between bursts in the acoustic signal and sharp changes in ICP. As is mentioned hereinabove, these bursts precede observed changes (Codman).

The signals of Figures 17 and 18a-f (SigO) were processed as described hereinabove in order to visualize the behavior of each isolated frequency band (Sigl l, Sigl2, Sigl3, Sigl4). Changes that similarly affect all frequency bands are shown in Figures 19a-b. Such changes are considered global and can result from elasticity loss, change in blood flow, change in the metabolic processes, hypercapnia, hypoxemia and in healthy individuals emotional condition or stressful changes of behavior. An event detected in a particular frequency band (Figures 20a-e) indicates local changes which can result from spasmodic processes of separate vessel regions, local change in ICP, and change in transmural pressure (as a result of aneurism or hematoma).

EXAMPLE 2

Normal Vs. Abnormal ICP

Acoustic signal capture and Codman readings were obtained from healthy (normal) and abnormal brains of subjects.

Figure 21 illustrates the acoustic signal spectrogram of a healthy individual. As is shown by the spectrogram, there is a clear pattern consisting of repeatable and similar complexes. Each complex includes two peaks, an acoustic response of systole (the higher one), lying in low frequency diapason, which may vary depending on the disease or health condition. In healthy individuals it starts at ~10Hz and ends at ~35Hz and an acoustic response of diastole (the lower one), which starts from a higher frequency than then the systole phase. The length and size of the diastole peak may also vary depending on the disease or health condition. In healthy individuals it starts at ~17Hz and ends at ~35Hz. Almost no energy is observed in higher frequencies (40-110 Hz), which may be interpreted as steady laminar flow of blood, with no disturbances and consequently with no additional noise.

Figure 22 illustrates a spectrogram of an acoustic signal obtained from a subject that underwent surgery to repair subarachnoid hemorrhage (SAH) caused by rupture of an aneurysm in the anterior communicating artery (ACOM).

Figure 23 illustrates a spectrogram of an acoustic signal obtained from a subject that underwent surgery to repair subarachnoid hemorrhage (SAH) caused by rupture of an aneurysm in the internal carotid artery (ICA).

Figure 24 illustrates a spectrogram of an acoustic signal obtained from a subject with Hydrocephalus treated with a VP shunt for several years and having shunt failure.

Figure 25 illustrates a spectrogram of an acoustic signal obtained from a subject having a pineal cyst, a benign, fluid-filled deposits located in the pineal gland region of the brain. Figure 26 illustrates a spectrogram of an acoustic signal obtained from a subject having idiopathic intracranial hypertension (IIH), a neurological disorder that is characterized by increased ICP in the absence of a tumor or other diseases.

Figure 27 illustrates a spectrogram of an acoustic signal obtained from a subject having traumatic brain injury (TBI) resulting from an external mechanical force, causing structural damage and impairment of brain function.

Figure 28 illustrates a spectrogram of an acoustic signal obtained from a subject suspected of having TBI.

The spectrograms of Figures 22-28 clearly illustrate that both systolic and diastolic peaks are affected by brain pathologies and can serve as indicators for changes in ICP.

EXAMPLE 3

Acoustic patterns reflecting functional disorders

A five minute recording of acoustic signals from a subject with suspected shunt failure was carried out using a prototype of the present system. Spectrograms were extracted from the acoustic signals as described hereinabove, and the spectrograms were analyzed as described above. Figure 29 illustrates a spectrogram with a normal periodic pattern indicating that the shunt functions properly. Figure 30 illustrates a spectrogram having a more chaotic less periodic pattern indicating malfunction of the shunt, and as a result a functional disorder.

Shunt failure can lead to idiopathic intracranial hypertension (IIH) which is detectable by the spectrogram of Figure 31 (functional shunt and no IIH is shown in Figure 32).

Patterns of minor and moderate vasospasms (after subarachnoid hemorrhage) are also detectable on a spectrogram of an acoustic signal as is shown in Figures 33-34 (respectively).

It is appreciated that certain features of the invention, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable subcombination. Although the invention has been described in conjunction with specific embodiments thereof, it is evident that many alternatives, modifications and variations will be apparent to those skilled in the art. Accordingly, it is intended to embrace all such alternatives, modifications and variations that fall within the spirit and broad scope of the appended claims. All publications, patents and patent applications mentioned in this specification are herein incorporated in their entirety by reference into the specification, to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated herein by reference. In addition, citation or identification of any reference in this application shall not be construed as an admission that such reference is available as prior art to the present invention.

Claims

WHAT IS CLAIMED IS:

1. A system for determining an intracranial pressure value comprising a computing platform configured for:

(a) capturing an intracranial acoustic signal resultant from fluid flow in a head of a subject;

(b) isolating acoustic signals at one or more frequency ranges;

(c) determining an energy, energy spectral density or amplitude of at least one of said acoustic signals or a portion thereof; and

(d) deriving an intracranial pressure value from said energy, or energy spectral density or said amplitude.

2. The system of claim 1, further comprising transforming said acoustic signals from a time domain to a frequency domain.

3. The system of claim 2, wherein (d) is effected using said energy level, said energy spectral density level or said amplitude values following non-linear transformation.

4. The system of claim 1, wherein said amplitude of (c) is determined from a representation of a time domain of said at least one of said acoustic signals or said portion thereof.

5. The system of claim 1, wherein said amplitude of (c) is determined from a representation of in a frequency domain of said at least one of said acoustic signals or said portion thereof.

6. The system of claim 1, wherein said energy level is the total energy of at least one of said acoustic signals or said portion thereof.

7. The system of claim 1, wherein said one or more frequency ranges are selected from the group consisting of 0.5-15 Hz, 0.5-25 Hz, 0.5-45 Hz, 0.5-75 Hz, 15-25 Hz, 25-45 Hz, 45-75 Hz and 75-90 Hz.

8. The system of claim 1, wherein said computing platform stores a model constructed from non-linearly transformed energy level, energy spectral density level and amplitude of said acoustic signals and their weights.

9. The system of claim 8, wherein invasive ICP values are utilized in said model to assign weights to said non-linearly transformed energy level, energy spectral density level and amplitude of said acoustic signals.

10. The system of claim 8, wherein (d) is effected by applying said model to said non-linearly transformed energy level, energy spectral density level and amplitude of said acoustic signals or said portion thereof.

11. The system of claim 1, wherein (a) is effected by an in-ear device including at least one microphone.

12. The system of claim 11, wherein said at least one microphone is a MEMS microphone.

13. The system of claim 12, wherein said MEMS microphone has a linear response of up to 130 dB sound pressure level (SPL).

14. The system of claim 12, wherein at least one microphone is characterized by low frequency extension to 6 Hz.

15. The system of claim 1, wherein said in-ear device is capable of filtering out non-intracranial acoustic signals.

16. The system of claim 1, further comprising applying a non-linear function to said energy level or energy spectral density level or amplitude prior to (d).

17. The system of claim 1, wherein said energy spectral density level is the total energy of at least one of said acoustic signals or said portion thereof transformed to a frequency domain.

18. A method of modeling a relationship between naturally occurring intracranial acoustic signals and ICP values, the method comprising:

(a) capturing an intracranial acoustic signal resultant from fluid flow in a head of each of a plurality of subjects;

(b) isolating acoustic signals at one or more frequency ranges from each intracranial acoustic signal captured;

(c) transforming said acoustic signals from a time domain to a frequency domain to thereby obtain transformed acoustic signals;

(d) determining an energy level or energy spectral density level or amplitude of said acoustic signals;

(e) obtaining invasive ICP values from said plurality of subjects; and

(f) correlating said non-linearly transformed energy level or energy spectral density level or amplitude of said acoustic signals to said invasive ICP values to thereby derive a weighted function correlating said non-linearly transformed energy level or energy spectral density level or amplitude of an intracranial acoustic signal to an invasive ICP value.

19. A method of determining an intracranial pressure value comprising:

(b) isolating acoustic signals at one or more frequency ranges;