CN112336319B - HRV detection method, device and storage medium - Google Patents
HRV detection method, device and storage medium Download PDFInfo
- Publication number
- CN112336319B CN112336319B CN202011063266.2A CN202011063266A CN112336319B CN 112336319 B CN112336319 B CN 112336319B CN 202011063266 A CN202011063266 A CN 202011063266A CN 112336319 B CN112336319 B CN 112336319B
- Authority
- CN
- China
- Prior art keywords
- hrv
- value
- curve
- determining
- interval
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biomedical Technology (AREA)
- Molecular Biology (AREA)
- Physiology (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Engineering & Computer Science (AREA)
- Cardiology (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- Physics & Mathematics (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- Measuring Frequencies, Analyzing Spectra (AREA)
- Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)
Abstract
The embodiment of the application provides a HRV detection method, a device and a storage medium, by acquiring PPG data and an acceleration signal; preprocessing the PPG data to obtain processed PPG data, and filtering the acceleration signal to obtain a processed acceleration signal; calculating pulse wave intervals according to the processed PPG data to obtain an original RR interval sequence; if the processed acceleration signal meets the preset condition, the HRV is calculated according to the original RR interval sequence, so that the HRV detection can be carried out by adopting the pulse wave data, and the HRV measurement can be carried out more accurately and conveniently.
Description
Technical Field
The application relates to the technical field of medical treatment, in particular to a HRV detection method, a device and a storage medium.
Background
Heart Rate Variability (HRV) refers to the variation of beat-to-beat cycle variability. HRV is a valuable index for judging the condition of cardiovascular diseases and the like, and preventing and predicting sudden cardiac death and arrhythmia events. Currently, many products on the market analyze HRV clinically, reflecting the activity and balance of the heart autonomic nervous system and related pathological states.
Existing HRV measurement methods include short-time testing and long-range testing. The data source is either Electrocardiogram (berg) data or an electrocardiograph monitor device. The short-time test is to measure through special equipment in a short time, has the advantages of short measurement and convenient use, but has large data fluctuation and larger error. The long-range test precision is higher, but the time is long, wears the dynamic electrocardiogram monitor throughout the day, and user's many actions all can receive the restriction, and the user is also convenient for the user to monitor own HRV change at any time under the daily life state simultaneously to the electrocardiogram monitor. Therefore, how to more accurately and conveniently measure the heart rate variability needs to be solved urgently.
Disclosure of Invention
The embodiment of the application provides an HRV detection method, an HRV detection device and a storage medium, which can adopt pulse wave data to carry out HRV detection and realize more accurate and convenient HRV measurement.
A first aspect of an embodiment of the present application provides a HRV detection method, where the method includes:
acquiring photoplethysmography (PPG) data and acceleration signals;
preprocessing the PPG data to obtain processed PPG data, and filtering the acceleration signal to obtain a processed acceleration signal;
calculating pulse wave intervals according to the processed PPG data to obtain an original RR interval sequence;
and if the processed acceleration signal meets a preset condition, calculating the HRV according to the original RR interval sequence.
A second aspect of embodiments of the present application provides an HRV detection apparatus, the apparatus including:
the acquisition unit is used for acquiring PPG data and an acceleration signal;
the processing unit is used for preprocessing the PPG data to obtain processed PPG data, and filtering the acceleration signal to obtain a processed acceleration signal;
the processing unit is further used for calculating pulse wave intervals according to the processed PPG data to obtain an original RR interval sequence;
the processing unit is further configured to calculate an HRV according to the original RR interval sequence if the processed acceleration signal satisfies a preset condition.
A third aspect of embodiments of the present application provides an HRV detection apparatus comprising a processor, a memory, a communication interface, and one or more programs stored in the memory and configured to be executed by the processor, the programs comprising instructions for performing some or all of the steps described in the method according to the first aspect of embodiments of the present application.
A fourth aspect of embodiments of the present application provides a computer-readable storage medium for storing a computer program, the computer program being executed by a processor to implement some or all of the steps described in the method according to the first aspect of embodiments of the present application.
A fifth aspect of embodiments of the present application provides a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the method as described in the first aspect of embodiments of the present application.
The embodiment of the application has at least the following beneficial effects:
it can be seen that, by the HRV detection method, apparatus, and storage medium in the embodiments of the present application, PPG data and acceleration signals are obtained; preprocessing the PPG data to obtain processed PPG data, and filtering the acceleration signal to obtain a processed acceleration signal; calculating pulse wave distance according to the processed PPG data to obtain an original RR interval sequence; if the processed acceleration signal meets the preset condition, the HRV is calculated according to the original RR interval sequence, so that the HRV detection can be carried out by adopting the pulse wave data, and the HRV measurement can be carried out more accurately and conveniently.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic structural diagram of an HRV detection apparatus according to an embodiment of the present disclosure;
fig. 2A is a schematic flowchart of an HRV detection method according to an embodiment of the present disclosure;
fig. 2B is a schematic diagram illustrating an example of raw PPG data acquired according to an embodiment of the present disclosure;
fig. 2C is a schematic diagram illustrating a PPG data after band-pass filtering provided in an embodiment of the present application;
FIG. 2D is a schematic diagram illustrating an example of a raw RR interval sequence;
fig. 2E is a schematic diagram of obtaining an octant according to an embodiment of the present disclosure;
FIG. 3 is a schematic flow chart of another HRV detection method provided in the embodiments of the present application;
fig. 4 is a schematic structural diagram of an HRV detection apparatus according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of another HRV detection apparatus according to an embodiment of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.
The terms "first," "second," and the like in the description and claims of the present application and in the foregoing drawings are used for distinguishing between different objects and not for describing a particular sequential order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an HRV detection apparatus provided in an embodiment of the present application, where the HRV detection apparatus may include a PPG data acquisition module, an acceleration sensor, a processor, and a memory, where,
the PPG data acquisition module is used for acquiring PPG data;
the acceleration sensor is used for acquiring an acceleration signal;
the processor is used for acquiring PPG data acquired by the PPG data acquisition module and acquiring an acceleration signal acquired by the acceleration sensor; and the number of the first and second groups,
performing band-pass filtering processing on the PPG data to obtain processed PPG data, and performing filtering processing on the acceleration signal to obtain a processed acceleration signal;
the processor is also used for calculating the pulse wave distance according to the processed PPG data to obtain an original RR interval sequence;
and the processor is further used for calculating the HRV according to the original RR interval sequence if the processed acceleration signal meets a preset condition.
Wherein, the memory is used for storing at least one of the following data: PPG data, acceleration signal, raw RR interval sequence, and HRV data.
Referring to fig. 2A, fig. 2A is a schematic flow chart of an HRV detection method according to an embodiment of the present disclosure. As shown in fig. 2A, the HRV detection method provided in the embodiment of the present application can be applied to the HRV detection apparatus shown in fig. 1, and the method can include the following steps:
201. acquiring PPG data and an acceleration signal;
in this application embodiment, HRV detection device can include multichannel PPG data acquisition module, acquires PPG data through multichannel PPG data acquisition module synchronization, for example, can gather 128 hz's PPG green light data, still can acquire acceleration signal through acceleration sensor and PPG data acquisition module synchronization, through multichannel PPG data acquisition module, can gather more PPG data to can calculate according to PPG data more accurately.
202. And preprocessing the PPG data to obtain processed PPG data, and filtering the acceleration signal to obtain a processed acceleration signal.
Wherein the pretreatment may include at least one of: and performing window system processing on the PPG data and performing band-pass filtering processing on the PPG data. Specifically, the first duration may be one window, the first duration may be 8 seconds, for example, the window is moved by a preset step size, the preset step size may be 2 seconds, for example, and then the band-pass filtering processing is performed on the PPG data of each window.
As shown in fig. 2B-2C, fig. 2B is a schematic diagram illustrating the acquired original PPG data provided in the embodiment of the present application, and fig. 2C is a schematic diagram illustrating the PPG data after the band-pass filtering provided in the embodiment of the present application.
In the time interval corresponding to each window, the acceleration signal can be filtered to obtain a processed acceleration signal.
Optionally, the filtering process may be performed on the PPG data or the acceleration signal by the following formula:
wherein, Y (n) is the PPG signal or acceleration discrete sequence after filtering, X (n) is the PPG signal or acceleration discrete sequence acquired, a K Filter coefficients for filtering the acceleration signal, b K Is a filter coefficient for filtering the PPG signal.
203. And calculating the pulse wave interval according to the processed PPG data to obtain an original RR interval sequence.
After the PPG data is subjected to band-pass filtering processing, the pulse wave interval can be calculated, and an original RR interval sequence is obtained.
Optionally, the calculating a pulse wave interval according to the processed PPG data to obtain a raw RR interval sequence includes:
and controlling a time window to move according to the PPG data by a preset step length, and storing the pulse wave interval updated in the PPG data in the moving process until an original RR interval sequence with preset duration is obtained.
Specifically, the step moving window may be preset until the original RR interval sequence with a preset duration is collected, where the preset duration may be, for example, 5 minutes or 8 minutes, and the embodiment of the present application is not limited.
204. And if the processed acceleration signal meets a preset condition, calculating the HRV according to the original RR interval sequence.
Specifically, whether the target object is in a quiet state or not can be judged according to the acceleration signal in the time interval corresponding to each window in the preset time length, if the target object is in the quiet state, the acceleration signal is determined to meet the preset condition, so that the HRV is calculated according to the original RR interval sequence, and if the acceleration signal does not meet the preset condition, the HRV is not calculated.
Optionally, the method further comprises:
41. judging whether the acceleration signal corresponding to the time window is in a quiet state or not according to the acceleration signal corresponding to the time window, and marking a judgment result to obtain a plurality of state marks within the preset time length;
42. and counting the plurality of state marks, and if the number of the state marks in the quiet state is greater than a preset threshold value, determining that the processed acceleration signal meets a preset condition.
In a specific implementation, it may be determined whether the acceleration signal of each time window is greater than a preset signal threshold, if not, the acceleration signal is determined to be in a quiet state, the quiet state is marked by a first state mark, for example, the quiet state may be marked as "1", if yes, the acceleration signal is determined not to be in the quiet state, the non-quiet state is marked by a second state mark, for example, the quiet state may be marked as "2", then the number of occurrences of the first state mark in a preset time period (for example, within 5 minutes) is counted, and if the number is greater than the preset threshold, it is determined that the processed acceleration signal satisfies a preset condition.
Optionally, the calculating HRV from the raw RR interval sequence comprises:
43. performing statistical sorting on the original RR interval sequences according to the sequence of the intervals from small to large to obtain the sorted RR interval sequences;
44. obtaining octants in the sequenced RR interval sequence to obtain an effective RR interval sequence;
45. reducing the effective RR interval sequence to a sequence position in the original RR interval sequence before sequencing to obtain a target RR interval sequence;
46. calculating time domain feature parameters of the HRV according to the target RR interval sequence;
47. and calculating the HRV value in a preset time period according to the time domain characteristic parameters.
Specifically, in this embodiment of the present application, for an original RR interval sequence RR (n) within a preset time duration, where n is 1, 2, …, n, as shown in fig. 2D, and fig. 2D is a schematic illustration of a demonstration of the original RR interval sequence provided in this embodiment of the present application, where an RR interval is an interval between a peak and a peak in PPG data, and the RR interval may be statistically sorted according to a sequence of the intervals from small to large, so as to obtain a sorted RR interval sequence, and obtain an RR interval sequence 1 (n), 1, 2, …, n, then obtaining octant in the sorted RR interval sequence to obtain effective RR interval sequence RR 1 (n/4), where n is n/8, n/8+1, …, 7 × n/8, as shown in fig. 2E, fig. 2E is a schematic diagram for obtaining octants provided in the embodiment of the present application, where the full distance refers to a range of all RR intervals in the sorted RR interval sequence, the upper bound refers to an upper limit value in the range of all RR intervals, the upper bound refers to a lower limit value in the range of all RR intervals, for example, the range of all RR intervals in the sorted RR interval sequence is (613, 875), the upper bound is 875, the lower bound is 613, the octant refers to a range between a maximum octant number and a minimum octant number in the RR interval sequence, the upper octant number refers to a maximum octant number, the lower octant number refers to a minimum octant number, for example, the maximum octant number is 812, the minimum octant number is 635, and the median number refers to a number at a position of 50%, for example, 750 may be that after the sorted RR interval sequences are obtained, a plurality of RR interval sequences in the sorted RR interval sequences are arranged in order of the small to large intervals, an octant may be sequentially obtained to obtain an effective RR interval sequence, and then the effective RR interval sequence is restored to the sequence position in the original RR interval sequence before sorting to obtain a target RR interval sequence RR (n/4), where n is 1, 2, …, 7 × n/8.
Optionally, the calculating temporal feature parameters of the HRV from the target RR interval sequence comprises:
4601. determining an RR interval average in the sequence of target RR intervals;
4602. calculating standard deviations of all NN intervals in the target RR interval sequence according to the target RR interval sequence and the RR interval average value;
4603. and calculating the root mean square value of the difference between all adjacent NN intervals according to the target RR interval sequence.
The standard deviation of all NN intervals in the target RR interval sequence is calculated according to the target RR interval sequence and the RR interval average value, and specifically, the standard deviation of all NN intervals in the target RR interval sequence can be calculated according to the following formula:
the SDNN is the standard deviation of all NN intervals in the target RR interval sequence, and the RR _ mean is the average value of RR intervals.
Calculating a root mean square value of the difference between all adjacent NN intervals according to the target RR interval sequence, specifically, calculating the root mean square value of the difference between all adjacent NN intervals according to the following formula:
where RNSSD is the root mean square value of the difference between all adjacent NN intervals.
Optionally, the method further comprises:
48. and generating an HRV distribution curve according to the HRV value in the preset time period.
The preset time period may be, for example, 24 hours, or may be a sleep time period of the target object.
In the implementation, during the night sleep period, the HRV can be automatically calculated, and the HRV distribution curve of the night sleep period is calculated, so that the user can know the HRV information of each time slot.
Optionally, after the generating an HRV distribution curve according to the HRV value in the preset time period, the method further includes:
50. determining a first-order difference value between every two adjacent HRV values in the HRV distribution curve to obtain a plurality of first-order difference values, and generating a first-order difference curve according to the plurality of first-order difference values;
51. dividing the first-order difference curve into at least one curve segment according to at least one preset threshold value;
52. and determining the sleep state corresponding to the HRV value range in which each curve segment is positioned in the at least one curve segment according to the preset mapping relation between the HRV value range and the sleep state.
In the embodiment of the present application, considering that the HRV values may present different HRV distribution curves in different sleep states of the user, the HRV distribution curves may be analyzed to identify the sleep state of the user, in a specific implementation, a first-order difference value between every two adjacent HRV values in the HRV distribution curves may be determined, and a first-order difference curve may be generated according to the first-order difference values, where a fluctuation state presented by the first-order difference curve is different from a range of the HRV values located in different sleep states, and therefore, a sleep state corresponding to the range of the HRV values located in each curve segment in at least one curve segment may be determined according to a mapping relationship between a preset range of the HRV values and the sleep state, and a sleep state within a time of each curve segment may be obtained.
Optionally, after step 51, the following steps may be further included, including:
53. determining at least one curve sub-segment of curve segment i where the HRV peak occurs; the starting position of each curve subsection corresponds to a first valley value, the ending position of each curve subsection corresponds to a second valley value, and the curve subsection i is any curve subsection in the at least one curve subsection;
54. determining an HRV value average value in the curve segment i;
55. determining a rising point at which a rising velocity is greater than a first value and a falling point at which a falling velocity is greater than a second value in each of the at least one curve sub-segment;
56. determining an HRV difference between a first HRV value corresponding to the ascending point and a second HRV value corresponding to the descending point in each of the at least one curve sub-segment, to obtain at least one HRV difference, where the first HRV value is greater than or equal to the first valley value, and the second HRV value is greater than or equal to the second valley value;
57. determining an average value of the at least one HRV difference value to obtain a target difference value average value; determining a first offset value corresponding to the target difference value average value;
58. determining a first HRV value range from the first offset value and the HRV value mean;
59. and determining a target sleep state corresponding to the first HRV value range according to a mapping relation between a preset HRV value range and the sleep state.
In specific implementation, at least one curve sub-segment with an HRV peak value in the curve segment i may be determined, then an average value of HRV values in the curve segment i may be determined, and a rising point at which a rising speed is greater than a first value and a falling point at which a falling speed is greater than a second value in each curve sub-segment may also be determined, generally, on one side of the HRV peak value, the HRV value may be in a rising trend, and a rising point at which the HRV distribution curve of the HRV value changes steepest on the side may be determined; on the other side of the HRV peak value, the HRV value is in a descending trend, and the descending point of the HRV value on the side with the steepest change of the HRV distribution curve can be determined; furthermore, an HRV difference between a first HRV value corresponding to the ascending point and a second HRV value corresponding to the descending point in each of the at least one curve sub-segment may be determined to obtain at least one HRV difference, and an average value of the at least one HRV difference may also be determined to obtain a target difference average value; determining a first offset value corresponding to the target difference value average value; and determining a first HRV value range according to the first offset value and the HRV value average value, wherein the first HRV value range can represent the range of the HRV value change of the user in the sleep state, and considering that the HRV value change of the user in the sleep state is different in different sleep states, the target sleep state of the user in the time corresponding to the curve segment i can be determined according to the first HRV value range.
It can be seen that, in the embodiment of the present application, PPG data and an acceleration signal are acquired; preprocessing the PPG data to obtain processed PPG data, and filtering the acceleration signal to obtain a processed acceleration signal; calculating pulse wave intervals according to the processed PPG data to obtain an original RR interval sequence; if the processed acceleration signal meets the preset condition, the HRV is calculated according to the original RR interval sequence, so that the HRV detection can be carried out by adopting the pulse wave data, and the HRV measurement can be carried out more accurately and conveniently.
Referring to fig. 3, fig. 3 is a schematic flow chart of an HRV detection method according to an embodiment of the present disclosure. As shown in fig. 3, the HRV detection method provided in the embodiment of the present application is applied to an HRV detection apparatus, and the HRV detection method may include the following steps:
301. PPG data and acceleration signals are acquired.
302. And preprocessing the PPG data to obtain processed PPG data, and filtering the acceleration signal to obtain a processed acceleration signal.
303. And controlling a time window to move according to the PPG data by a preset step length, and storing the pulse wave interval updated in the PPG data in the moving process until an original RR interval sequence with preset duration is obtained.
304. And judging whether the acceleration signal corresponding to the time window is in a quiet state or not according to the acceleration signal corresponding to the time window, and marking a judgment result to obtain a plurality of state marks in the preset time length.
305. And counting the plurality of state marks, and if the number of the state marks in the quiet state is greater than a preset threshold value, determining that the processed acceleration signal meets a preset condition.
306. And carrying out statistical sequencing on the original RR interval sequences according to the sequence of the intervals from small to large to obtain the sequenced RR interval sequences.
307. And acquiring octants in the sequenced RR interval sequence to obtain an effective RR interval sequence.
308. And restoring the effective RR interval sequence to the sequence position in the original RR interval sequence before sequencing to obtain a target RR interval sequence.
309. Calculating time-domain feature parameters of the HRV from the target RR interval sequence.
310. And calculating the HRV value in a preset time period according to the time domain characteristic parameters.
311. And generating an HRV distribution curve according to the HRV value in the preset time period.
The specific implementation process of 301-311 can refer to the corresponding description in the method shown in fig. 2, and is not described herein again.
It can be seen that by acquiring PPG data and acceleration signals; preprocessing the PPG data to obtain processed PPG data, and filtering the acceleration signal to obtain a processed acceleration signal; controlling a time window to move according to PPG data by a preset step length, storing updated pulse wave intervals in the PPG data in the moving process until an original RR interval sequence with preset duration is obtained, judging whether the time window is in a quiet state according to an acceleration signal corresponding to the time window, marking a judgment result to obtain a plurality of state marks in the preset duration, counting the plurality of state marks, determining that the processed acceleration signal meets a preset condition if the number of the state marks in the quiet state is greater than a preset threshold value, performing statistical sorting on the original RR interval sequence according to the sequence of the intervals from small to large to obtain a sorted RR interval sequence, obtaining an octant number in the sorted RR interval sequence to obtain an effective RR interval sequence, reducing the effective RR interval sequence to a sequence position in the original RR interval sequence before sorting to obtain a target RR interval sequence, the time domain characteristic parameters of the HRV are calculated according to the target RR interval sequence, the HRV value in the preset time period is calculated according to the time domain characteristic parameters, and the HRV distribution curve is generated according to the HRV value in the preset time period.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an HRV detection apparatus 400 according to the present embodiment, the apparatus 400 includes an obtaining unit 401 and a processing unit 402, wherein,
the acquiring unit 401 is configured to acquire PPG data and an acceleration signal;
the processing unit 402 is configured to pre-process the PPG data to obtain processed PPG data, and perform filtering processing on the acceleration signal to obtain a processed acceleration signal;
the processing unit 402 is further configured to calculate a pulse wave interval according to the processed PPG data, so as to obtain an original RR interval sequence;
the processing unit 402 is further configured to calculate an HRV according to the original RR interval sequence if the processed acceleration signal satisfies a preset condition.
Optionally, in terms of calculating a pulse wave interval according to the processed PPG data to obtain an original RR interval sequence, the processing unit 402 is specifically configured to:
and controlling a time window to move according to the PPG data by a preset step length, and storing the pulse wave interval updated in the PPG data in the moving process until an original RR interval sequence with preset duration is obtained.
Optionally, the processing unit 402 is further configured to:
judging whether the acceleration signal corresponding to the time window is in a quiet state or not according to the acceleration signal corresponding to the time window, and marking a judgment result to obtain a plurality of state marks within the preset time length;
and counting the plurality of state marks, and if the number of the state marks in the quiet state is greater than a preset threshold value, determining that the processed acceleration signal meets a preset condition.
Optionally, in terms of the calculating HRV from the raw RR interval sequence, the processing unit 402 is specifically configured to:
performing statistical sorting on the original RR interval sequences according to the sequence of the intervals from small to large to obtain the sorted RR interval sequences;
obtaining octants in the sequenced RR interval sequence to obtain an effective RR interval sequence;
restoring the effective RR interval sequence to the sequence position in the original RR interval sequence before sequencing to obtain a target RR interval sequence;
calculating time domain feature parameters of the HRV according to the target RR interval sequence;
and calculating the HRV value in a preset time period according to the time domain characteristic parameters.
Optionally, in terms of the calculating the temporal feature parameter of the HRV according to the target RR interval sequence, the processing unit 402 is specifically configured to:
determining an RR interval average in the sequence of target RR intervals;
calculating standard deviations of all NN intervals in the target RR interval sequence according to the target RR interval sequence and the RR interval average value;
and calculating the root mean square value of the difference between all adjacent NN intervals according to the target RR interval sequence.
Optionally, the processing unit 402 is further configured to:
and generating an HRV distribution curve according to the HRV value in the preset time period.
Optionally, the preset time period is a sleep time period in which the target subject is in a sleep state, and after the HRV distribution curve is generated according to the HRV values in the preset time period, the processing unit 402 is further configured to:
determining a first-order difference value between every two adjacent HRV values in the HRV distribution curve to obtain a plurality of first-order difference values, and generating a first-order difference curve according to the plurality of first-order difference values;
dividing the first-order difference curve into at least one curve segment according to at least one preset threshold value;
and determining the sleep state corresponding to the HRV value range in which each curve segment is positioned in the at least one curve segment according to the preset mapping relation between the HRV value range and the sleep state.
Optionally, after the dividing the first-order difference curve into at least one curve segment according to at least one preset threshold, the processing unit 402 is further configured to:
determining at least one curve sub-segment of curve segment i where the HRV peak occurs; the starting position of each curve subsection corresponds to a first valley value, the ending position of each curve subsection corresponds to a second valley value, and the curve subsection i is any one of the at least one curve subsection;
determining an average value of HRV values in the curve segment i;
determining a rising point at which a rising speed is greater than a first value and a falling point at which a falling speed is greater than a second value in each of the at least one curve sub-segment;
determining an HRV difference between a first HRV value corresponding to the ascending point and a second HRV value corresponding to the descending point in each of the at least one curve sub-segment, to obtain at least one HRV difference, where the first HRV value is greater than or equal to the first valley value, and the second HRV value is greater than or equal to the second valley value;
determining an average value of the at least one HRV difference value to obtain a target difference value average value; determining a first offset value corresponding to the target difference value average value;
determining a first HRV value range from the first offset value and the HRV value mean;
and determining a target sleep state corresponding to the first HRV value range according to a mapping relation between a preset HRV value range and the sleep state.
It can be seen that the HRV detection apparatus described in the embodiment of the present application obtains PPG data and an acceleration signal; preprocessing the PPG data to obtain processed PPG data, and filtering the acceleration signal to obtain a processed acceleration signal; calculating pulse wave intervals according to the processed PPG data to obtain an original RR interval sequence; if the processed acceleration signal meets the preset condition, the HRV is calculated according to the original RR interval sequence, so that the HRV detection can be carried out by adopting the pulse wave data, and the HRV measurement can be carried out more accurately and conveniently.
Referring to fig. 5, fig. 5 is a schematic structural diagram of an HRV detection apparatus disclosed in an embodiment of the present application, and as shown in the drawing, the HRV detection apparatus includes a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for performing the following steps:
acquiring PPG data and an acceleration signal;
preprocessing the PPG data to obtain processed PPG data, and filtering the acceleration signal to obtain a processed acceleration signal;
calculating pulse wave intervals according to the processed PPG data to obtain an original RR interval sequence;
and if the processed acceleration signal meets a preset condition, calculating the HRV according to the original RR interval sequence.
In one possible example, in said calculating pulse wave intervals from said processed PPG data, resulting in a raw RR interval sequence, the above procedure comprises instructions for performing the following steps:
and controlling a time window to move according to the PPG data by a preset step length, and storing the pulse wave interval updated in the PPG data in the moving process until an original RR interval sequence with preset duration is obtained.
In one possible example, the program further includes instructions for performing the steps of:
judging whether the acceleration signal corresponding to the time window is in a quiet state or not according to the acceleration signal corresponding to the time window, and marking a judgment result to obtain a plurality of state marks within the preset time length;
and counting the plurality of state marks, and if the number of the state marks in the quiet state is greater than a preset threshold value, determining that the processed acceleration signal meets a preset condition.
In one possible example, in the calculating HRV from the raw RR interval sequence, the above program includes instructions for performing the steps of:
performing statistical sorting on the original RR interval sequences according to the sequence of the intervals from small to large to obtain the sorted RR interval sequences;
obtaining octants in the sequenced RR interval sequence to obtain an effective RR interval sequence;
reducing the effective RR interval sequence to a sequence position in the original RR interval sequence before sequencing to obtain a target RR interval sequence;
calculating time domain feature parameters of the HRV according to the target RR interval sequence;
and calculating the HRV value in a preset time period according to the time domain characteristic parameters.
In one possible example, in the calculating the temporal characteristic parameter of the HRV from the target RR interval sequence, the above procedure includes instructions for performing the steps of:
determining an RR interval average in the sequence of target RR intervals;
calculating standard deviations of all NN intervals in the target RR interval sequence according to the target RR interval sequence and the RR interval average value;
and calculating the root mean square value of the difference between all adjacent NN intervals according to the target RR interval sequence.
In one possible example, the program further includes instructions for performing the steps of:
and generating an HRV distribution curve according to the HRV value in the preset time period.
In one possible example, the preset time period is a sleep time period in which the target subject is in a sleep state, and after the HRV profile is generated according to the HRV values in the preset time period, the program further includes instructions for performing the following steps:
determining a first-order difference value between every two adjacent HRV values in the HRV distribution curve to obtain a plurality of first-order difference values, and generating a first-order difference curve according to the plurality of first-order difference values;
dividing the first-order difference curve into at least one curve segment according to at least one preset threshold value;
and determining the sleep state corresponding to the HRV value range in which each curve segment is positioned in the at least one curve segment according to the preset mapping relation between the HRV value range and the sleep state.
In a possible example, after said dividing of said first order difference curve into at least one curve segment according to a preset at least one threshold, the above procedure comprises instructions for performing the following steps:
determining at least one curve sub-segment of curve segment i where the HRV peak occurs; the starting position of each curve subsection corresponds to a first valley value, the ending position of each curve subsection corresponds to a second valley value, and the curve subsection i is any curve subsection in the at least one curve subsection;
determining an HRV value average value in the curve segment i;
determining a rising point at which a rising speed is greater than a first value and a falling point at which a falling speed is greater than a second value in each of the at least one curve sub-segment;
determining an HRV difference between a first HRV value corresponding to the ascending point and a second HRV value corresponding to the descending point in each of the at least one curve sub-segment, to obtain at least one HRV difference, where the first HRV value is greater than or equal to the first valley value, and the second HRV value is greater than or equal to the second valley value;
determining an average value of the at least one HRV difference value to obtain a target difference value average value; determining a first offset value corresponding to the target difference value average value;
determining a first HRV value range from the first offset value and the HRV value mean;
and determining a target sleep state corresponding to the first HRV value range according to a mapping relation between a preset HRV value range and the sleep state.
Embodiments of the present application also provide a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any one of the HRV detection methods as described in the above method embodiments.
Embodiments of the present application also provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program, the computer program causing a computer to perform some or all of the steps of any one of the HRV detection methods as set forth in the above method embodiments.
It should be noted that for simplicity of description, the above-mentioned embodiments of the method are described as a series of acts, but those skilled in the art should understand that the present application is not limited by the described order of acts, as some steps may be performed in other orders or simultaneously according to the present application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to the related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may be implemented in the form of a software program module.
The integrated units, if implemented in the form of software program modules and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned memory comprises: various media capable of storing program codes, such as a usb disk, a read-only memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and the like.
Those skilled in the art will appreciate that all or part of the steps of the methods of the above embodiments may be implemented by a program, which is stored in a computer-readable memory, the memory including: flash memory disks, read-only memory, random access memory, magnetic or optical disks, and the like.
The foregoing embodiments have been described in detail, and specific examples are used herein to explain the principles and implementations of the present application, where the above description of the embodiments is only intended to help understand the method and its core ideas of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (6)
1. A HRV detection method, the method comprising:
acquiring PPG data and an acceleration signal;
preprocessing the PPG data to obtain processed PPG data, and filtering the acceleration signal to obtain a processed acceleration signal;
calculating pulse wave distance according to the processed PPG data to obtain an original RR interval sequence;
if the processed acceleration signal meets a preset condition, calculating HRV according to the original RR interval sequence;
wherein said calculating an HRV from the raw RR interval sequence comprises:
performing statistical sorting on the original RR interval sequences according to the sequence of the intervals from small to large to obtain the sorted RR interval sequences;
obtaining octants in the sequenced RR interval sequence to obtain an effective RR interval sequence;
reducing the effective RR interval sequence to a sequence position in the original RR interval sequence before sequencing to obtain a target RR interval sequence;
calculating time domain characteristic parameters of the HRV according to the target RR interval sequence;
calculating an HRV value in a preset time period according to the time domain characteristic parameters;
wherein the calculating time-domain feature parameters of the HRV from the target RR interval sequence comprises:
determining an RR interval average in the sequence of target RR intervals;
calculating standard deviations of all NN intervals in the target RR interval sequence according to the target RR interval sequence and the RR interval average value;
calculating a root mean square value of a difference between all adjacent NN intervals according to the target RR interval sequence;
wherein the method further comprises:
generating an HRV distribution curve according to the HRV value in the preset time period;
wherein, the preset time period is a sleep time period when the target object is in a sleep state, and after the HRV distribution curve is generated according to the HRV value in the preset time period, the method further includes:
determining a first-order difference value between every two adjacent HRV values in the HRV distribution curve to obtain a plurality of first-order difference values, and generating a first-order difference curve according to the plurality of first-order difference values;
dividing the first-order difference curve into at least one curve segment according to at least one preset threshold value;
determining a sleep state corresponding to the HRV value range in which each curve segment of the at least one curve segment is located according to a preset mapping relation between the HRV value range and the sleep state;
wherein, the determining the sleep state corresponding to the HRV value range in which each curve segment of the at least one curve segment is located according to the preset mapping relationship between the HRV value range and the sleep state includes:
determining at least one curve sub-segment of curve segment i where the HRV peak occurs; the starting position of each curve subsection corresponds to a first valley value, the ending position of each curve subsection corresponds to a second valley value, and the curve subsection i is any curve subsection in the at least one curve subsection;
determining an average value of HRV values in the curve segment i;
determining a rising point at which a rising velocity is greater than a first value and a falling point at which a falling velocity is greater than a second value in each of the at least one curve sub-segment;
determining an HRV difference between a first HRV value corresponding to the ascending point and a second HRV value corresponding to the descending point in each of the at least one curve sub-segment, to obtain at least one HRV difference, where the first HRV value is greater than or equal to the first valley value, and the second HRV value is greater than or equal to the second valley value;
determining an average value of the at least one HRV difference value to obtain a target average value of difference values; determining a first offset value corresponding to the target difference value average value;
determining a first HRV value range from the first offset value and the HRV value mean;
and determining a target sleep state corresponding to the first HRV value range according to a mapping relation between a preset HRV value range and the sleep state.
2. The method of claim 1, wherein said calculating pulse wave spacing from said processed PPG data, resulting in a sequence of raw RR intervals, comprises:
and controlling a time window to move according to the PPG data by a preset step length, and storing the pulse wave interval updated in the PPG data in the moving process until an original RR interval sequence with preset duration is obtained.
3. The method of claim 2, further comprising:
judging whether the acceleration signal corresponding to the time window is in a quiet state or not according to the acceleration signal corresponding to the time window, and marking a judgment result to obtain a plurality of state marks in the preset time length;
and counting the plurality of state marks, and if the number of the state marks in the quiet state is greater than a preset threshold value, determining that the processed acceleration signal meets a preset condition.
4. An HRV detection apparatus, the apparatus comprising:
the acquisition unit is used for acquiring PPG data and an acceleration signal;
the processing unit is used for preprocessing the PPG data to obtain processed PPG data, and filtering the acceleration signal to obtain a processed acceleration signal;
the processing unit is further used for calculating pulse wave intervals according to the processed PPG data to obtain an original RR interval sequence;
the processing unit is further configured to calculate an HRV according to the original RR interval sequence if the processed acceleration signal meets a preset condition;
wherein said calculating an HRV from the raw RR interval sequence comprises:
performing statistical sorting on the original RR interval sequences according to the sequence of the intervals from small to large to obtain the sorted RR interval sequences;
obtaining octants in the sequenced RR interval sequence to obtain an effective RR interval sequence;
reducing the effective RR interval sequence to a sequence position in the original RR interval sequence before sequencing to obtain a target RR interval sequence;
calculating time domain feature parameters of the HRV according to the target RR interval sequence;
calculating an HRV value in a preset time period according to the time domain characteristic parameters;
wherein the calculating time-domain feature parameters of the HRV from the target RR interval sequence comprises:
determining an RR interval average in the sequence of target RR intervals;
calculating standard deviations of all NN intervals in the target RR interval sequence according to the target RR interval sequence and the RR interval average value;
calculating a root mean square value of a difference between all adjacent NN intervals according to the target RR interval sequence;
wherein the apparatus is further specifically configured to:
generating an HRV distribution curve according to the HRV value in the preset time period;
wherein, the preset time period is a sleep time period in which the target object is in a sleep state, and after the HRV distribution curve is generated according to the HRV value in the preset time period, the apparatus is further specifically configured to:
determining a first-order difference value between every two adjacent HRV values in the HRV distribution curve to obtain a plurality of first-order difference values, and generating a first-order difference curve according to the plurality of first-order difference values;
dividing the first-order difference curve into at least one curve segment according to at least one preset threshold value;
determining a sleep state corresponding to the HRV value range of each curve segment in the at least one curve segment according to a mapping relation between a preset HRV value range and the sleep state;
wherein, the determining the sleep state corresponding to the HRV value range in which each curve segment of the at least one curve segment is located according to the preset mapping relationship between the HRV value range and the sleep state includes:
determining at least one curve sub-segment of curve segment i where the HRV peak occurs; the starting position of each curve subsection corresponds to a first valley value, the ending position of each curve subsection corresponds to a second valley value, and the curve subsection i is any curve subsection in the at least one curve subsection;
determining an HRV value average value in the curve segment i;
determining a rising point at which a rising velocity is greater than a first value and a falling point at which a falling velocity is greater than a second value in each of the at least one curve sub-segment;
determining an HRV difference between a first HRV value corresponding to the ascending point and a second HRV value corresponding to the descending point in each of the at least one curve sub-segment, to obtain at least one HRV difference, where the first HRV value is greater than or equal to the first valley value, and the second HRV value is greater than or equal to the second valley value;
determining an average value of the at least one HRV difference value to obtain a target difference value average value; determining a first offset value corresponding to the target difference value average value;
determining a first HRV value range from the first offset value and the HRV value mean;
and determining a target sleep state corresponding to the first HRV value range according to a mapping relation between a preset HRV value range and the sleep state.
5. An HRV-based detection apparatus comprising a processor, a memory, a communication interface, and one or more programs stored in the memory and configured to be executed by the processor, the programs comprising instructions for performing the steps in the method of any of claims 1-3.
6. A computer-readable storage medium, characterized in that a computer program for electronic data exchange is stored, wherein the computer program causes a computer to perform the method according to any of claims 1-3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011063266.2A CN112336319B (en) | 2020-09-30 | 2020-09-30 | HRV detection method, device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011063266.2A CN112336319B (en) | 2020-09-30 | 2020-09-30 | HRV detection method, device and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112336319A CN112336319A (en) | 2021-02-09 |
| CN112336319B true CN112336319B (en) | 2022-09-13 |
Family
ID=74361601
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011063266.2A Active CN112336319B (en) | 2020-09-30 | 2020-09-30 | HRV detection method, device and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112336319B (en) |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8019406B2 (en) * | 2008-03-10 | 2011-09-13 | Biotronik Crm Patent Ag | Apparatus and method to assess the risk of R-on-T event |
| US10183142B2 (en) * | 2013-04-10 | 2019-01-22 | Koninklijke Philips N.V. | System and method for enhancing sleep slow wave activity based on cardiac activity |
| CN107890339B (en) * | 2017-11-09 | 2020-09-08 | 常熟理工学院 | Sleep staging detection method and wearable sleep staging detection device |
| CN107874750B (en) * | 2017-11-28 | 2020-01-10 | 华南理工大学 | Pulse rate variability and sleep quality fused psychological pressure monitoring method and device |
| CN109222949B (en) * | 2018-10-12 | 2021-07-09 | 杭州士兰微电子股份有限公司 | Heart rate detection method and heart rate detection device |
-
2020
- 2020-09-30 CN CN202011063266.2A patent/CN112336319B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN112336319A (en) | 2021-02-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Brennan et al. | Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability? | |
| CN110338786B (en) | Epileptic discharge identification and classification method, system, device and medium | |
| US8568330B2 (en) | Composite human physiological stress index based on heart beat and sleep and/or activity history data including actigraphy | |
| JP5865379B2 (en) | Method for determining the characteristics of a subject's circadian rhythm | |
| CN110236491B (en) | Sleep stage monitoring method | |
| CN109009028B (en) | Wearable device capable of reflecting human fatigue degree | |
| EP2845539B1 (en) | Device and method for automatically normalizing the physiological signals of a living being | |
| CN108209912A (en) | A kind of electromyographic signal collection method and device | |
| CN109938719B (en) | Driver fatigue detection method based on physiological parameters | |
| US10342474B2 (en) | System for the analysis of the daily heart rhythm autonomic nervous system balance | |
| CN103405225A (en) | Method, apparatus and device for obtaining pain feeling evaluation indexes | |
| WO2013059057A1 (en) | System and method of quality analysis in acquisition of ambulatory electrocardiography device data | |
| KR101870630B1 (en) | Method and device for the measurement of energy consumption based on vital/motion signals | |
| CN111839494A (en) | Heart rate monitoring method and system | |
| CN109674474B (en) | Sleep apnea recognition method, device and computer readable medium | |
| US20150272474A1 (en) | Respiration rate determination in impedance pneumography | |
| Malik | Time-domain measurement of heart rate variability | |
| US8073218B2 (en) | Method for detecting bio signal features in the presence of noise | |
| CN108652611B (en) | Heart rate calculation method, device, equipment and computer readable storage medium | |
| CN112336319B (en) | HRV detection method, device and storage medium | |
| CN117271977A (en) | HRV data preprocessing method and device and electronic equipment | |
| CN110353705B (en) | Method and device for recognizing emotion | |
| CN116942182A (en) | Physical health monitoring device | |
| US8301230B2 (en) | Method for reducing baseline drift in a biological signal | |
| CN114010185B (en) | Method, device, equipment and medium for monitoring respiratory disorder |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |