CN111839491A - Heart beat function monitoring method, heart beat function continuous monitoring method and electronic equipment - Google Patents

Heart beat function monitoring method, heart beat function continuous monitoring method and electronic equipment Download PDF

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CN111839491A
CN111839491A CN202010609616.4A CN202010609616A CN111839491A CN 111839491 A CN111839491 A CN 111839491A CN 202010609616 A CN202010609616 A CN 202010609616A CN 111839491 A CN111839491 A CN 111839491A
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heart rate
frequency domain
preset
heartbeat
heart
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CN111839491B (en
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刘旭华
肖婷婷
韩烽
于雪平
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Zhongke Zhenzhi Medical Instrument Jinan Co ltd
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Beijing Shuguang Autopass Technology Co ltd
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    • 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
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/02405—Determining heart rate variability
    • 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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7253—Details of waveform analysis characterised by using transforms
    • A61B5/7257—Details of waveform analysis characterised by using transforms using Fourier transforms
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient ; user input means
    • A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms

Abstract

The invention provides a heartbeat function monitoring method, a heartbeat function continuous monitoring method and an electronic device. The heart beat function monitoring method comprises the following steps: acquiring heartbeat waveform data with preset time length; respectively carrying out frequency domain analysis and time domain analysis on the heartbeat waveform data with the preset duration to obtain a frequency domain heart rate and a time domain heart rate within the preset duration; and determining the comprehensive heart rate in the preset time length according to the frequency domain heart rate and the time domain heart rate in the preset time length, wherein the comprehensive heart rate in the preset time length is used for determining whether abnormal heart rate exists in the preset time length. The invention realizes the real-time monitoring of heart rate and heart rhythm and the long-term monitoring of heart rate variability, can give out timely early warning to heart rate abnormity, arrhythmia and heart rate variability abnormity, and helps users to carry out early prevention and diagnosis and treatment on cardiovascular diseases.

Description

Heart beat function monitoring method, heart beat function continuous monitoring method and electronic equipment
Technical Field
The invention relates to the technical field of medical care, in particular to a heart beat function monitoring method, a heart beat function continuous monitoring method and electronic equipment.
Background
A large body of research data suggests that cardiovascular disease is one of the most mortality diseases at present, and the primary predictor of the onset of such disease is arrhythmia (e.g., bradycardia, tachycardia) or arrhythmia. In addition, cardiac arrhythmias are also a major cause of sudden death.
If early warning can be carried out on cardiovascular diseases, early detection and early treatment of the diseases are realized, and the survival rate of patients can be greatly improved. Researchers find that the cardiovascular diseases can be effectively predicted by continuously monitoring the heart beat function of the human body and analyzing the heart beat function indexes of the human body. Therefore, it is important to provide a method for continuously monitoring the cardiac function of a human body.
Disclosure of Invention
According to an embodiment of the present invention, there is provided a cardiac function monitoring method including: acquiring heartbeat waveform data of a predetermined duration, wherein the heartbeat waveform data represents heart beat amplitude over time; respectively carrying out frequency domain analysis and time domain analysis on the heartbeat waveform data with the preset duration to obtain a frequency domain heart rate and a time domain heart rate within the preset duration; and determining the comprehensive heart rate in the preset time length according to the frequency domain heart rate and the time domain heart rate in the preset time length so as to determine whether abnormal heart rate exists in the preset time length.
In the method, the frequency domain analysis and the time domain analysis of the heartbeat waveform data with the preset time length respectively comprise: dividing the heartbeat waveform data of the preset duration into m segments of sub-heartbeat waveform data, converting each segment of sub-heartbeat waveform data into heartbeat distribution data on a frequency domain, extracting segmented frequency domain heart rate from the heartbeat distribution data on each segment of frequency domain, and determining the frequency domain heart rate in the preset duration according to the extracted segmented frequency domain heart rate; dividing the heartbeat waveform data with the preset duration into n segments of sub-heartbeat waveform data, converting each segment of sub-heartbeat waveform data into sub-heartbeat waveform data on a time domain, extracting a segmented time domain heart rate from the sub-heartbeat waveform data on each segment of the time domain, and determining the time domain heart rate within the preset duration according to the extracted segmented time domain heart rate; wherein m and n are integers of 1 or more. The above method may further comprise: sub-heartbeat waveform data having a standard deviation not within a predetermined heartbeat stability range is excluded from the m pieces of sub-heartbeat waveform data.
In the method, converting each segment of sub-heartbeat waveform data into heartbeat distribution data on a frequency domain comprises: acquiring an envelope curve of the segment of sub-heartbeat waveform data; carrying out mean value filtering and normalization operation on the envelope curve; and performing discrete Fourier transform on the envelope subjected to the mean filtering and normalization operation to obtain heart beat distribution data on a frequency domain.
In the above method, extracting the segmented frequency domain heart rate from the heart beat distribution data on each segment of the frequency domain comprises:
searching peak points with the amplitude larger than a preset frequency domain threshold value in a preset heart rate frequency domain area of heart beat distribution data on the section of frequency domain to obtain a peak point set FPS, and determining the number of the peak points in the peak point set FPS;
responding to the fact that a peak point is contained in the peak point set FPS, and setting the heart rate of the segmented frequency domain as the frequency of the peak point; or
Responding to the fact that the peak point set FPS comprises a plurality of peak points, clustering all the peak points in the peak point set FPS based on frequency, and determining the number of the obtained classes;
responding to the clustering result of one type or more than two types, and setting the heart rate of the segmented frequency domain as the frequency of the peak point with the maximum amplitude in the peak point set FPS; or
Responding to the clustering result of two types, and judging whether the proportion of the clustering kernels of the two types meets a quadratic relation within a preset error range; if not, setting the heart rate of the segmented frequency domain as the frequency of the peak point with the maximum amplitude in the peak point set FPS; if so, selecting the class with the smaller clustering kernel in the two classes as a target class, and determining the number of peak points in the target class;
In response to the target class containing a peak point, setting the segmented frequency domain heart rate to the frequency of the peak point; or
And in response to the target class containing a plurality of peak points, setting the segmented frequency domain heart rate to the frequency of the peak point with the maximum amplitude in the target class.
In the method, the method further includes extracting a heartbeat rhythmicity index from heartbeat distribution data on each frequency domain, determining a heart rhythm determination result within the predetermined time length according to the extracted heartbeat rhythmicity index, and outputting the heart rhythm determination result within the predetermined time length. Wherein, extracting the heart beat rhythmicity index from the heart beat distribution data on each section of frequency domain comprises the following steps:
responding to the fact that the peak point set FPS comprises a peak point or responding to the fact that the target class comprises a peak point, and setting the heartbeat rhythmicity index to indicate abnormal heartbeat rhythm;
setting a heartbeat rhythmicity index according to the following formula in response to the condition that the peak point set FPS comprises a plurality of peak points and the clustering result is of one type, or in response to the condition that the peak point set FPS comprises a plurality of peak points, the clustering result is of two types, and the proportion of the clustering kernels of the two types does not meet the quadratic relation in a preset error range:
Figure BDA0002560510670000031
Wherein cri represents a heart beat rhythmicity index;
Figure BDA0002560510670000032
representing a maximum amplitude of a peak point in the set of peak points FPS; MB represents the maximum frequency bandwidth of all intervals of the heartbeat distribution data on the section of frequency domain, the amplitude of which is continuously larger than a preset frequency domain threshold value, in the preset heart rate frequency domain area;
responding to the condition that the peak point set FPS comprises a plurality of peak points and the clustering result is more than two types, setting heartbeat distribution data on the frequency domain section as a heartbeat rhythmicity index which is not extracted;
in response to the target class containing a plurality of peak points, setting a heartbeat rhythmicity indicator according to:
Figure BDA0002560510670000033
wherein cri denotes a cardiac rhythmicity index; tar represents the target class in question,
Figure BDA0002560510670000034
representing a maximum amplitude of a peak point in the target class Tar; MB represents the maximum frequency bandwidth of all intervals corresponding to the target class, of which the amplitude is continuously larger than a predetermined frequency domain threshold value, of the heartbeat distribution data on the section of frequency domain in the preset heart rate frequency domain area.
Determining the heart rhythm judgment result within the preset time length according to the extracted heart rhythm index comprises the following steps: counting the number of the heart rhythm indexes which are larger than a preset heart rhythm threshold value in all the extracted heart rhythm indexes; calculating the ratio of the counted number to the extracted number of all the heart rhythm indexes; if the ratio exceeds a preset ratio, setting the rhythm judgment result in the preset time length as arrhythmia, otherwise, setting the rhythm judgment result in the preset time length as normal rhythm.
In the above method, extracting the segmented frequency domain heart rate and the heart beat rhythmicity index from the heart beat distribution data on each segment of the frequency domain further includes: and comparing the maximum amplitude of the heartbeat distribution data on the section of frequency domain in the low-frequency area with the maximum amplitude of the heartbeat distribution data on the preset heartbeat frequency domain area, and setting the heartbeat distribution data on the section of frequency domain as that the segmented frequency domain heart rate and the heartbeat rhythmicity index are not extracted in response to that the maximum amplitude of the low-frequency area is greater than the maximum amplitude of the preset heartbeat frequency domain area.
In the method, determining the frequency domain heart rate within the predetermined time period according to the extracted segmented frequency domain heart rate includes: and taking the extracted average value of all the segmented frequency domain heart rates as the frequency domain heart rate in the preset time length.
In the method, converting each segment of sub-heartbeat waveform data into sub-heartbeat waveform data in a time domain includes: and (4) calculating the Shannon energy of the segment of sub-heartbeat waveform data, and calculating an autocorrelation curve by using the Shannon energy to obtain the sub-heartbeat waveform data in the time domain.
In the method, extracting the segmented time-domain heart rate from the sub-heartbeat waveform data in each segment of the time domain comprises the following steps:
searching peak points with amplitude values larger than a preset time domain threshold value in the sub-heartbeat waveform data on the time domain to obtain a peak point set TPS, and setting a polynomial fitting order as a first preset order;
Comparing the number of peak points in the peak point set TPS with a preset second number threshold;
responding to the fact that the number of peak points in the peak point set TPS is smaller than or equal to a preset second number threshold, and setting sub-heartbeat waveform data in the time domain as the heart rate of the segmented time domain which is not extracted; or
Responding to the fact that the number of peak points in the peak point set TPS is larger than a preset second quantity threshold, performing polynomial high-order curve fitting on sub-heartbeat waveform data in the time domain by utilizing the polynomial fitting order to obtain a fitting curve, searching peak points with amplitude larger than a preset fitting curve threshold on the fitting curve to obtain a peak point set FCPS, and comparing the number of the peak points in the peak point set FCPS with a preset third quantity threshold;
responding to the number of the peak points in the peak point set FCPS is smaller than or equal to a third quantity threshold value, and setting the sub-heartbeat waveform data in the segment of time domain as the segmented time domain heart rate which is not extracted; or
Responding to the fact that the number of the peak points in the peak point set FCPS is larger than a third quantity threshold value, for each peak point in the peak point set FCPS, obtaining a convex interval containing the peak point from sub-heartbeat waveform data in the time domain, and if the distance between the peak point and two end points of the convex interval in the time domain is smaller than a preset time threshold value, adding the peak point into a peak point set for analysis; and calculating the average value of the distances of all adjacent peak points in the peak point set in the analysis in the time domain as an average cardiac interval, and calculating the segmented time domain heart rate according to the average cardiac interval.
In the above method, before comparing the number of peak points in the peak point set TPS with a predetermined second number threshold, the method further includes: judging whether the amplitude average value of the peak points in the TPS meets a preset amplitude threshold value or not and whether the number of the peak points meets a preset first number threshold value or not; if not, in the peak point set TPS, for two peak points whose distance in the time domain is smaller than a predetermined proximity threshold, one peak point whose amplitude is smaller is filtered out, and the polynomial fitting order is set to a second predetermined order.
In the method, determining the time domain heart rate within the predetermined time period according to the extracted segmented time domain heart rate includes: and taking the extracted average value of all the segmented time domain heart rates as the time domain heart rate in the preset time length.
In the above method, determining the comprehensive heart rate in the predetermined time period according to the frequency domain heart rate and the time domain heart rate in the predetermined time period includes:
calculating the difference value of the frequency domain heart rate and the time domain heart rate in the preset time length, and judging whether the difference value is smaller than a preset heart rate difference value threshold value or not;
in response to determining that the difference is less than a predetermined heart rate difference threshold, setting the integrated heart rate for the predetermined length of time to be either the frequency domain heart rate for the predetermined length of time or the time domain heart rate for the predetermined length of time; or
In response to determining that the difference is greater than or equal to a predetermined heart rate difference threshold, calculating a ratio of the frequency domain heart rate to the time domain heart rate within the predetermined length of time, and determining whether the ratio is less than a predetermined heart rate ratio threshold;
in response to determining that the ratio is less than a predetermined heart rate ratio threshold, setting the integrated heart rate for the predetermined duration to the frequency domain heart rate for the predetermined duration; or
In response to determining that the ratio is greater than or equal to a predetermined heart rate ratio threshold, setting the integrated heart rate for the predetermined duration as a weighted average of the frequency domain heart rate for the predetermined duration and the time domain heart rate for the predetermined duration.
The method further comprises the following steps: in response to determining that the difference is less than a predetermined heart rate difference threshold, determining the magnitudes of m and n;
if m is larger than n, setting the comprehensive heart rate in the preset time length as the frequency domain heart rate in the preset time length;
and if m is less than n, setting the comprehensive heart rate in the preset time length as the time domain heart rate in the preset time length.
In the above method, the arrhythmia includes bradycardia and tachycardia, and the method further includes:
comparing the comprehensive heart rate within the preset time length with a preset heart rate over-slow threshold value and a preset heart rate over-speed threshold value respectively;
In response to the integrated heart rate over the predetermined period of time being less than a predetermined threshold of bradycardia, outputting a warning indicating bradycardia; or
In response to the integrated heart rate over the predetermined period of time being greater than a predetermined tachycardia threshold, outputting an alert indicative of tachycardia; or
And responding to the comprehensive heart rate in the preset time length being more than or equal to a preset heart rate over-slow threshold value and less than or equal to a preset heart rate over-speed threshold value, and outputting information indicating that the heart rate is normal.
There is also provided, in accordance with an embodiment of the present invention, a method for continuous monitoring of cardiac function, including
Continuously executing the heart beat function continuous monitoring method to obtain a plurality of comprehensive heart rates within a preset time;
calculating a value indicative of heart rate variability according to:
Figure BDA0002560510670000061
wherein, SDANNrepresentationA characterizing value representing heart rate variability; n represents the number of said predetermined periods, HRiRepresenting the integrated heart rate within the ith predetermined time period;
determining whether the characteristic value of the heart rate variability is outside a predetermined characteristic value range; if the heart rate variability judgment result is out of the preset characterization value range, setting the heart rate variability judgment result as heart rate variability abnormity; if the heart rate variability judgment result is within the preset characterization value range, setting the heart rate variability judgment result as normal heart rate variability; and
And outputting the heart rate variability judgment result.
There is also provided, in accordance with an embodiment of the present invention, electronic equipment including a processor and a memory, the memory storing a computer program which, when executed by the processor, implements the above-described heartbeat function monitoring method or heartbeat function duration monitoring method.
The embodiment of the invention can provide the following beneficial effects:
the heart rate and the heart rhythm are monitored in real time, the heart rate variability is monitored for a long time, and the heart rate abnormity, the arrhythmia and the heart rate variability abnormity can be timely early warned, so that the early prevention and diagnosis and treatment of cardiovascular diseases of a user are facilitated. When the heart rate is extracted from the heart beat waveform data with the preset duration, the heart rate is extracted from two angles of a frequency domain and a time domain, so that the accuracy of a monitoring result is improved. Further, the heartbeat waveform data of the preset time length is segmented to eliminate the noise data, so that the accuracy of the monitoring result is further improved.
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Example embodiments will be described in detail with reference to the accompanying drawings, which are intended to depict example embodiments and should not be construed as limiting the intended scope of the claims. The drawings are not to be considered as drawn to scale unless explicitly indicated.
Fig. 1 schematically shows a flow chart of a method of continuous monitoring of cardiac function according to an embodiment of the invention;
FIG. 2 schematically illustrates a flow diagram of a method of extracting frequency domain heart rate and heart rhythm determinations over a predetermined length of time from heart beat waveform data of a predetermined length of time, in accordance with one embodiment of the present invention;
3(a) -3(d) schematically illustrate sub-heartbeat waveform data, an upper envelope of the sub-heartbeat waveform data, an envelope of the mean filtering and normalization operations, and heartbeat distribution data in a frequency domain corresponding to the sub-heartbeat waveform data, respectively, in accordance with one embodiment of the present invention;
4(a) -4(b) schematically illustrate two different heart rate determinations extracted from two sub-heartbeat waveform data, respectively, in accordance with one embodiment of the present invention;
FIG. 5 schematically illustrates a flow diagram of a method of extracting a time-domain heart rate over a predetermined duration from heart beat waveform data of the predetermined duration, in accordance with one embodiment of the present invention;
6(a) -6(c) schematically illustrate sub-heartbeat waveform data, the Shannon energy of the sub-heartbeat waveform data, and the sub-heartbeat waveform data in the time domain, respectively, in accordance with one embodiment of the present invention;
FIG. 7 schematically illustrates a flow chart of a method of determining a combined heart rate and arrhythmia determination for a predetermined period of time, in accordance with one embodiment of the invention;
8(a) -8(c) respectively schematically illustrate three different abnormal heart rate determinations extracted from three sub-heartbeat waveform data according to an embodiment of the present invention;
FIG. 9 schematically shows a flow chart of a method of extracting heart rate and heart beat rhythmicity indicators from heart beat distribution data in a frequency domain according to one embodiment of the present invention;
fig. 10 schematically shows a flow chart of a method of extracting a heart rate from sub-heartbeat waveform data in the time domain according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail by embodiments with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Through research, people with a fast heart rate (namely, the number of heart beats per minute), particularly people with a fast resting heart rate, have a significantly higher probability of cardiovascular diseases than people with a slow heart rate; whereas arrhythmia (i.e., heart beat rhythm/rhythm, which may be described as the stability of the interval duration of two adjacent heart beats) is a major cause of sudden cardiac death. In addition, heart rate variability (i.e., variability in beat-to-beat cycle variability) has been increasingly used in the prognosis of the general population as well as in cardiovascular patients as an independent risk factor for arrhythmia and post-myocardial infarction death. In view of the above, the heart rate, heart rhythm and heart rate variability are used as the human heart beat function indexes, and the human heart beat function indexes are continuously monitored and early warned.
According to one embodiment of the present invention, a method for continuous monitoring of cardiac function is provided. For example, a piezoelectric film sensor (e.g., PVDF piezoelectric film sensor) is first placed under a mattress at a position just on the chest of a human body, and pressure data of the human body in a lying state due to body vibration (mainly cardiac vibration) is continuously collected using the piezoelectric film sensor. It should be understood that other devices and methods known in the art may be utilized to obtain such data. Fig. 1 schematically shows a flowchart of a method for continuously monitoring cardiac function according to an embodiment of the present invention, wherein the method for continuously monitoring cardiac function is used for continuously monitoring cardiac function indexes of a human body in a lying state (for example, in a sleep state), and comprises steps S11-S15, and the steps of the method for continuously monitoring cardiac function are described below with reference to fig. 1.
And S11, acquiring heartbeat waveform data of a preset time length. Wherein the heartbeat waveform data represents a heart beat amplitude over time, and wherein an amplitude of the heartbeat waveform data at a point in time corresponds to a beat amplitude of the heart at the point in time.
According to one embodiment of the invention, acquiring heartbeat waveform data for a predetermined length of time includes the sub-steps of:
And S111, collecting body vibration data of a preset time length, wherein the preset time length is preferably 30 seconds.
After the pressure data of the preset time length is collected, the pressure data is filtered and amplified, and therefore body vibration data of the human body in the lying state of the preset time length are obtained.
S112, preprocessing the body vibration data with the preset time length to obtain heartbeat waveform data with the preset time length, wherein the method comprises the following steps:
s1121) determining whether the type of the body vibration data of the predetermined length of time conforms to the predetermined data type, that is, determining whether the body vibration data of the predetermined length of time is sign data of a predetermined length of time (for example, 30 seconds), for example, detecting whether the data is in a predetermined waveform shape and the length satisfies 30 seconds; if yes, go to S1122); otherwise, returning to step S111) to collect body vibration data for the next predetermined period of time, while outputting a warning that the data type is illegal.
S1122) detecting whether power frequency noise caused by environmental reasons exists in the body vibration data of the preset time length; and if the power frequency noise is detected to exist, performing band elimination filtering on the body vibration data with preset duration to filter the power frequency noise.
S1123) filtering processing of a preset heart rate frequency band is carried out on the body vibration data with the preset duration by using a finite length unit impulse response (FIR) filter, and therefore heart beat waveform data with the preset duration is obtained.
And S12, extracting the frequency domain heart rate, the heart rhythm judgment result and the time domain heart rate within the preset time length from the heart beat waveform data within the preset time length, and outputting the heart rhythm judgment result within the preset time length.
In summary, step S12 includes: performing frequency domain analysis on heart beat waveform data with preset duration to obtain frequency domain heart rate and heart rate judgment results within the preset duration, and outputting the heart rate judgment results within the preset duration; and carrying out time domain analysis on the heartbeat waveform data with the preset time length to obtain the time domain heart rate within the preset time length.
It should be understood that the frequency domain analysis and the time domain analysis of the heart beat waveform data of a predetermined duration may be performed in parallel, or sequentially in any order, as described separately below:
frequency domain analysis of heart beat waveform data of predetermined duration
Fig. 2 schematically shows a flow chart of a method for extracting frequency domain heart rate and heart rhythm determination results in a predetermined time period from heart beat waveform data in the predetermined time period, according to an embodiment of the invention, and the method comprises the following sub-steps:
s1211) dividing heartbeat waveform data of a preset time length into m (m is larger than or equal to 1) segments of sub-heartbeat waveform data. Taking the heartbeat waveform data with the predetermined time length of 30 seconds as an example, m is preferably 3, so that 3 pieces of sub-heartbeat waveform data with 10 seconds respectively are obtained, and each piece of sub-heartbeat waveform data can be seen in fig. 3 (a). In fig. 3(a), the abscissa represents time (its unit is second), and the ordinate represents amplitude (its unit is microvolts) of the sub-heartbeat waveform data.
S1212) excluding sub-heartbeat waveform data having poor stability, that is, excluding sub-heartbeat waveform data having a standard deviation not within a predetermined heartbeat stability range, from the m pieces of sub-heartbeat waveform data. It should be understood that the standard deviation is used to measure the difference between a series of data and the average, and in this embodiment, the standard deviation is used to measure the stability of each segment of the waveform data of the sub-heart beat.
Specifically, for each of the m pieces of sub-heartbeat waveform data, the following operations are performed:
calculate the standard deviation of the waveform data for this segment of the sub-beat, see the following equation:
Figure BDA0002560510670000091
wherein std ismRepresents the standard deviation of the waveform data of the sub-heartbeat, n represents the length of the waveform data of the sub-heartbeat on the abscissa, datam[i]A value (i.e., amplitude) of the ordinate corresponding to the waveform data of the sub-heartbeat when the abscissa is i.
Comparing the calculated standard deviation with a predetermined range of heart beat stabilities; if the calculated standard deviation exceeds the predetermined heartbeat stability range (representing the poor stability of the segment of the sub-heartbeat waveform data), the segment of the sub-heartbeat waveform data is excluded from the m segments of the sub-heartbeat waveform data.
For the excluded sub-beat waveform data, it is set such that the corresponding segmented frequency domain heart rate and beat rhythmicity indicators are not extracted. For example, the segmented frequency domain heart rate and the heart beat rhythmicity index of the excluded sub-heartbeat waveform data are both set to "-1" to indicate that the segmented frequency domain heart rate and the heart beat rhythmicity index are not extracted from the corresponding sub-heartbeat waveform data.
It should be noted that the segmented frequency domain heart rate of the sub-heartbeat waveform data refers to a heart rate obtained by performing frequency domain analysis on the sub-heartbeat waveform data. The heartbeat rhythmicity index of the sub-heartbeat waveform data refers to an index obtained by frequency domain analysis of the sub-heartbeat waveform data and used for showing whether or not the heartbeat rhythm is abnormal or abnormal degree, and besides the condition that the heartbeat rhythmicity index is not extracted is represented by "-1", the heartbeat rhythmicity index can have a value in [0, k ], wherein 0 represents no heartbeat rhythm abnormality, more than 0 represents heartbeat rhythm abnormality, and the larger the heartbeat rhythmicity index is, the more serious the degree of the heartbeat rhythm abnormality is. According to other embodiments, other means, such as letters or the like, may be used to indicate the severity of the heart beat rhythm disorder.
S1213) converts each of the remaining pieces of sub-heart beat waveform data into heart beat distribution data on the frequency domain.
Specifically, for each remaining segment of sub-heartbeat waveform data, the following operations are performed:
acquiring an envelope of the segment of sub-heartbeat waveform data. Wherein the acquired may be an upper envelope or a lower envelope of the segment of sub-heartbeat waveform data. Preferably, an upper envelope of the segment of sub-heartbeat waveform data is acquired, as shown in fig. 3 (b).
The envelope of the acquired waveform data of the segment of the sub-heartbeat is preprocessed, and the waveform data shown in fig. 3(c) is obtained. The preprocessing process may include mean filtering and normalization, and the mean filtering may make the waveform data smoother.
Discrete fourier transform (it will be understood by those skilled in the art that other transform methods such as z-transform may be used) is performed on the preprocessed waveform data to obtain heart beat distribution data in the frequency domain corresponding to the segment of sub-heart beat waveform data, as shown in fig. 3 (d). As shown in fig. 3(d), the heart beat distribution data in the frequency domain shows the intensity of the frequency domain distribution as a function of frequency.
S1214) extracting corresponding heart rate and heart beat rhythmicity indexes from the heart beat distribution data on each segment of frequency domain, and respectively using the heart rate and heart beat rhythmicity indexes as the segmented frequency domain heart rate and heart beat rhythmicity indexes of the corresponding remaining waveform data of each segment of sub heart beat.
According to one embodiment of the invention, for the heartbeat distribution data on each segment of the frequency domain, the frequency of the peak point with the largest amplitude is extracted as the heart rate of the heartbeat distribution data on the segment of the frequency domain, and the heart rate is taken as the segmented frequency domain heart rate of the corresponding sub-heartbeat waveform data. According to one embodiment of the invention, searching a peak point set with the amplitude larger than a predetermined frequency domain threshold value from the heart beat distribution data on each frequency domain; if only one peak point is in the peak point set, setting the heartbeat rhythmicity index of the corresponding sub-heartbeat waveform data to be 0, wherein the heartbeat rhythmicity index is used for representing no heartbeat rhythm abnormality; if the peak point is concentrated with a plurality of peak points, the heartbeat rhythm index of the corresponding sub-heartbeat waveform data is set as the quotient of the maximum amplitude of the peak point in the peak point concentration and the preset frequency domain width, and the quotient is used for representing the abnormal degree of the heartbeat rhythm.
It should be understood that the above is only an exemplary method for extracting the corresponding heart rate and heart beat rhythmicity index from the heart beat distribution data in each frequency domain, and other existing frequency domain analysis methods may be utilized to extract the heart rate and heart beat rhythmicity index from the heart beat distribution data in each frequency domain.
S1215) determining the frequency domain heart rate within the predetermined time length according to all the extracted segmented frequency domain heart rates, determining the heart rhythm determination result within the predetermined time length according to all the extracted heart rhythm indexes, and outputting the heart rhythm determination result within the predetermined time length, wherein the step S1215) includes:
and taking the average value of all the extracted segmented frequency-domain heart rates as the frequency-domain heart rate in the preset time length.
The number of the extracted heart beat rhythmicity indexes larger than the predetermined heart beat rhythmicity threshold is counted, and the ratio of the counted number to the number of the extracted heart beat rhythmicity indexes is calculated. If the proportion exceeds a preset proportion, determining that arrhythmia occurs in the heartbeat waveform data in the preset time length, and setting the arrhythmia judgment result in the preset time length as 'arrhythmia', otherwise, setting the arrhythmia judgment result in the preset time length as 'normal rhythm'. Fig. 4(a) and 4(b) respectively show two different heart rhythm determination results obtained from two sub-heartbeat waveform data according to the method provided by the present embodiment.
If the judgment result of the heart rhythm in the preset time length is 'arrhythmia', a corresponding warning is sent out; and if the heart rhythm judgment result in the preset time length is 'normal heart rhythm', outputting the heart rhythm judgment result in the preset time length.
Time domain analysis of heart beat waveform data of predetermined duration
Fig. 5 schematically shows a flowchart of a method for extracting a time-domain heart rate within a predetermined duration from heart beat waveform data of the predetermined duration, the method comprising:
s1221) dividing the heartbeat waveform data of a preset time length into n (n is larger than or equal to 1) segments of sub-heartbeat waveform data. Taking the heartbeat waveform data with the predetermined time length of 30 seconds as an example, n is preferably 5, so as to obtain 5 sub-heartbeat waveform data with 6 seconds respectively, and each piece of sub-heartbeat waveform data can be seen in fig. 6 (a).
S1222) for each of the n pieces of sub-heart beat waveform data, calculating shannon energy of the piece of sub-heart beat waveform data (as shown in fig. 6(b), and calculating an autocorrelation curve from the shannon energy, resulting in sub-heart beat waveform data in a time domain corresponding to the piece of sub-heart beat waveform data (as shown in fig. 6 (c)).
According to one embodiment of the invention, the shannon energy of the sub-heartbeat waveform data is calculated using the following formula:
data1＝data0/[max(data0)-min(data0)](2)
data2＝data1-average(data1) (3)
data3＝-data22*log(data22) (4)
Wherein data0 represents sub-heartbeat waveform data; max (data0) represents the maximum magnitude of data 0; min (data0) represents the minimum amplitude of data 0; data1 and data2 are intermediate data, and average (data1) represents the average value of the amplitude of data 1; data3 represents the shannon energy of the sub-heartbeat waveform data.
In addition, an autocorrelation curve is calculated according to the following formula, and sub-heartbeat waveform data in a time domain is obtained:
Figure BDA0002560510670000121
wherein, corrsiRepresents the ith discrete data point on the autocorrelation curve; l represents the total number of data contained in the Shannon energy data3, i is more than 0 and less than or equal to l, and j is more than or equal to 1 and less than or equal to l-i + 1; data3jA jth discrete data point representing shannon energy data 3; data3j+i-1The j + i-1 th discrete data point representing the shannon energy data 3.
And S1223) extracting a corresponding heart rate from the sub-heartbeat waveform data on each segment of the time domain to serve as a segmented time domain heart rate of the corresponding sub-heartbeat waveform data.
It should be appreciated that existing time domain analysis methods may be utilized to extract the heart rate in the sub-heartbeat waveform data over each segment of the time domain. For example, the heart rate in the sub-heartbeat waveform data of each time domain is extracted by using the existing electrocardiogram analysis method as the segmented time domain heart rate of the corresponding sub-heartbeat waveform data of each segment.
S1224) determining the time domain heart rate within the preset time length according to all the extracted segmented time domain heart rates. For example, the average value of all the extracted segmented time domain heart rates is used as the time domain heart rate within a preset time length.
As described above, whether frequency domain analysis or time domain analysis is performed, the heart beat waveform data of a predetermined time length may be first segmented, and then the segmented sub-heart beat waveform data may be subjected to frequency domain and time domain analysis. The segmentation processing can eliminate some noise data, so that more accurate frequency domain heart rate, heart rhythm judgment result and time domain heart rate are extracted, and the accuracy of the monitoring result is further improved.
And S13, determining a comprehensive heart rate within a preset time length according to the frequency domain heart rate and the time domain heart rate within the preset time length, determining an abnormal heart rate judgment result according to the comprehensive heart rate within the preset time length, and outputting the abnormal heart rate judgment result. Referring to fig. 7, step S13 includes the following sub-steps:
s131) calculating a difference value between the heart rate of the frequency domain and the heart rate of the time domain within a preset time length, and comparing the difference value with a preset heart rate difference value threshold; if the former is smaller than the latter, go on to step S132); otherwise, execution continues with step S133).
S132) selecting the frequency domain heart rate within the preset time length or the time domain heart rate within the preset time length as the comprehensive heart rate within the preset time length, and continuing to execute the step S136);
in one embodiment, the frequency domain heart rate over a predetermined length of time or the time domain heart rate over a predetermined length of time may be randomly selected as the integrated heart rate over the predetermined length of time.
Further, referring to step S12, when frequency domain and time domain analysis is performed on the heart beat waveform data of the predetermined time length, the heart beat waveform data of the predetermined time length is divided into m pieces and n pieces of sub-heart beat waveform data, respectively. If m is not equal to n, in step S132), the heart rate within the predetermined time period (possibly the frequency domain heart rate or the time domain heart rate) obtained when the number of segments is large is taken as the integrated heart rate within the predetermined time period. For example, as described above, if m is 3 and n is 5, then the time domain heart rate within the predetermined time period is taken as the integrated heart rate within the predetermined time period. The unit of the sub-heartbeat waveform data when the segmented time domain heart rate is extracted is 6 seconds, and the unit of the sub-heartbeat waveform data when the segmented frequency domain heart rate is extracted is 10 seconds, so that the granularity of the sub-heartbeat waveform data is smaller, and the extracted heart rate is more accurate.
S133) calculating the ratio of the frequency domain heart rate to the time domain heart rate within a preset time length, and comparing the ratio with a preset heart rate ratio threshold value; if the former is smaller than the latter, continue to execute step S134); otherwise, execution continues with step S135).
S134) selects the frequency domain heart rate within the predetermined time period as the integrated heart rate within the predetermined time period, and proceeds to step S136).
In the time domain, the calculation of heart beat waveform data with the first heart sound and the second heart sound close to each other has limitation, so that the extracted frequency domain heart rate is more accurate, and the frequency domain heart rate is used as the comprehensive heart rate in the preset duration to enable the comprehensive heart rate to be more accurate.
S135) carrying out weighted average on the frequency domain heart rate and the time domain heart rate within the preset time length according to the preset weight of the frequency domain heart rate and the time domain heart rate, and taking the weighted average result as the comprehensive heart rate within the preset time length.
S136) determining an abnormal heart rate judgment result according to the comprehensive heart rate within the preset time length, and outputting the abnormal heart rate judgment result.
Comparing the integrated heart rate over a predetermined time period to a predetermined heart rate bradycardia threshold:
and if the comprehensive heart rate within the preset time is less than the preset heart rate bradycardia threshold, setting the heart rate abnormity judgment result as 'bradycardia', and outputting a warning indicating 'bradycardia'.
If the comprehensive heart rate in the preset time length is larger than or equal to the preset heart rate over-slow threshold value, comparing the comprehensive heart rate in the preset time length with the preset heart rate over-speed threshold value:
And if the comprehensive heart rate in the preset time is greater than the preset heart rate overspeed threshold value, setting the abnormal heart rate judgment result as heart rate overspeed and outputting a warning indicating the heart rate overspeed.
Otherwise, the abnormal heart rate determination result is set to "normal heart rate", and information indicating "normal heart rate" is output, see fig. 8(a) -8 (c).
S14, judging whether the continuous monitoring on the heartbeat function is finished or not; if not, returning to step S11; otherwise, execution continues with step S15.
For example, if the piezoelectric film sensor is monitored to be turned off, it is determined that the continuous monitoring of the cardiac function is finished.
Step S15, determining a representative value of heart rate variability according to the comprehensive heart rate in all the preset time periods at night, determining a heart rate variability judgment result according to the representative value of the heart rate variability, and outputting the heart rate variability judgment result, wherein the step S15 comprises the following steps:
s151) calculating a value indicative of heart rate variability according to:
Figure BDA0002560510670000141
wherein, SDANNrepresentationA characterizing value representing heart rate variability; n represents the number of all the predetermined time periods, HR, included all nightiRepresenting the integrated heart rate over the ith predetermined period.
It will be appreciated that based on the segmented sub-heartbeat waveform data, the extracted integrated heart rate approximates the human body's real-time heart rate. The representative value of the heart rate variability of the whole night is calculated by utilizing the comprehensive heart rate to represent the actual heart rate variability of the user, and the monitoring result is accurate.
S152) determining whether the characteristic value of the heart rate variability is outside a predetermined characteristic value range; if the heart rate variability is out of the preset characterization value range, setting the heart rate variability judgment result as heart rate variability abnormity, and outputting an alarm indicating the heart rate variability abnormity; if the heart rate variability is within the preset characterization value range, the heart rate variability judgment result is set to be 'normal heart rate variability', and information indicating 'normal heart rate variability' is output.
According to an embodiment of the present invention, different ranges of the token values can be preset for users of different age groups, as follows:
18-29 years old: 169 +/-41
30-49 years old: 148 +/-32
50-69 years old: 121 +/-29
The embodiment realizes real-time monitoring of heart rate and heart rhythm and long-term monitoring of heart rate variability, can give out timely early warning to heart rate abnormity, arrhythmia and heart rate variability abnormity, and helps a user to perform early prevention and diagnosis and treatment on cardiovascular diseases. When the heart rate is extracted from the heart beat waveform data with the preset duration, the heart rate is extracted from two angles of a frequency domain and a time domain, so that the accuracy of a monitoring result is improved. Further, the heartbeat waveform data of the preset time length is segmented to eliminate the noise data, so that the accuracy of the monitoring result is further improved.
According to an embodiment of the invention, in order to further improve the accuracy of the monitoring result, a method for extracting heart rate and heart beat rhythmicity indexes from heart beat distribution data on a frequency domain is also provided. Referring to fig. 9, the method comprises the following steps:
step S901, judging whether low-frequency noise exists in heart beat distribution data on a frequency domain; if yes, continuing to execute step S910 to set the heart beat distribution data in the frequency domain as that the corresponding heart rate and heart beat rhythmicity index are not extracted; otherwise, the step S902 is continued.
It should be understood that in the heart beat frequency domain, the low frequency region generally refers to heart rates in the range of 0-12 Hz, the medium frequency region generally refers to heart rates in the range of 12-60 Hz, and the high frequency region generally refers to heart rates greater than 60 Hz. According to one embodiment of the invention, determining whether low frequency noise is present in heart beat distribution data in the frequency domain comprises:
the maximum amplitude of the heart beat distribution data in the frequency domain in a low frequency region (i.e. 12-60 Hz) is compared with the maximum amplitude in a preset heart rate frequency domain region (as shown in FIG. 3(d), the amplitude indicates the intensity of the frequency domain distribution). The preset heart rate frequency domain area is 30-120 Hz and indicates the heart rate range of a normal person. If the former is larger than the latter, it indicates that the heart beat distribution data in the frequency domain has low-frequency noise (i.e. the corresponding sub-heart beat waveform data has low-frequency noise); otherwise, it indicates that the heart beat distribution data in the frequency domain has no low-frequency noise.
S902, searching all peak points of which the amplitudes are larger than a preset frequency domain threshold value in a preset heart rate frequency domain area (namely 30-120 Hz) of heart beat distribution data on a frequency domain to obtain a peak point set FPS.
Wherein the predetermined frequency domain threshold may be obtained from the maximum amplitude and the average amplitude of the heart beat distribution data in the frequency domain, for example, see the following formula:
Figure BDA0002560510670000151
wherein thr represents a predetermined frequency domain threshold, N represents the number of sampling points of the heart beat distribution data in the frequency domain, and data [ i ] represents the amplitude of the ith sampling point in the heart beat distribution data in the frequency domain.
Step S903, judging whether the peak point set FPS only has one peak point; if there is only one peak point, that is, the heart beat distribution data in the frequency domain has only one significant frequency in the preset heart rate frequency domain area, that is, there is no abnormal heart beat rhythm, step S911 is continuously executed to use the significant frequency as the heart rate of the heart beat distribution data in the frequency domain and set the heart beat rhythmicity index to "0"; otherwise (i.e. including multiple peak points), the process continues to step S904.
And S904, based on the principle that the class interval is larger than a preset class interval threshold, carrying out K-Means data clustering by taking the frequencies of all peak points in the peak point set FPS as source data.
Specifically, K-Means data clustering is performed by taking the frequency of all peak points in the peak point set FPS as source data. Wherein, if two centroids are obtained after clustering, judging whether the values of the two centroids have a relation of approximately 2 times (preferably, judging whether the larger value divided by the smaller value in the two centroids is in the range of 1.7 to 2.3); if there is no approximately 2-fold relationship (e.g., the larger of the two centroids divided by the smaller is not in the range of 1.7 to 2.3), then the clustering result is considered to be one class (i.e., the clustering result is invalid and all source data belongs to one class).
It will be appreciated by those skilled in the art that other conventional clustering methods may be used for data clustering in addition to K-Means.
Step S905, judging whether the clustering result is of one type; if the clustering result is a type, that is, the heart beat distribution data in the frequency domain has only the distribution characteristic of the first heart sound in the preset heart rate frequency domain area, and the type includes a plurality of close frequencies, that is, the heart beat distribution data in the frequency domain has a plurality of significant frequencies in the preset heart rate frequency domain area (that is, there is heart beat rhythm abnormality), continuing to execute step S912 to extract the heart rate and heart beat rhythm index; if the clustering result is more than one type, the process continues to step S906.
S906, judging whether the clustering result is more than two types; if the clustering result is more than two types, it indicates that significant noise generated by body movement and other factors exists in the heart beat distribution data in the frequency domain in the preset heart beat frequency domain area, and therefore the heart beat rhythmicity index cannot be accurately extracted from the heart beat distribution data in the frequency domain, step S913 is continuously executed to extract the heart beat and set the heart beat distribution data in the frequency domain as the heart beat rhythmicity index which is not extracted; otherwise (i.e. the clustering result is two types), the step S907 continues to be executed.
Step S907, responding to the clustering result of two classes, judging whether the proportion of the clustering kernels of the two classes (namely, the average value of the frequencies of all peak points in each class) meets a quadratic relation within a preset error range; if the heart rate distribution data does not meet the preset heart rate frequency domain, the heart rate distribution data on the frequency domain does not have obvious distribution characteristics of the first heart sound and the second heart sound in the preset heart rate frequency domain area, but has a plurality of significant frequencies (namely, heart rate rhythm abnormality exists), executing step S912 to extract heart rate and heart rate rhythmicity indexes; otherwise, step S908 is performed.
Step S908, in response to the fact that the proportion of the clustering cores of the two classes meets the quadratic relation in a preset error range (the distribution characteristics of the first heart sound and the second heart sound existing in the heart beat distribution data in the frequency domain in the preset heart rate frequency domain region at the same time), the class with the smaller clustering core of the two classes is selected as a target class.
S909, judging whether only one peak point exists in the target class; if the target class has only one peak point, which indicates that the heartbeat distribution data in the frequency domain has a unique significant frequency in the preset heart rate frequency domain area, continuing to execute step S914; otherwise (there are multiple peak points in the target class), it indicates that the heartbeat distribution data in the frequency domain has multiple significant frequencies in the preset heart rate frequency domain area (i.e. there is a heartbeat rhythm abnormality), then step S915 is continuously executed.
And S910, setting the heart beat distribution data in the frequency domain as the heart beat and heart beat rhythmicity indexes which are not extracted, and ending the method. Specifically, the heart rate and the heart beat rhythmicity index of the heart beat distribution data on the frequency domain are both set to "-1". Thus, when low-frequency noise exists in the heart beat distribution data in the frequency domain, the heart beat distribution data cannot be used for calculating the heart rate and the heart rhythm judgment result in the frequency domain within the preset time length, and therefore the monitoring result is more accurate.
Step S911, responding to only one peak point in the peak point set FPS, taking the frequency corresponding to the only one peak point in the peak point set FPS as the heart rate of the heart beat distribution data on the frequency domain, and setting the heart beat rhythmicity index of the heart beat distribution data on the frequency domain to be 0, namely representing no heart beat rhythm abnormality; the method ends.
Step S912, taking the frequency corresponding to the peak point with the maximum amplitude in the peak point set FPS as the heart rate of heart beat distribution data in the frequency domain; calculating a heartbeat rhythmicity index of heartbeat distribution data on a frequency domain according to formula (8); the method ends.
Figure BDA0002560510670000171
Wherein cri represents a heartbeat rhythmicity index of heartbeat distribution data on a frequency domain;
Figure BDA0002560510670000172
representing the maximum amplitude of the peak points in the set of peak points FPS; MB represents the maximum frequency bandwidth of all intervals in which the amplitude of the heart beat distribution data in the frequency domain is continuously larger than the predetermined frequency domain threshold thr in the preset heart rate frequency domain area. As described above, the larger the heart beat rhythmicity index is, the more serious the degree of representing the heart beat rhythm abnormality is.
S913, taking the frequency corresponding to the peak point with the maximum amplitude in the peak point set FPS as the heart rate of the heart beat distribution data in the frequency domain; setting the heart beat rhythmicity index of the heart beat distribution data on the frequency domain to be-1 to show that the corresponding heart beat rhythmicity index is not extracted from the heart beat distribution data on the frequency domain; the method ends.
S914, setting the heart rate of the heart beat distribution data on the frequency domain as the frequency corresponding to the peak point in the target class; setting a heartbeat rhythmicity index of heartbeat distribution data on a frequency domain to be "0" to represent "no heartbeat rhythm abnormality"; the method ends.
S915, setting the heart rate of heart beat distribution data on a frequency domain as the frequency corresponding to the peak point with the maximum amplitude in the target class; calculating a heartbeat rhythmicity index of heartbeat distribution data on a frequency domain according to formula (9):
Figure BDA0002560510670000181
cri denotes a heart beat rhythmicity index of heart beat distribution data in a frequency domain; tar represents the target class and is used as the target class,
Figure BDA0002560510670000182
represents the maximum amplitude of the peak point in the target class Tar; MB represents the maximum frequency bandwidth of all intervals corresponding to the target class, in which the amplitude of the heart beat distribution data in the frequency domain is continuously greater than the predetermined frequency domain threshold thr, in the preset heart rate frequency domain area.
According to an embodiment of the invention, in order to further improve the accuracy of the monitoring result, a method for extracting the heart rate from the sub-heartbeat waveform data in the time domain is also provided. Referring to fig. 10, the method comprises the following steps:
s1001, searching all peak points with amplitudes larger than a preset time domain threshold value in sub-heartbeat waveform data in a time domain to obtain a peak point set TPS. Wherein the predetermined time domain threshold is the sum of the mean and standard deviation of the waveform data of the sub-heartbeat in the time domain.
S1002, judging whether noise interference exists in sub-heartbeat waveform data in a time domain; if no noise interference exists, continuing to execute step S1003; if there is noise interference, step S1004 is executed to perform noise filtering.
Judging whether noise interference exists in the sub-heartbeat waveform data in the time domain comprises the following steps: judging whether the amplitude average value of the peak points in the TPS meets a preset amplitude threshold value or not and whether the number of the peak points meets a preset first number threshold value or not; if the sub-heartbeat waveform data meets the requirement, the sub-heartbeat waveform data in the time domain has no noise interference; otherwise, it indicates that the sub-heartbeat waveform data in the time domain has noise interference (i.e. a false peak exists).
And S1003, setting the polynomial fitting order as a first preset order, and continuing to execute the step S1005. Wherein the first predetermined order is a constant set according to an empirical value.
Step S1004, in the peak point set TPS, for two peak points of which the distance (abscissa distance) in the time domain is smaller than a preset adjacent threshold value, filtering out one peak point with a smaller amplitude value, namely filtering out a pseudo peak point; and setting a polynomial fitting order to a second predetermined order, wherein the second predetermined order is a constant different from the first predetermined order set according to an empirical value.
S1005, comparing the number of peak points in the peak point set TPS with a preset second number threshold; if the former is less than or equal to the latter, it indicates that the sub-heart beat waveform data in the time domain does not have a complete cardiac interval (the cardiac interval indicates the interval duration between two heartbeats, see fig. 6(c)), step S1011 is continuously executed to set the heart rate of the sub-heart beat waveform data in the time domain to "-1"; otherwise (i.e., the former is larger than the latter), the process continues to step S1006.
And S1006, performing polynomial high-order curve fitting on the sub-heartbeat waveform data on the time domain by using a polynomial fitting order to obtain a fitting curve.
For example, a least squares method is used to perform polynomial higher order curve fitting on the sub-heartbeat waveform data in the time domain, see the following formula:
Figure BDA0002560510670000191
wherein f (x; w) represents a polynomial high-order fitting curve, M represents a polynomial fitting order, and wiRepresenting polynomial coefficients, xiRepresenting an argument.
Step S1007, searching all peak points with the amplitude values larger than the preset threshold value of the fitting curve on the fitting curve to obtain a peak point set FCPS. Wherein the predetermined threshold value of the fitted curve is the sum of the mean and the standard deviation of the fitted curve.
Step S1008, comparing the number of peak points in the peak point set FCPS with a preset third number threshold; if the former is less than or equal to the latter, it indicates that the sub-heartbeat waveform data in the time domain does not have a complete cardiac interval, then step S1011 is continuously executed; otherwise, execution continues with step S1009.
Step S1009, for each peak point in the peak point set FCPS, acquiring a convex interval (or a convex interval corresponding to the peak point) containing the peak point from the sub-heartbeat waveform data on the time domain, wherein the convex interval takes the first inflection point on the left and right of the peak point as a boundary; if the distances between the peak point and the two end points of the convex section in the time domain (taking fig. 6(c) as an example, that is, the distances between the peak point and the two end points of the convex section in the abscissa) are both smaller than a predetermined time threshold, the peak point is added to the peak point set for analysis.
Step S1010, calculating the average value of the distances of all adjacent peak points in the peak point set for analysis in the time domain (namely the distances on the abscissa), and taking the average value as an average cardiac interval; and calculating a heart rate of the sub-heartbeat waveform data in the time domain from the average cardiac interval; the method ends.
Therein, the heart rate of the sub-heartbeat waveform data in the time domain is calculated, for example, with reference to the following equation:
heartRate＝60/NN (11)
where heartbeat represents the heart rate (in beats per minute as described above) of the sub-beat waveform data in the time domain, and NN represents the average cardiac interval.
And S1011, setting the heart rate of the waveform data of the sub-heartbeat in the time domain to be '1', namely setting the waveform data of the sub-heartbeat in the time domain as the heart rate which is not extracted, and ending the method.
It should be noted that some exemplary methods are depicted as flowcharts. Although a flowchart may describe the operations as being performed serially, it can be appreciated that many of the operations can be performed in parallel, concurrently, or with synchronization. In addition, the order of the operations may be rearranged. A process may terminate when an operation is completed, but may have additional steps not included in the figure or embodiment.
The above-described methods may be implemented by hardware, software, firmware, middleware, pseudocode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware, or pseudo code, the program code or code segments to perform the tasks may be stored in a computer readable medium such as a storage medium, and a processor may perform the tasks.
It should be appreciated that the software-implemented exemplary embodiment is typically encoded on some form of program storage medium or implemented over some type of transmission medium. The program storage medium may be any non-transitory storage medium such as a magnetic disk (e.g., a floppy disk or a hard drive) or an optical disk (e.g., a compact disk read only memory or "CD ROM"), and may be read only or random access. Similarly, the transmission medium may be twisted wire pairs, coaxial cable, optical fiber, or some other suitable transmission medium known to the art.
Although the present invention has been described by way of preferred embodiments, the present invention is not limited to the embodiments described herein, and various changes and modifications may be made without departing from the scope of the present invention.

Claims (12)

1. A method of cardiac function monitoring, comprising:
acquiring heartbeat waveform data of a predetermined duration, wherein the heartbeat waveform data represents heart beat amplitude over time;
respectively carrying out frequency domain analysis and time domain analysis on the heartbeat waveform data with the preset duration to obtain a frequency domain heart rate and a time domain heart rate within the preset duration; and
And determining the comprehensive heart rate in the preset time length according to the frequency domain heart rate and the time domain heart rate in the preset time length so as to determine whether abnormal heart rate exists in the preset time length.
2. The method of claim 1, wherein performing frequency domain analysis and time domain analysis, respectively, on the heart beat waveform data of the predetermined duration comprises:
dividing the heartbeat waveform data of the preset duration into m segments of sub-heartbeat waveform data, converting each segment of sub-heartbeat waveform data into heartbeat distribution data on a frequency domain, extracting segmented frequency domain heart rate from the heartbeat distribution data on each segment of frequency domain, and determining the frequency domain heart rate in the preset duration according to the extracted segmented frequency domain heart rate; and
dividing the heartbeat waveform data with the preset duration into n segments of sub-heartbeat waveform data, converting each segment of sub-heartbeat waveform data into sub-heartbeat waveform data on a time domain, extracting a segmented time domain heart rate from each segment of sub-heartbeat waveform data on the time domain, and determining the time domain heart rate within the preset duration according to the extracted segmented time domain heart rate; wherein m and n are integers of 1 or more.
3. The method of claim 2, further comprising:
Sub-heartbeat waveform data having a standard deviation not within a predetermined heartbeat stability range is excluded from the m pieces of sub-heartbeat waveform data.
4. The method of claim 2, wherein converting each segment of sub-heartbeat waveform data into heartbeat distribution data in the frequency domain comprises:
acquiring an envelope curve of the segment of sub-heartbeat waveform data;
carrying out mean value filtering and normalization operation on the envelope curve; and
carrying out Fourier transform on the envelope subjected to mean filtering and normalization operation to obtain heart beat distribution data on a frequency domain; and
extracting segmented frequency domain heart rates from the heart beat distribution data on each segment of the frequency domain comprises:
searching peak points with the amplitude larger than a preset frequency domain threshold value in a preset heart rate frequency domain area of heart beat distribution data on the section of frequency domain to obtain a peak point set FPS, and determining the number of the peak points in the peak point set FPS;
responding to the fact that a peak point is contained in the peak point set FPS, and setting the heart rate of the segmented frequency domain as the frequency of the peak point; or
Responding to the fact that the peak point set FPS comprises a plurality of peak points, clustering all the peak points in the peak point set FPS based on frequency, and determining the number of the obtained classes;
Responding to the clustering result of one type or more than two types, and setting the heart rate of the segmented frequency domain as the frequency of the peak point with the maximum amplitude in the peak point set FPS; or
Responding to the clustering result of two types, and judging whether the proportion of the clustering kernels of the two types meets a quadratic relation within a preset error range; if not, setting the heart rate of the segmented frequency domain as the frequency of the peak point with the maximum amplitude in the peak point set FPS; if so, selecting the class with the smaller clustering kernel in the two classes as a target class, and determining the number of peak points in the target class;
in response to the target class containing a peak point, setting the segmented frequency domain heart rate to the frequency of the peak point; or
And in response to the target class containing a plurality of peak points, setting the segmented frequency domain heart rate to the frequency of the peak point with the maximum amplitude in the target class.
5. The method according to claim 4, further comprising extracting a heart beat rhythmicity index from the heart beat distribution data on each frequency domain, determining a heart rhythm determination result within the predetermined period of time from the extracted heart beat rhythmicity index, and outputting the heart rhythm determination result within the predetermined period of time;
Wherein, extracting the heart beat rhythmicity index from the heart beat distribution data on each section of frequency domain comprises the following steps:
responding to the fact that the peak point set FPS comprises a peak point or responding to the fact that the target class comprises a peak point, and setting the heartbeat rhythmicity index to indicate abnormal heartbeat rhythm;
setting a heartbeat rhythmicity index according to the following formula in response to the condition that the peak point set FPS comprises a plurality of peak points and the clustering result is of one type, or in response to the condition that the peak point set FPS comprises a plurality of peak points, the clustering result is of two types, and the proportion of the clustering kernels of the two types does not meet the quadratic relation in a preset error range:
Figure FDA0002560510660000021
wherein cri represents a heart beat rhythmicity index;
Figure FDA0002560510660000022
representing a maximum amplitude of a peak point in the set of peak points FPS; MB represents the maximum frequency bandwidth of all intervals of the heartbeat distribution data on the section of frequency domain, the amplitude of which is continuously larger than a preset frequency domain threshold value, in the preset heart rate frequency domain area;
responding to the condition that the peak point set FPS comprises a plurality of peak points and the clustering result is more than two types, setting heartbeat distribution data on the frequency domain section as a heartbeat rhythmicity index which is not extracted;
in response to the target class containing a plurality of peak points, setting a heartbeat rhythmicity indicator according to:
Figure FDA0002560510660000031
Wherein cri denotes a cardiac rhythmicity index; tar represents the target class in question,
Figure FDA0002560510660000032
representing a maximum amplitude of a peak point in the target class Tar; MB represents the maximum frequency bandwidth of all intervals corresponding to the target class, of which the amplitude is continuously larger than a preset frequency domain threshold value, of the heartbeat distribution data on the section of frequency domain in the preset heart rate frequency domain area; and
determining the heart rhythm judgment result within the preset time length according to the extracted heart rhythm index comprises the following steps:
counting the number of the heart rhythm indexes which are larger than a preset heart rhythm threshold value in all the extracted heart rhythm indexes;
calculating the ratio of the counted number to the extracted number of all the heart rhythm indexes;
if the ratio exceeds a preset ratio, setting the rhythm judgment result in the preset time length as arrhythmia, otherwise, setting the rhythm judgment result in the preset time length as normal rhythm.
6. The method of claim 5, wherein extracting segmented frequency domain heart rates and heart beat rhythmicity indicators from the heart beat distribution data over each segment of the frequency domain further comprises:
and comparing the maximum amplitude of the heartbeat distribution data on the section of frequency domain in the low-frequency area with the maximum amplitude of the heartbeat distribution data on the preset heartbeat frequency domain area, and setting the heartbeat distribution data on the section of frequency domain as that the segmented frequency domain heart rate and the heartbeat rhythmicity index are not extracted in response to that the maximum amplitude of the low-frequency area is greater than the maximum amplitude of the preset heartbeat frequency domain area.
7. The method according to any one of claims 2-6, wherein converting each segment of sub-heartbeat waveform data into sub-heartbeat waveform data in the time domain comprises:
calculating the Shannon energy of the sub-heartbeat waveform data, and calculating an autocorrelation curve by the Shannon energy to obtain the sub-heartbeat waveform data on a time domain; and
extracting the segmented time-domain heart rate from the sub-heartbeat waveform data on each segment of the time domain comprises the following steps:
searching peak points with amplitude values larger than a preset time domain threshold value in the sub-heartbeat waveform data on the time domain to obtain a peak point set TPS, and setting a polynomial fitting order as a first preset order;
comparing the number of peak points in the peak point set TPS with a preset second number threshold;
responding to the fact that the number of peak points in the peak point set TPS is smaller than or equal to a preset second number threshold, and setting sub-heartbeat waveform data in the time domain as the heart rate of the segmented time domain which is not extracted; or
Responding to the fact that the number of peak points in the peak point set TPS is larger than a preset second quantity threshold, performing polynomial high-order curve fitting on sub-heartbeat waveform data in the time domain by utilizing the polynomial fitting order to obtain a fitting curve, searching peak points with amplitude larger than a preset fitting curve threshold on the fitting curve to obtain a peak point set FCPS, and comparing the number of the peak points in the peak point set FCPS with a preset third quantity threshold;
Responding to the number of the peak points in the peak point set FCPS is smaller than or equal to a third quantity threshold value, and setting the sub-heartbeat waveform data in the segment of time domain as the segmented time domain heart rate which is not extracted; or
Responding to the fact that the number of the peak points in the peak point set FCPS is larger than a third quantity threshold value, for each peak point in the peak point set FCPS, obtaining a convex interval containing the peak point from sub-heartbeat waveform data in the time domain, and if the distance between the peak point and two end points of the convex interval in the time domain is smaller than a preset time threshold value, adding the peak point into a peak point set for analysis; and calculating the average value of the distances of all adjacent peak points in the peak point set in the analysis in the time domain as an average cardiac interval, and calculating the segmented time domain heart rate according to the average cardiac interval.
8. The method of claim 7, further comprising, before comparing the number of peak points in the TPS with a predetermined second number threshold:
judging whether the amplitude average value of the peak points in the TPS meets a preset amplitude threshold value or not and whether the number of the peak points meets a preset first number threshold value or not;
If not, in the peak point set TPS, for two peak points whose distance in the time domain is smaller than a predetermined proximity threshold, one peak point whose amplitude is smaller is filtered out, and the polynomial fitting order is set to a second predetermined order.
9. The method of any one of claims 2-6, wherein determining the integrated heart rate for the predetermined length of time from the frequency domain heart rate and the time domain heart rate for the predetermined length of time comprises:
calculating the difference value of the frequency domain heart rate and the time domain heart rate in the preset time length, and judging whether the difference value is smaller than a preset heart rate difference value threshold value or not;
in response to determining that the difference is less than a predetermined heart rate difference threshold, setting the integrated heart rate for the predetermined length of time to be either the frequency domain heart rate for the predetermined length of time or the time domain heart rate for the predetermined length of time; or
In response to determining that the difference is greater than or equal to a predetermined heart rate difference threshold, calculating a ratio of the frequency domain heart rate to the time domain heart rate within the predetermined length of time, and determining whether the ratio is less than a predetermined heart rate ratio threshold;
in response to determining that the ratio is less than a predetermined heart rate ratio threshold, setting the integrated heart rate for the predetermined duration to the frequency domain heart rate for the predetermined duration; or
In response to determining that the ratio is greater than or equal to a predetermined heart rate ratio threshold, setting the integrated heart rate for the predetermined duration as a weighted average of the frequency domain heart rate for the predetermined duration and the time domain heart rate for the predetermined duration.
10. The method according to any one of claims 1-6, wherein the heart rate abnormalities include bradycardia and tachycardia, and the method further comprises:
comparing the comprehensive heart rate within the preset time length with a preset heart rate over-slow threshold value and a preset heart rate over-speed threshold value respectively;
in response to the integrated heart rate over the predetermined period of time being less than a predetermined threshold of bradycardia, outputting a warning indicating bradycardia; or
In response to the integrated heart rate over the predetermined period of time being greater than a predetermined tachycardia threshold, outputting an alert indicative of tachycardia; or
And responding to the comprehensive heart rate in the preset time length being more than or equal to a preset heart rate over-slow threshold value and less than or equal to a preset heart rate over-speed threshold value, and outputting information indicating that the heart rate is normal.
11. A method for continuous monitoring of cardiac function, comprising:
continuously performing the method of any one of claims 1-10, resulting in a combined heart rate over a plurality of predetermined time periods;
Calculating a value indicative of heart rate variability according to:
Figure FDA0002560510660000051
wherein, SDANNrepresentationA characterizing value representing heart rate variability; n represents the number of said predetermined periods, HRiRepresenting the integrated heart rate within the ith predetermined time period;
determining whether the characteristic value of the heart rate variability is outside a predetermined characteristic value range; if the heart rate variability judgment result is out of the preset characterization value range, setting the heart rate variability judgment result as heart rate variability abnormity; if the heart rate variability judgment result is within the preset characterization value range, setting the heart rate variability judgment result as normal heart rate variability;
and outputting the heart rate variability judgment result.
12. An electronic device, comprising a processor and a memory, the memory storing a computer program which, when executed by the processor, implements the method of any one of claims 1-11.
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