阅读说明：本技术 基于心冲击信号的心率确定方法及装置 (Heart rate determination method and device based on heart attack signal ) 是由 李润超 宋臣 汤青 杨明明 于 2021-07-05 设计创作，主要内容包括：本发明涉及一种基于心冲击信号的心率确定方法及装置,该方法包括：对采样序列中的心冲击信号进行预处理；对预处理后的心冲击信号进行极值点检测,得到多个极值点；针对所述采样序列中的每一个极值点确定对应的振幅阈值,若所述振幅阈值大于对应极值点的振幅,则确定所述对应极值点为噪声点,并所述对应极值点去掉；根据所述采样序列中去掉噪声点的各个极值点,确定心率值。本发明采用的是动态阈值算法,这种方式更能适应时刻动态变化的心冲击信号,可以提高J波波峰的检测准确率,从而提高心率计算的准确率。还有,相对于现有技术本发明涉及到的参数较少,不需要过多的参数,计算复杂度低,计算时间短、延迟短,有利于心率的实时监测。(The invention relates to a heart rate determining method and device based on a heart attack signal, wherein the method comprises the following steps: preprocessing a cardiac shock signal in a sampling sequence; carrying out extreme point detection on the preprocessed cardiac shock signal to obtain a plurality of extreme points; determining a corresponding amplitude threshold value for each extreme point in the sampling sequence, and if the amplitude threshold value is larger than the amplitude of the corresponding extreme point, determining the corresponding extreme point as a noise point and removing the corresponding extreme point; and determining a heart rate value according to each extreme point of the removed noise points in the sampling sequence. The invention adopts a dynamic threshold algorithm, which is more suitable for the heart shock signal dynamically changing at any moment and can improve the detection accuracy of the J wave crest, thereby improving the accuracy of heart rate calculation. In addition, compared with the prior art, the method has the advantages of less related parameters, no need of excessive parameters, low calculation complexity, short calculation time and short delay, and is favorable for real-time monitoring of the heart rate.)

1. A method for determining heart rate based on ballistocardiogram signals, comprising:

preprocessing a cardiac shock signal in a sampling sequence;

carrying out extreme point detection on the preprocessed cardiac shock signal to obtain a plurality of extreme points;

determining a corresponding amplitude threshold value for each extreme point in the sampling sequence, and if the amplitude threshold value is larger than the amplitude of the corresponding extreme point, determining the corresponding extreme point as a noise point and removing the corresponding extreme point;

and determining a heart rate value according to each extreme point of the removed noise points in the sampling sequence.

2. The method of claim 1, wherein determining a corresponding amplitude threshold for each extreme point in the sequence of samples comprises:

for a first extreme point in the sampling sequence, determining an amplitude threshold of the first extreme point according to the amplitude of the first extreme point and the maximum amplitude value in a sampling period which is the latest before the first extreme point;

and aiming at each of the other extreme points except the first extreme point in the sampling sequence, determining the amplitude threshold corresponding to each of the other extreme points according to the amplitude corresponding to each of the other extreme points and the amplitude threshold corresponding to the last extreme point.

3. The method of claim 2, wherein the amplitude threshold corresponding to the first extremum point in the sample sequence is calculated using a first formula, the first formula comprising:

Thr(1)＝0.2*max/3+0.8*data(1)

where Thr (1) is the amplitude threshold of the first extreme point, max is the maximum amplitude value in the last sampling period before the first extreme point, and data (1) is the amplitude of the first extreme point; and/or

Calculating the amplitude threshold corresponding to the ith extreme point in the sampling sequence by adopting a second formula, wherein the second formula comprises

Thr(i)＝0.2*Thr(i-1)+0.8*data(i)

Wherein Thr (i) is the amplitude threshold corresponding to the ith extreme point, Thr (i-1) is the amplitude threshold corresponding to the ith-1 extreme point, data (i) is the amplitude of the ith extreme point, and i is an integer greater than 1.

4. The method according to claim 1, wherein the performing extreme point detection on the preprocessed ballistocardiogram signal to obtain a plurality of extreme points comprises:

and carrying out extreme point detection on the preprocessed ballistocardiogram signal by adopting a peak searching algorithm to obtain a plurality of extreme points, wherein the detection interval adopted in the detection process is a Fs, a is greater than 0 and less than 1, and Fs is the sampling rate of the sampling sequence.

5. The method of claim 1, wherein preprocessing the ballistocardiogram signal in the sample sequence comprises:

aiming at one sampling point in the sampling sequence, acquiring the nearest sampling point with preset number in front of the sampling point and the nearest sampling point with preset number behind the sampling point;

determining whether the sampling point is an abnormal point according to the magnitude relation between the amplitude of the sampling point and the amplitude of any one of the closest preset number of sampling points before the sampling point and the magnitude relation between the amplitude of the sampling point and the amplitude of any one of the closest preset number of sampling points after the sampling point;

and if the sampling point is determined to be an abnormal value, updating the amplitude of the sampling point according to the amplitude of the closest preset number of sampling points before the sampling point and the amplitude of the closest preset number of sampling points after the sampling point.

6. The method of claim 5, wherein the obtaining a preset number of samples nearest to the sampling point and a preset number of samples nearest to the sampling point comprises:

and acquiring a plurality of sampling points by adopting a preset sliding window, wherein the plurality of sampling points comprise the closest sampling point with preset quantity before the sampling point, the sampling point and the closest sampling point with preset quantity after the sampling point.

7. The method according to claim 5, wherein the determining whether the sample point is an abnormal point according to a magnitude relationship between the amplitude of the sample point and the amplitude of any one of the preset number of sample points nearest before the sample point and a magnitude relationship between the amplitude of the sample point and the amplitude of any one of the preset number of sample points nearest after the sample point comprises:

and if the absolute value of the difference between the amplitude of the sampling point and the amplitude of any one of the sampling points in the preset number nearest to the sampling point before the sampling point is greater than a preset value, and the absolute value of the difference between the amplitude of the sampling point and the amplitude of any one of the sampling points in the preset number nearest to the sampling point after the sampling point is greater than a preset value, the sampling point is an abnormal point.

8. The method of claim 5, wherein updating the amplitudes of the sample points according to the amplitudes of the closest preset number of sample points before and after the sample point comprises:

calculating the updated amplitude of the jth sampling point by using a third formula, wherein the third formula comprises:

in formula (II), x'_jThe updated amplitude of the jth sampling point, n is 2m +1, m is the preset number, x_j-1The amplitude of the j-1 th sample point.

9. The method of claim 1, wherein preprocessing the ballistocardiogram signal in the sample sequence comprises:

setting the cut-off frequency of a low-pass filter and the cut-off frequency of a high-pass filter according to the frequency range of the ballistocardiographic signal;

and performing low-pass filtering on the cardioblast signals in the sampling sequence according to the cut-off frequency of the low-pass filter, and performing high-pass filtering on the cardioblast signals in the sampling sequence according to the cut-off frequency of the high-pass filter.

10. A heart rate determination device based on ballistocardiogram signals, comprising:

the preprocessing module is used for preprocessing the cardiac shock signals in the sampling sequence;

the extreme point detection module is used for carrying out extreme point detection on the preprocessed cardiac shock signal to obtain a plurality of extreme points;

a noise point removing module, configured to determine a corresponding amplitude threshold for each extreme point in the sampling sequence, and if the amplitude threshold is greater than the amplitude of the corresponding extreme point, determine that the corresponding extreme point is a noise point, and remove the corresponding extreme point;

and the heart rate calculation module is used for determining a heart rate value according to each extreme point of the noise-removed points in the sampling sequence.

Technical Field

The invention relates to the technical field of heart rate calculation, in particular to a heart rate determining method and device based on a heart attack signal.

Background

The BCG signal is the change of human body external pressure caused by the heart beat and the blood circulation of the aorta, and can be used for the non-contact detection of the heart activity. Compared with the existing cardiovascular detection means, the cardiac shock signal can acquire data in an interference-free and non-contact manner, and can be detected from the surface of a human body under the unconstrained condition, and the detection device can also be designed on a bed or a chair. Compared with other cardiovascular detection technologies, BCG signal detection has the advantages of being noninvasive, undisturbed, free of direct contact, convenient to detect and the like. In recent years, with the development of sensors and digital signal processing, BCG signals are gradually valued by researchers, and researches show that the BCG signals can be applied to detection of heart rate, heart rate variability, cardiac output and the like, and have high practical value.

The BCG signal of a normal person is synchronous with the heartbeat and has repeatability. As shown in fig. 1, the BCG signal in one sampling period mainly includes H, I, J, K, M and N characteristic positions, wherein the amplitude of the J wave peak is the largest, and the interval between the J wave peaks in two adjacent sampling periods is a cardiac cycle, through which a real-time heart rate can be obtained. Common J-wave peak detection methods include a threshold value method, a pseudo-period method, an adaptive template matching method and the like, and the methods have long calculation time, large time delay and high algorithm complexity and are not favorable for real-time monitoring of the BCG signal heart rate. And the detection effect is poor, so that the heart rate calculation is not accurate.

Disclosure of Invention

In order to solve the above technical problem or at least partially solve the above technical problem, the present invention provides a heart rate determining method and apparatus based on a ballistocardiogram signal.

In a first aspect, the present invention provides a heart rate determining method based on a ballistocardiogram signal, comprising:

preprocessing a cardiac shock signal in a sampling sequence;

carrying out extreme point detection on the preprocessed cardiac shock signal to obtain a plurality of extreme points;

determining a corresponding amplitude threshold value for each extreme point in the sampling sequence, and if the amplitude threshold value is larger than the amplitude of the corresponding extreme point, determining the corresponding extreme point as a noise point and removing the corresponding extreme point;

and determining a heart rate value according to each extreme point of the removed noise points in the sampling sequence.

In a second aspect, the invention provides a heart rate determination apparatus based on ballistocardiogram signals, comprising:

the preprocessing module is used for preprocessing the cardiac shock signals in the sampling sequence;

the extreme point detection module is used for carrying out extreme point detection on the preprocessed cardiac shock signal to obtain a plurality of extreme points;

a noise point removing module, configured to determine a corresponding amplitude threshold for each extreme point in the sampling sequence, and if the amplitude threshold is greater than the amplitude of the corresponding extreme point, determine that the corresponding extreme point is a noise point, and remove the corresponding extreme point;

and the heart rate calculation module is used for determining a heart rate value according to each extreme point of the noise-removed points in the sampling sequence.

In the method and the device for determining a heart rate based on a ballistocardiogram signal, a corresponding amplitude threshold is set for each detected extreme point, if the amplitude of the extreme point is smaller than the corresponding amplitude threshold, the extreme point is considered as a noise point, the noise point is removed, the remaining extreme points are used as J-wave peaks, and the heart rate value is calculated by using the detected J-wave peaks. The method is more suitable for a dynamically changing heart attack signal at any moment, and if a uniform amplitude threshold value is adopted, the uniform amplitude threshold value needs to take multiple extreme points into consideration, so that the condition that noise points are possibly missed by adopting the uniform amplitude threshold value is avoided. In addition, compared with the prior art, the method has the advantages of less related parameters, no need of excessive parameters, low calculation complexity, short calculation time and short delay, and is favorable for real-time monitoring of the heart rate.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.

In order to more clearly illustrate the embodiments of the present invention 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, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.

FIG. 1 is a schematic diagram of a waveform of a ballistocardiograph signal over a sampling period;

FIG. 2 is a schematic flow chart of a heart rate determining method based on a ballistocardiogram signal according to an embodiment of the invention;

FIG. 3 is a schematic diagram of a sampling sequence in an embodiment of the present invention;

FIG. 4 is a schematic diagram of a sample sequence obtained by exception handling of the sample sequence shown in FIG. 3;

fig. 5 is a schematic diagram of a sample sequence obtained by filtering the sample sequence shown in fig. 4;

fig. 6 is a schematic diagram of the sample sequence shown in fig. 5 after the extreme point detection is performed on each extreme point in the sample sequence;

FIG. 7 is a schematic illustration of amplitude thresholds in a sample sequence for each of the extreme points in FIG. 6;

FIG. 8 is a schematic diagram of each J-wave peak in the sampling sequence obtained after removing two noise points in FIG. 7;

fig. 9 is a schematic structural diagram of a heart rate determining apparatus based on a ballistocardiogram signal according to an embodiment of the invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.

In a first aspect, the present invention provides a heart rate determining method based on a ballistocardiogram signal, as shown in fig. 2, the method comprising:

s100, preprocessing a heart impact signal in a sampling sequence;

it can be understood that the cardiac shock signal is a human body biological signal, has the characteristics of low frequency and low intensity, and is easily interfered by respiration, body movement and the outside, so that the cardiac shock signal obtained by direct sampling contains more noise, and accurate physiological characteristic information cannot be directly obtained, so that the collected cardiac shock signal needs to be preprocessed to obtain a cleaner cardiac shock signal.

In particular implementations, the preprocessing may include outlier removal and/or filtering, among others.

The following describes the procedure of removing the outlier through steps S110 to S130:

s110, aiming at one sampling point in the sampling sequence, acquiring the nearest sampling point with preset number in front of the sampling point and the nearest sampling point with preset number behind the sampling point;

the preset number m may be set as required, for example, the preset number m is 1, 2, 3 or other positive integer. Since the predetermined number of sampling points are taken before one sampling point and the predetermined number of sampling points are also taken after one sampling point, a total of 2m +1 sampling points are required.

Specifically, the 2m +1 sampling points may be obtained by using a sliding window, and the closest sampling point m in the preset number before the sampling point, and the closest sampling point m in the preset number after the sampling point may be obtained by using the sliding window.

For example, if the size of the sliding window is set to 5, 5 sampling points can be acquired through the sliding window, the signal values or amplitudes of the 5 sampling points are x1, x2, x3, x4 and x5, the nearest sampling points before the third sampling point are two, the nearest sampling points after the third sampling point are also two, and whether the third sampling point, which is the center sampling point, is an abnormal point can be determined according to the five sampling points.

S120, determining whether the sampling point is an abnormal point according to the magnitude relation between the amplitude of the sampling point and the amplitude of any one of the closest preset number of sampling points before the sampling point and the magnitude relation between the amplitude of the sampling point and the amplitude of any one of the closest preset number of sampling points after the sampling point;

for example, regarding the above 5 sampling points, if any of the following four conditions is satisfied, the third sampling point can be considered as an outlier:

(1) | x3-x2| > thr and | x3-x4| > thr;

(2) | x3-x1| > thr and | x3-x5| > thr;

(3) | x3-x1| > thr and | x3-x4| > thr;

(4) | x3-x2| > thr and | x3-x5| > thr;

that is, the absolute value of the difference between the amplitude of the third sampling point and the amplitude of any one of the first two sampling points is greater than the preset value thr, and the absolute value of the difference between the amplitude of the third sampling point and the amplitude of any one of the second two sampling points is greater than the preset value thr, the third sampling point is considered as an outlier.

If the size of the sliding window is set to 7, there are 3 × 3 — 9 cases, and as long as any one of the nine cases is satisfied, the center sample point, the fourth sample point, is considered to be an outlier.

As can be seen, the specific manner of determining whether the sampling point is an abnormal point in S120 may be: and if the absolute value of the difference between the amplitude of the sampling point and the amplitude of any one of the sampling points in the preset number nearest to the sampling point before the sampling point is greater than a preset value, and the absolute value of the difference between the amplitude of the sampling point and the amplitude of any one of the sampling points in the preset number nearest to the sampling point after the sampling point is greater than a preset value, the sampling point is an abnormal point.

And S130, if the sampling point is determined to be an abnormal value, updating the amplitude of the sampling point according to the amplitude of the closest preset number of sampling points before the sampling point and the amplitude of the closest preset number of sampling points after the sampling point.

It will be appreciated that the anomaly is evaluated here in the form of an amplitude update of the anomaly.

Since the determination of the abnormal point is performed according to the preset number of sampling points before and after one sampling point, in order to ensure that the sampling point after the amplitude update is a normal point, the new amplitude of the central sampling point can be determined by using the preset number of sampling points before and after.

In specific implementation, the updated amplitude of the jth sampling point may be calculated by using a formula, and in order to distinguish from the subsequent two formulas, the formula may be referred to as a third formula, where the third formula includes:

in formula (II), x'_jThe updated amplitude of the jth sampling point, n is 2m +1, m is the preset number, x_j-1The amplitude of the j-1 th sample point.

For example, for the third sample point, the updated amplitude of the third sample point is calculated according to the two sample points before and after the third sample point, and the specific formula may be:

the processing operation for the abnormal point can be realized through the above steps S110 to S130, as shown in fig. 3, which is a schematic diagram of a sampling sequence before the abnormal processing, and in the diagram, it can be seen that the sampling sequence has 1000 sampling points in total, the horizontal axis is the position of each sampling point in the sampling sequence, and the vertical axis is the amplitude or signal value of each sampling point. After exception handling of the sample sequence shown in fig. 3, the sample sequence shown in fig. 4 results.

Because the acquired heart attack signal contains respiration, circuit noise, 50Hz power frequency interference and high-frequency noise, the heart attack signal can be filtered in order to eliminate the noise of the heart attack signal and highlight the cardiac cycle information. The process of the filtering process may include the following steps S140 to S150:

s140, setting the cut-off frequency of a low-pass filter and the cut-off frequency of a high-pass filter according to the frequency range of the heart attack signal;

in practice, the frequency of the heart attack signal is detected to be in the range of 1Hz to 10Hz, so that the cut-off frequency of the high-pass filter can be set to be 1Hz, and the cut-off frequency of the low-pass filter can be set to be 10 Hz.

S150, low-pass filtering is carried out on the cardioblast signals in the sampling sequence according to the cut-off frequency of the low-pass filter, and high-pass filtering is carried out on the cardioblast signals in the sampling sequence according to the cut-off frequency of the high-pass filter.

The low-pass filter can remove power frequency interference in the cardioblast signal, and simultaneously can filter high-frequency noise, and the high-pass filter can filter low-frequency noise in the cardioblast signal.

For example, after the filtering process is performed on the sample sequence shown in fig. 4, the sample sequence shown in fig. 5 can be obtained.

Therefore, through the pretreatment process, a clean cardiac shock signal can be obtained.

S200, carrying out extreme point detection on the preprocessed heart impact signal to obtain a plurality of extreme points;

as can be seen from fig. 1, the J wave peak is the point with the largest amplitude in one sampling period, and the purpose of performing the extreme point detection is to acquire all the J wave peaks in the sampling sequence.

In specific implementation, a peak-finding algorithm may be used to perform extreme point detection on the preprocessed ballistocardiogram signal to obtain a plurality of extreme points, a detection interval adopted in the detection process is a × Fs, a is greater than 0 and less than 1, and Fs is a sampling rate of the sampling sequence.

The peak searching algorithm, namely the Findpeaks algorithm, can find a peak value in a detection interval through the peak searching algorithm, and thus a local peak value can be found through the peak searching algorithm.

Wherein the detection interval corresponds to a window whose size determines the number of extreme points found in the entire sample sequence.

For example, a sampling period is 1s, a total of 200 sampling points are acquired in one sampling period, and a total of 5 sampling periods are acquired in the sampling sequence shown in fig. 3, and the sampling rate of the sampling sequence is 200. Since there should be a J-wave in each sampling period, in order to avoid missing detection, a may be set to a value smaller than 1 and larger than 0, for example, 0.5, 200 × 0.5 ═ 100, that is, an extreme point is detected every 100 sampling points. In this way, 10 extreme points can be detected out of 1000 sampling points. Among the 10 extreme points, some are J-wave peaks, and some are not J-wave peaks. For example, for the sampling sequence shown in fig. 5, the sampling sequence shown in fig. 6 can be obtained by performing extreme point detection through a peak finding algorithm, in which a star point in the sampling sequence is a detected extreme point, there are 11 extreme points in total, two extreme points with the minimum amplitude are not J-wave peaks, and the remaining 9 extreme points with relatively large amplitude are J-wave peaks. Further screening is required after the detection of the extreme points.

It is understood that the smaller the value a, the greater the number of detected extreme points, and the larger the value a, the fewer the number of detected extreme points.

S300, determining a corresponding amplitude threshold value aiming at each extreme point in the sampling sequence, if the amplitude threshold value is larger than the amplitude of the corresponding extreme point, determining the corresponding extreme point as a noise point, and removing the corresponding extreme point;

here, a corresponding threshold is set for each extreme point, instead of using the same threshold for all extreme points, which is more suitable for the dynamic change of the heartbeat signal.

In a specific implementation, the determining, in S300, a corresponding amplitude threshold for each extreme point in the sampling sequence includes:

s310, aiming at a first extreme point in the sampling sequence, determining an amplitude threshold value of the first extreme point according to the amplitude of the first extreme point and the maximum value of the amplitude in a sampling period which is the latest before the first extreme point;

for the first extreme point, the amplitude threshold is determined based on the amplitude of the first extreme point and the maximum amplitude value in the most recent sampling period before the first extreme point. For example, the sampling period is 1s, the number of sampling points in one sampling period is 200, and the maximum value of the amplitude in one sampling period immediately before the first extreme point is actually the maximum value of the amplitude in 200 sampling points immediately before the first extreme point.

Specifically, a first formula may be adopted to calculate the amplitude threshold corresponding to the first extremum point in the sampling sequence, where the first formula includes:

Thr(1)＝0.2*max/3+0.8*data(1)

where Thr (1) is the amplitude threshold of the first extreme point, max is the maximum amplitude value in the latest sampling period before the first extreme point, and data (1) is the amplitude of the first extreme point.

S320, aiming at each of the other extreme points except the first extreme point in the sampling sequence, determining the amplitude threshold corresponding to each of the other extreme points according to the amplitude corresponding to each of the other extreme points and the amplitude threshold corresponding to the last extreme point.

For all extreme points except the first extreme point, the amplitude threshold is determined by the amplitude corresponding to the extreme point and the amplitude threshold corresponding to the last extreme point, and specifically, the amplitude threshold corresponding to the ith extreme point in the sampling sequence may be calculated by using a second formula, where the second formula includes

Thr(i)＝0.2*Thr(i-1)+0.8*data(i)

Wherein Thr (i) is the amplitude threshold corresponding to the ith extreme point, Thr (i-1) is the amplitude threshold corresponding to the ith-1 extreme point, data (i) is the amplitude of the ith extreme point, and i is an integer greater than 1.

For example, the amplitude threshold values calculated for the respective extreme points in fig. 6 are shown in fig. 7, and in fig. 7, the star points are the extreme points, and the oval black points are the amplitude threshold values. In fig. 7, two amplitude thresholds are greater than the amplitude of their corresponding extreme points, while the remaining amplitude thresholds are less than the amplitude of their corresponding extreme points. Two extreme points with amplitudes smaller than the corresponding amplitude threshold are noise points, not J-wave peaks, and therefore, the two noise points are solved to obtain 9J-wave peaks in total, as shown in fig. 8.

S400, determining a heart rate value according to each extreme point of the removed noise points in the sampling sequence.

After the J wave peaks are obtained, the time interval between two adjacent J wave peaks can be calculated, then time interval abnormality detection is carried out, for example, the variance of each time interval is calculated, then if the difference value of one time interval and the variance is too large, the time interval is abnormal, the time interval is removed, and only the average value of the normal time intervals is calculated. And finally, calculating the heart rate value by using the average value of the normal time interval.

Specifically, the heart rate value may be calculated by using a fourth formula, where the fourth formula includes:

Hr＝60/(Fs*L)

where Hr is the heart rate value, Fs is the sample rate of the sample sequence, and L is the average of the normal time intervals.

The heart rate determining method based on the cardiac shock signal provided by the invention sets a corresponding amplitude threshold value for each detected extreme point, if the amplitude of the extreme point is smaller than the corresponding amplitude threshold value, the extreme point is considered as a noise point, the noise point is removed, the remaining extreme points are used as J-wave peaks, and the heart rate value is calculated by using the detected J-wave peaks. The method is more suitable for a dynamically changing heart attack signal at any moment, and if a uniform amplitude threshold value is adopted, the uniform amplitude threshold value needs to take multiple extreme points into consideration, so that the condition that noise points are possibly missed by adopting the uniform amplitude threshold value is avoided. Compared with the prior art, the method has the advantages of fewer related parameters, no need of excessive parameters and low calculation complexity.

Furthermore, the method adopts preprocessing such as exception processing, filtering and the like, can obtain a stable J-wave peak recognition effect on the high-complexity and variable-form cardiac shock signals, namely has strong adaptability, can obtain a good denoising effect on the non-cardiac shock signals, and further improves the accuracy of heart rate calculation.

In a second aspect, the present invention provides a heart rate determining apparatus based on a ballistocardiogram signal, as shown in fig. 9, the apparatus comprising:

the preprocessing module 100 is configured to preprocess the cardiac shock signal in the sampling sequence;

the extreme point detection module 200 is configured to perform extreme point detection on the preprocessed cardiac shock signal to obtain a plurality of extreme points;

a noise point removing module 300, configured to determine a corresponding amplitude threshold for each extreme point in the sampling sequence, and if the amplitude threshold is greater than the amplitude of the corresponding extreme point, determine that the corresponding extreme point is a noise point, and remove the corresponding extreme point;

and the heart rate calculation module 400 is configured to determine a heart rate value according to each extreme point of the removed noise points in the sampling sequence.

It is understood that the explanation, examples, specific implementation, beneficial effects and the like of the related contents in the heart rate determining device provided by the second aspect can be found in the corresponding parts in the first aspect, and are not repeated herein.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.

While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.