Method for evaluating stability of PPG signal acquisition device based on information similarity

Abstract

The present application relates to a kind of PPG signal acquisition equipment stability evaluation method based on information similarity, belong to PPG signal and its acquisition equipment analysis technical field.The method includes: obtaining the original PPG signal in PPG signal acquisition equipment;Original signal is preprocessed;Characteristic point positioning and characteristic parameter calculation are carried out to the signal after pre-processing, and the signal after pre-processing is reduced sampling processing;The PPG original signal collected by different equipment or different individual is processed as described above;The PPG signal after each reduced sampling processing and its characteristic parameter are processed by information similarity algorithm two by two.The present application directly processes PPG signal itself, with the advantages such as simple method, small amount of calculation, based on this method, the stability of the equipment for the same individual signal acquisition and the specificity of different individual signal acquisition can be conveniently evaluated, the stability and repeatability of equipment are evaluated from the angle of the signal collected.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of PPG signal and its acquisition device analysis, and relates to a PPG signal acquisition device stability evaluation method based on information similarity. BACKGROUND

[0002] Photoplethysmography (PPG) is a simple, low-cost, non-invasive optical technique that uses light absorption and scattering properties to detect changes in blood flow in microvascular tissue under the skin, and has been widely used in medical monitoring and wearable devices.

[0003] The types of PPG signal acquisition devices are increasingly diverse, and they play an important role in monitoring blood oxygen, pulse rate, non-invasive blood glucose, and hemodynamic state. However, due to differences in signal conditioning circuits of different types of PPG signal acquisition devices, as well as the influence of component errors, external environment, etc. on the same device, the acquisition results may be unstable and inconsistent, which will affect the quality of PPG signals and the subsequent use of data. Therefore, ensuring the stability and reliability of the acquired PPG signals is crucial for obtaining accurate physiological information, and the quality evaluation of PPG signals is essential.

[0004] Currently, the evaluation of the stability of PPG signal acquisition devices mainly relies on hardware-level material traceability, error testing and whole machine testing, etc. The evaluation of PPG signal quality involves time-frequency analysis, machine learning, statistical analysis and other technologies. However, some evaluation methods have some shortcomings, such as long test period, high computational complexity, and strict required conditions, which need to be considered and improved in practical applications. SUMMARY

[0005] Therefore, the purpose of the present application is to provide a PPG signal acquisition device stability evaluation method based on information similarity, which directly processes the PPG signal itself and has the advantages of simple method and small amount of calculation.

[0006] To achieve the above purpose, the present application provides the following technical solutions:

[0007] A PPG signal acquisition device stability evaluation method based on information similarity, specifically comprising the following steps:

[0008] S1: data acquisition, acquiring the original PPG signal in the PPG acquisition device;

[0009] S2: signal preprocessing of the original PPG signal obtained in step S1;

[0010] S3: locating feature points on the preprocessed PPG signal obtained in step S2;

[0011] S4: calculating feature parameters based on the PPG feature points obtained in step S3;

[0012] S5: performing down-sampling processing on the preprocessed PPG signal obtained in step S2;

[0013] S6: repeating steps S1-S5 to obtain another set of PPG signals and feature parameters of the same device or different devices, the same individual or different individuals;

[0014] S7: performing information similarity algorithm processing on the two sets of PPG signals and feature parameters obtained in steps S1-S6;

[0015] S8: repeating steps S1-S7 to obtain multiple sets of information similarity algorithm processing results;

[0016] S9: analyzing the results obtained in step S8 to draw a conclusion on the stability of the PPG signal acquisition device.

[0017] Further, step S2 specifically includes the following steps:

[0018] S21: performing digital low-pass filtering processing on the original PPG signal to remove high-frequency noise interference in the signal, and the frequency response of an n-order Butterworth low-pass filter is as follows:

[0019]

[0020] where ω is the angular frequency, ω c is the cutoff angular frequency, and n is the filter order;

[0021] In the digital low-pass filtering processing, a 2-8 order Butterworth low-pass filter is used, and the cutoff frequency is set to 10-20 Hz;

[0022] S22: signal slicing processing, which retains the middle signal segment of the PPG signal;

[0023] S23: removing baseline drift to reduce the influence of breathing and motion during the acquisition process, specifically, a cubic spline interpolation algorithm is used to fit the baseline to obtain the baseline of the PPG signal, and the original signal is subtracted from the fitted baseline to remove the baseline drift of the PPG signal; on each interval [x i ,x i+1 ], the cubic spline interpolation function is expressed as:

[0024] S i (x)=a i +b i (x-x i)+c i (x-x i ) 2 +d i (x-x i ) 3

[0025] wherein a i , b i , c i , d i are undetermined coefficients;

[0026] S24: signal normalization processing, eliminating the deviation caused by the amplitude difference between different signals, the normalization formula is as follows:

[0027]

[0028] wherein PPGs is the PPG signal data point before normalization, mean(PPGs) is the mean of PPG signal before normalization, and std(PPGs) is the standard deviation of PPG signal before normalization.

[0029] Further, step S3 specifically comprises the following steps:

[0030] S31: positioning the main wave peak point (B) of the signal by using the sliding window method, obtaining the peak value in each sliding window signal, and obtaining the wave peak positioning of the actual PPG signal through interval screening;

[0031] The sliding window width is 1 / 2 of the sampling rate of the PPG signal, and the sliding step is 1 / 4 of the sampling rate of the PPG signal.

[0032] S32: positioning the wave trough point (A) of the signal based on the main wave peak point (B), and obtaining the wave trough positioning of the PPG signal through forward search of the wave peak;

[0033] S33: positioning the wave peak point (C) of the signal based on the main wave peak point (B), and obtaining the wave peak positioning of the PPG signal through backward search of the wave peak.

[0034] Further, step S4 specifically comprises the following steps:

[0035] S41: dividing the PPG signal into a plurality of pulse periods according to the wave trough point;

[0036] S42: extracting the waveform features of each pulse period, including the main wave peak height h1, the wave peak height h2 of the double beat wave, the rising branch time t1, the falling branch time t2, the main wave peak-double beat wave peak interval time t pp , and part of the feature calculation formula is as follows:

[0037]

[0038] wherein x A is the coordinate position of the starting trough point within the pulse period, x A′ is the coordinate position of the ending trough point within the pulse period, x B is the coordinate position of the main peak point within the pulse period, x C is the coordinate position of the double peak point within the pulse period, and Sample_rate is the PPG signal sampling rate.

[0039] S43: Extract the heart rate variability features of each two pulse periods, including RR interval and relative RR interval, etc. For n pulse periods, the feature calculation formula is as follows:

[0040]

[0041] wherein, is the coordinate position of the main peak point within the i-th pulse period, RR i is the i-th RR interval value, rr i is the i-th relative RR interval value, and Sample_rate is the PPG signal sampling rate.

[0042] Further, in step S5, the preprocessed PPG signal is subjected to down-sampling processing, and the processing method is as follows:

[0043] z[n] = decimate(x[n], D, ftype);

[0044] wherein z[n] is the signal after down-sampling processing, x[n] is the original signal, D is the down-sampling factor, ftype is the filter type, and decimate(a, b, c) represents the down-sampling function.

[0045] Further, step S7 specifically includes the following steps:

[0046] S71: For a continuous time interval sequence X i = {x0, x1, …, x n}, starting from x0, the continuous two interval sequences {x i , x i+1} are taken as a group, and a group of interval pairs will produce two change states of x i , which are represented by 0 and 1 respectively, and the sequence is subjected to binary conversion, and the conversion formula is as follows:

[0047]

[0048] S72: According to step S71, the signal sequence with a length of n+1 is converted into a binary symbol sequence B i = {I0, I1, …, In} with a length of n.n}, take a window of length m, m < n, and move it back by one data element each time, starting from I0, and divide B i into a set of m-length sequences of binary symbols.

[0049] In step S72, the window length in the information similarity algorithm is set to 2-4 when the input data is the PPG feature parameter, and set to 5-8 when the input data is the down-sampled PPG signal.

[0050] S73: Each m-length sequence of binary symbols represents a unique change pattern of the original data. For a set of n-length data, there are 2 m change patterns for the m-length sequences of binary symbols, and the set composed of the change patterns is denoted as W i = {M0, M1, …, M n-m}; the frequencies and probabilities of the change patterns in the set W are counted, and the probabilities are sorted in descending order;

[0051] S74: According to the probabilities and the sorting of the change patterns, the similarity distance of the two pieces of data is calculated. The more similar the data are, the smaller the similarity distance is, and vice versa. The calculation formula of the similarity distance is as follows:

[0052]

[0053] where D m (S1, S2) represents the similarity distance between the data S1 and S2, p1(w i ) and R1(w i ) represent the probability and the ranking of the binary symbol sequence w i in the data S1, respectively; p2(w i ) and R2(w i ) represent the probability and the ranking of the binary symbol sequence w i in the data S2, respectively.

[0054] S75: The rankings of the change patterns of the data S1 and S2 are plotted into a scatter plot. If the rankings of the same change patterns of the two pieces of data are similar, the scatter points are closer to the diagonal line (y=x). The closer the scatter points on the whole scatter plot are to the diagonal line, the higher the similarity of the two signals is, and vice versa. The average distance of the scatter points from the diagonal line is also calculated, which can measure the similarity of the two pieces of data. The calculation formula of the average distance is as follows:

[0055]

[0056] where d represents the average distance of the scatter points from the diagonal line.

[0057] Further, step S9 specifically comprises the following steps:

[0058] S91: respectively calculate the similarity distance D between the two groups of data m and the average distance of each scatter point to the diagonal ;

[0059] S92: compare the distance with the threshold value to obtain the similarity between the two groups of data, and the comparison method is as follows:

[0060]

[0061] wherein the similarity distance threshold value D m_threshold ∈(min(D m ),max(D m )), and the average distance threshold value of each scatter point to the diagonal

[0062] The similarity distance threshold value is set to 0.03-0.04, and the average distance threshold value of each scatter point to the diagonal is set to 3-6;

[0063] S93: for D m of step S92 , judge the similarity of the signal, and the smaller the value, the higher the similarity between the two groups of data, if the two groups of data come from the same individual and different devices, it indicates that the sampling consistency of the test device is good; if the two groups of data come from the same individual and the same device, it indicates that the sampling repeatability of the test device is good; if the two groups of data come from different individuals and the same device, it indicates that the sampling specificity of the test device is good; vice versa.

[0064] The beneficial effects of the present application are that the method only needs to process the PPG signal itself, and after preprocessing, feature point positioning, feature parameter calculation, and downsampling processing of the obtained original PPG signal, the information similarity algorithm processing can be performed based on the feature parameter sequence or the down-sampled PPG signal sequence, and the similarity information of the evaluated data is obtained, and the stability of the PPG signal acquisition device is further evaluated. The present application can be conveniently used to evaluate the stability of the signal acquisition of the same individual and the specificity of the signal acquisition of different individuals; the processing method of the PPG signal is mature and reliable, the result is accurate and reliable, and it has practicality, the information similarity algorithm used is simple and has small calculation amount, and is suitable for engineering application.

[0065] Other advantages, objects, and features of the present application will be set forth in part in the following specification taken in conjunction with the accompanying drawings, and in part will be apparent to those skilled in the art from the following description, or can be learned from the practice of the present application. The objects and other advantages of the present application can be realized and attained by the below description. Attached Figure Description

[0066] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0067] Figure 1 This is a schematic diagram of the stability evaluation method for PPG signal acquisition equipment based on information similarity algorithm proposed in this invention;

[0068] Figure 2 This is a schematic diagram of the signal preprocessing, feature point localization, and feature parameter calculation process in the method of the present invention;

[0069] Figure 3 This is a schematic diagram of the information similarity algorithm in the method of the present invention;

[0070] Figure 4 A typical PPG signal waveform characteristic diagram;

[0071] Figure 5 The time-domain waveforms and characteristic parameter calculation results of the original signal, preprocessed signal, signal after feature point localization, and downsampled signal in the method of this invention;

[0072] Figure 6 This is a scatter plot generated by the information similarity algorithm in the method of this invention to rank the various change patterns of different PPG signal data.

[0073] Figure 7 This is a heatmap showing the similarity distance between pairs of signals calculated by the information similarity algorithm for different PPG signal data in the method of this invention.

[0074] Figure 8 This is a heatmap of the average distance between each scatter point and the diagonal line between different PPG signal data calculated by the information similarity algorithm in the method of this invention. Detailed Implementation

[0075] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0076] Please see Figures 1-8The application provides a PPG signal acquisition device stability evaluation method based on information similarity, Figure 1 The flow of the method is shown, after the original PPG signal is acquired from the PPG signal acquisition device (two groups of signals are needed), the two groups of signals are subjected to the same pretreatment method, path one: continue to locate the feature points, calculate the related feature parameters (h1, h2, t1, t2, tpp, RRs, rrs, etc.) by using the located feature points, and obtain the feature parameter sequence; path two: the pretreated signal is subjected to down-sampling processing, and the down-sampled PPG sequence is obtained, the purpose of the down-sampling is to remove the points with consistent change trend in the PPG signal, so that the purpose of reducing the calculation amount in the information similarity algorithm is achieved. The information similarity algorithm can be calculated for the feature parameter sequence or the down-sampled waveform sequence of the two groups of PPG signals, and the signal similarity and the acquisition device stability evaluation are further obtained.

[0077] Figure 2 The signal pretreatment, feature point positioning and feature parameter calculation flow is shown, and the specific flow is as follows:

[0078] (1) The original PPG signal is subjected to signal pretreatment, specifically including the following steps:

[0079] S21: The original PPG signal is subjected to digital low-pass filtering processing, and the high-frequency noise interference in the signal is removed, the frequency response of an n-order Butterworth low-pass filter is as follows:

[0080]

[0081] Wherein, ω is the angular frequency, ω c is the cut-off angular frequency, and n is the filter order;

[0082] A 2-8 order Butterworth low-pass filter is used in the digital low-pass filtering processing, and the cut-off frequency is set to be in the range of 10-20 Hz;

[0083] S22: Signal slicing processing, the middle signal segment of the PPG signal is reserved;

[0084] S23: Baseline drift is removed, and the influence of breathing and motion in the acquisition process is reduced, specifically, a cubic spline interpolation algorithm is used to fit the baseline, the baseline of the PPG signal is obtained, and the original signal is subtracted from the fitted baseline, so that the baseline drift of the PPG signal is removed; on each interval [x i ,x i+1 ], the cubic spline interpolation function is expressed as:

[0085] S i (x)=a i +b i (x-x i )+ci (x-x i ) 2 +d i (x-x i ) 3

[0086] wherein, a i , b i , c i , d i are undetermined coefficients;

[0087] S24: signal normalization processing, eliminating the deviation caused by the amplitude difference between different signals, the normalization formula is as follows:

[0088]

[0089] wherein, PPGs is the PPG signal data point before normalization, mean(PPGs) is the mean of PPG signal before normalization, std(PPGs) is the standard deviation of PPG signal before normalization.

[0090] (2) The feature point positioning is carried out on the preprocessed PPG signal, which specifically includes the following steps:

[0091] S31: The main wave peak point (B) of the signal is positioned by using the sliding window method, the peak value in each sliding window signal is obtained, and the wave peak positioning of the actual PPG signal is obtained through the interval screening;

[0092] The width of the sliding window is 1 / 2 of the sampling rate of the PPG signal, and the sliding step is 1 / 4 of the sampling rate of the PPG signal;

[0093] S32: The wave trough point (A) of the signal is positioned based on the main wave peak point (B), and the wave trough positioning of the PPG signal is obtained through the forward search of the wave peak;

[0094] S33: The wave peak point (C) of the signal is positioned based on the main wave peak point (B), and the wave peak positioning of the PPG signal is obtained through the backward search of the wave peak.

[0095] (3) The feature parameters are calculated based on the PPG feature points, which specifically includes the following steps:

[0096] S41: The PPG signal is divided into several pulse periods according to the wave trough point;

[0097] S42: The waveform features of each pulse period are extracted, including the main wave peak height h1, the wave peak height h2 of the double beat wave, the rising branch time t1, the falling branch time t2, the main wave peak-double beat wave peak interval time t pp , etc., and part of the feature calculation formula is as follows:

[0098]

[0099] where x A is the coordinate position of the starting trough point within the pulse period, x A′ is the coordinate position of the ending trough point within the pulse period, x B is the coordinate position of the main peak point within the pulse period, x C is the coordinate position of the dicrotic wave peak point within the pulse period, and Sample_rate is the sampling rate of the PPG signal;

[0100] S43: Extract the heart rate variability features of pairwise pulse periods, including RR interval and relative RR interval, etc. For n pulse periods, the feature calculation formula is as follows:

[0101]

[0102]

[0103] where is the coordinate position of the main peak point within the i-th pulse period, RR i is the i-th RR interval value, rr i is the i-th relative RR interval value, and Sample_rate is the sampling rate of the PPG signal.

[0104] Figure 3 The information similarity algorithm process is shown as follows, which specifically includes the following steps:

[0105] S71: For a continuous time interval sequence X i ={x0, x1, …, x n}, starting from x0, take two consecutive interval sequences {x i , x i+1} as a group. One group of interval pairs will produce two change states of x i . Represent these two change states with 0 and 1 respectively, and perform binary conversion of the sequence. The conversion formula is as follows:

[0106]

[0107] S72: According to step S71, convert the signal sequence with length n + 1 into a binary symbol sequence B i ={I0, I1, …, I n}. Take a window with length m, where m < n. Starting from I0, move 1 data element backward each time, and divide B i into multiple binary symbol sequence sets with length m;

[0108] In step S72, the window length in the information similarity algorithm is set to 2 to 4 when the input data is PPG feature parameters, and to 5 to 8 when the input data is downsampled PPG signal.

[0109] S73: Each binarized symbol sequence of length m represents a unique variation pattern of the original data. For a set of data of length n, there are a total of 2 binarized symbol sequences with a window length of m. m The set formed by combining various change patterns is denoted as W. i ={M0,M1,…,M n-m}; Statistical set W i The frequency and probability of various change patterns appearing in the data are ranked from highest to lowest probability.

[0110] S74: Calculate the similarity distance between two data segments based on the probability and ranking of each change pattern. The more similar the data, the smaller the similarity distance, and vice versa. The formula for calculating the similarity distance is as follows:

[0111]

[0112] Among them, D m (S1,S2) represents the similarity distance between data S1 and S2, p1(w i ) and R1(w i ) represent the binary symbol sequence w respectively. i The probability and ranking of p2(w) in data S1; i ) and R2(w i ) represent the binary symbol sequence w respectively. i The probability and ranking of occurrence in data S2;

[0113] S75: Rank the variation patterns of data S1 and S2 into a scatter plot. If the rankings of the same variation patterns in the two data sets are similar, the closer the scatter points are to the diagonal (y=x); the closer the scatter points are to the diagonal, the higher the similarity between the two signals, and vice versa. The similarity between the two data sets can also be measured by calculating the average distance between each scatter point and the diagonal. The formula for calculating the average distance is:

[0114]

[0115] in, This represents the average distance between each point and the diagonal.

[0116] Figure 4 The image shows a typical PPG signal waveform characteristic diagram. Figure 4In the figure, A point is PPG signal trough point, B point is PPG signal main peak point, C point is PPG signal double beat wave peak point, D point is PPG signal rising branch slope maximum point, E point is PPG signal descending branch slope maximum point (absolute value), F point is PPG signal descending branch inflection point; h1 is PPG signal main peak height, h2 is PPG signal double beat wave peak height; t1 is PPG signal rising branch time, t2 is PPG signal descending branch time, tpp is time interval of PPG signal main peak to double beat wave peak.

[0117] Figure 5 The figure shows time domain waveforms of original signal, preprocessed signal, signal after locating feature points, signal after downsampling, and calculation results of feature parameters. Figure 5 In the figure, A point is PPG signal trough point, B point is PPG signal main peak point, C point is PPG signal double beat wave peak point, D point is PPG signal rising branch slope maximum point, E point is PPG signal descending branch slope maximum point (absolute value), F point is PPG signal descending branch inflection point; h1 is PPG signal main peak height, h2 is PPG signal double beat wave peak height; t1 is PPG signal rising branch time, t2 is PPG signal descending branch time, tpp is time interval of PPG signal main peak to double beat wave peak.

[0118] Figure 6 、 Figure 7 、 Figure 8 In the figure, A point is PPG signal trough point, B point is PPG signal main peak point, C point is PPG signal double beat wave peak point, D point is PPG signal rising branch slope maximum point, E point is PPG signal descending branch slope maximum point (absolute value), F point is PPG signal descending branch inflection point; h1 is PPG signal main peak height, h2 is PPG signal double beat wave peak height; t1 is PPG signal rising branch time, t2 is PPG signal descending branch time, tpp is time interval of PPG signal main peak to double beat wave peak. Figure 5 In the figure, A point is PPG signal trough point, B point is PPG signal main peak point, C point is PPG signal double beat wave peak point, D point is PPG signal rising branch slope maximum point, E point is PPG signal descending branch slope maximum point (absolute value), F point is PPG signal descending branch inflection point; h1 is PPG signal main peak height, h2 is PPG signal double beat wave peak height; t1 is PPG signal rising branch time, t2 is PPG signal descending branch time, tpp is time interval of PPG signal main peak to double beat wave peak. Figure 6 In the figure, A point is PPG signal trough point, B point is PPG signal main peak point, C point is PPG signal double beat wave peak point, D point is PPG signal rising branch slope maximum point, E point is PPG signal descending branch slope maximum point (absolute value), F point is PPG signal descending branch inflection point; h1 is PPG signal main peak height, h2 is PPG signal double beat wave peak height; t1 is PPG signal rising branch time, t2 is PPG signal descending branch time, tpp is time interval of PPG signal main peak to double beat wave peak.Figure 7 ), the darker the color block in the heat map, the higher the similarity between the two signals corresponding to the block, and vice versa. Analysis Figures 5-7 It can be known that the signals S1-S5 can be identified as high similarity between each other, and low similarity between S1-S5 and S6, S7. The method can reflect the stability and specificity of the evaluated device for data of the same device, the same individual and different individuals.

[0119] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should be covered in the scope of the claims of the present application.

Claims

1. A method for evaluating stability of a PPG signal acquisition device based on information similarity, characterized in that, The method specifically comprises the following steps: S1: obtaining an original PPG signal in a PPG acquisition device; S2: performing signal preprocessing on the original PPG signal obtained in step S1; S3: locating feature points of the PPG signal obtained after preprocessing in step S2; S4: calculating feature parameters based on the PPG feature points obtained in step S3; S5: performing down-sampling processing on the PPG signal obtained after preprocessing in step S2; S6: repeating steps S1-S5 to obtain another group of PPG signals and feature parameters of the same device or different devices, the same individual or different individuals; S7: performing information similarity algorithm processing on the two groups of PPG signals and feature parameters obtained in steps S1-S6; S8: repeating steps S1-S7 to obtain a plurality of information similarity algorithm processing results; S9: analyzing the results obtained in step S8 to obtain a conclusion on the stability of the PPG signal acquisition device.

2. The PPG signal acquisition device stability assessment method of claim 1, wherein, Step S2 specifically comprises the following steps: S21: performing digital low-pass filtering processing on the original PPG signal to remove high-frequency noise interference in the signal; S22: performing signal slicing processing to retain the middle signal segment of the PPG signal; S23: removing baseline drift, specifically, using a cubic spline interpolation algorithm to fit the baseline to obtain the baseline of the PPG signal, and removing the baseline drift of the PPG signal by subtracting the fitted baseline from the original signal; S24: performing signal normalization processing to eliminate the deviation caused by the amplitude difference between different signals.

3. The PPG signal acquisition device stability assessment method of claim 1, wherein, Step S3 specifically comprises the following steps: S31: locating the main peak point B of the signal by using a sliding window method, obtaining the peak value in each sliding window signal, and obtaining the wave peak positioning of the actual PPG signal through interval screening; S32: locating the wave trough point A of the signal based on the main peak point B, and obtaining the wave trough positioning of the PPG signal through forward search of the wave peak; S33: locating the double beat wave peak point C of the signal based on the main peak point B, and obtaining the double beat wave peak positioning of the PPG signal through backward search of the wave peak.

4. The PPG signal acquisition device stability assessment method of claim 1, wherein, Step S4 specifically comprises the following steps: S41: dividing the PPG signal into a plurality of pulse periods according to the wave trough point; S42: Extract waveform features of each pulse cycle, including main peak height , double peak height , upstroke time , downstroke time , main peak-double peak interval time ; S43: extracting the heart rate variability features of each two pulse periods, including RR interval and relative RR interval.

5. The PPG signal acquisition device stability assessment method of claim 1, wherein, In step S5, the PPG signal obtained after preprocessing is processed by down-sampling, and the processing method is as follows: ； wherein, is the down-sampled processed signal, is the original signal, is the down-sampling factor, is the filter type, denotes the down-sampling function.

6. The PPG signal acquisition device stability assessment method of claim 1, wherein, Step S7 specifically comprises the following steps: S71: For a continuous time interval sequence ,from Begin by taking two consecutive interval sequences. As a set, a set of interval pairs will produce The sequence is divided into two states, represented by 0 and 1 respectively, and then binarized using the following formula: S72: convert the signal sequence of length to a binary symbol sequence of length according to step S71 , take a window of length , , move back by 1 data element each time starting from , divide into a plurality of binary symbol sequence sets of length ; S73: Each binary symbol sequence of length represents a unique variation pattern of the original data. For a set of data of length , there are binary symbol sequences of length , and the set of combinations is denoted as ; the frequencies and probabilities of the various variation patterns in the set are counted and sorted in descending order of probability; S74: calculating the similarity distance of the two data according to the probability and order of each change mode, the more similar the data, the smaller the similarity distance, and vice versa; the calculation formula of the similarity distance is as follows: wherein, representing data with a similarity distance between, and respectively represent a binary symbol sequence a probability and a rank of appearing in data ; and respectively represent a binary symbol sequence a probability and a rank of appearing in data ; S75: data with of each variation mode are plotted as scatter plots, if the rankings of two pieces of data under the same variation mode are similar, the scatter points are closer to the vicinity of the diagonal line; the closer the scatter points on the entire scatter plot are to the diagonal line, the higher the similarity of the two signals, and vice versa; by calculating the average distance of each scatter point from the diagonal line, the similarity of the two pieces of data can also be measured; the average distance calculation formula is: wherein, represents the average distance of each scatter point from the diagonal.

7. The PPG signal acquisition device stability assessment method of claim 6, wherein, In step S72, the window length in the information similarity algorithm is set to 2-4 when the input data is the PPG feature parameter, and is set to 5-8 when the input data is the PPG signal after down-sampling.

8. The PPG signal acquisition device stability assessment method of claim 6, wherein, Step S9 specifically comprises the following steps: S91: Calculate the similarity distance between the two groups of data respectively and the average distance between each scatter point and the diagonal ; S92: comparing the distance with the threshold value to obtain the similarity between the two groups of data, and the comparison method is as follows: wherein the similarity distance threshold , the average distance threshold of each scatter point and the diagonal line ; S93: for step S92 With The smaller the value of the signal similarity, the higher the similarity between the two groups of data. If the two groups of data come from the same individual and different devices, it indicates that the sampling consistency of the test device is good. If the two groups of data come from the same individual and the same device, it indicates that the sampling repeatability of the test device is good. If the two groups of data come from different individuals and the same device, it indicates that the sampling specificity of the test device is poor. Conversely, the same is true.

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