Method for identifying and positioning feature points of PPG and derivative signals thereof

Abstract

The invention relates to a method for identifying and positioning feature points of PPG and derivative signals thereof, and belongs to the technical field of biomedical signal processing. The method comprises the following steps: S1, signal preprocessing: filtering and extracting a complete period; s2, detecting a basic datum point: identifying a wave crest, a wave trough, a zero crossing point and other mapped feature points by adopting a method of combining a moving sliding window and a threshold value; s3, waveform classification: taking the detected basic reference point as a reference, and realizing classification based on the characteristics of waveform form change; and S4, identification of different types of feature points: selecting a corresponding method according to a waveform classification result to realize identification of the feature points in different types of states. According to the invention, identification and positioning of multiple types and multiple feature points are realized, and the feature points of PPG and derivative signals thereof in a pathological state can be accurately identified and positioned.

Description

Technical Field

[0001] The present invention belongs to the technical field of biomedical signal processing, and relates to a method for identifying and locating characteristic points of PPG and its derivative signals. Background Art

[0002] Pulse is an external reflection of important information such as the state of the heart and blood vessels, and its fluctuation contains rich physiological and pathological information. Each beat of the cardiovascular system drives a periodic fluctuation in the blood volume within the peripheral blood vessels, and this dynamic change is the fundamental mechanism for the formation of the PPG waveform. Any subtle change within the human body may have a significant impact on the pulse system, and the resulting changes are not only reflected in the characteristics such as the amplitude, rate, and rhythm of the waveform, but also an intuitive reflection of the current physiological state of the cardiovascular system and a sensitive indicator of potential pathological changes. Therefore, the PPG waveform has become a key window for exploring the physiological and pathological conditions of the human body.

[0003] Currently, the methods for identifying characteristic points of pulse wave signals include the time-domain differential threshold method, the time-domain discrimination method based on extreme values, amplitudes, slopes, etc., and the method of decomposing the signal by methods such as Hilbert transform, EMD, and wavelet transform and detecting characteristic waveforms at specific layers.

[0004] However, due to the complexity of pathological factors and the variability of signals in clinical practice, when facing pathological pulse wave signals, the interference of noise and the complex pathological state are likely to cause the signal characteristics to be submerged or the signal to be severely deformed, resulting in the fuzziness of the reference points. As a result, the traditional characteristic point recognition methods have limitations, leading to problems such as difficult characteristic point recognition, low recognition efficiency, and poor recognition accuracy. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a method for identifying and locating characteristic points of PPG and its derivative signals based on multiple features and multiple categories to accurately identify and locate the characteristic points of PPG and its derivative signals in a pathological state, aiming at the problems of difficult characteristic point recognition, low recognition efficiency, and poor recognition accuracy of pathological pulse wave signals.

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

[0007] A method for identifying and locating characteristic points of PPG and its derivative signals specifically includes the following steps:

[0008] S1: Signal preprocessing: Obtain the original PPG signal, use a Butterworth low-pass filter and cubic spline interpolation method to remove high-frequency noise and baseline drift, and use the period segmentation method to extract the waveform within the entire period range in the time domain;

[0009] S2: Detection of basic reference points: Based on the time-domain full-cycle PPG signal obtained in step S1, perform point-by-point difference operations on the waveform data of the PPG signal, calculate the first derivative function VPG signal and the second derivative function APG signal of the PPG signal respectively, and use the methods of moving sliding window, threshold determination, and adaptive dynamic adjustment strategy to detect the basic reference points of the PPG, VPG, and APG waveforms;

[0010] S3: Waveform classification: Based on the basic reference points identified in step S2, divide the signal segments, and effectively classify two different forms of PPG waveforms and three different forms of APG waveforms according to the classification strategy of waveform morphological changes in the signal segments;

[0011] S4: Identification of feature points of different categories: Based on the different categories of PPG waveforms and APG waveforms obtained by the classification strategy of waveform morphological changes in step S3, adaptively select feature points that match the waveform characteristics to identify the N points and D points of different PPG waveforms, and the c points and d points of different APG waveforms; among them, the N point and D point are the bisecting notch point and diastolic peak point of the PPG waveform respectively; the c point is the late systolic augmentation wave of the APG waveform, and the d point is the late systolic decay wave of the APG waveform.

[0012] Further, in step S2, the basic reference points include the peak S and trough O of the PPG waveform, the peak u and trough v of the VPG waveform, the peak a and trough b of the APG waveform, and the feature points where the peaks and troughs of each waveform are mapped to other waveforms, that is, the maximum slope point E of the rising branch of the PPG waveform and the minimum slope point F of the falling branch, the zero-crossing point L of the VPG waveform, the diastolic peak w point of the VPG waveform, and the late systolic augmentation wave e point of the APG waveform.

[0013] Further, in step S2, the width value W of the moving sliding window is set according to the following formula:

[0014] W = m * fs

[0015] where fs is the signal sampling frequency, and m is a coefficient, and its value range is 0.6 - 1.2; the preset amplitude threshold H is taken as 0.6 - 0.8 times the maximum amplitude in the waveform data to identify the peaks and troughs of the PPG, VPG, and APG waveforms;

[0016] The adaptive dynamic adjustment strategy includes threshold judgment of the peak interval and a low-amplitude missed detection judgment mechanism.

[0017] Further, in step S2, the threshold judgment of the peak interval is to use the method of combining a moving sliding window with an initial threshold to identify the peaks and troughs of the PPG, VPG, and APG signals, and then calculate the peak interval T based on the peak S point of the PPG signal. The threshold R1 of the misjudgment mechanism of the main wave peak of the VPG and APG waveforms is preset to 0.6 to 0.8 times the average value of the peak interval T. If the peak interval is less than R1, it is confirmed that there is a situation of peak misdetection;

[0018] The low-amplitude missed detection judgment mechanism calculates the peak interval T based on the peak S point of the PPG waveform identified by combining a moving sliding window with an initial threshold. The threshold R2 of the low-amplitude missed detection judgment mechanism of the VPG and APG waveforms is preset to 1.5 to 1.7 times the average value of the peak interval T. If the current peak interval is greater than R2, it is considered that there is a missed detection of a low-amplitude peak in the middle.

[0019] Further, in step S3, the judgment mechanism of the classification strategy for waveform morphological changes is to perform extreme value calculation and curvature analysis within the signal segment to determine whether there are extreme points and inflection points in the waveform, which serves as the basis for classifying the PPG and APG waveforms;

[0020] The two different morphological PPG waveforms are divided into two categories: those with obvious dicrotic waves and those without obvious dicrotic waves according to the waveform characteristics of points N and D;

[0021] The three different morphological APG waveforms are divided into three categories: those with significant points c and d, those with relatively obvious points c and d, and those without clear points c and d according to the waveform characteristics of points c and d;

[0022] The determination of whether there are extreme points and inflection points in the waveform is based on dividing the signal segment with the identified basic reference point as the reference. First, perform a point-by-point difference operation on the signal segment and calculate the maximum and minimum values in the segment. If both extreme points exist, the waveform is divided into one category; if not, perform a second point-by-point difference operation, calculate the curvature of the signal segment and analyze it, and use the curvature to determine whether there is an inflection point. If there is, the waveform is divided into the second category; if neither extreme points nor inflection points exist, it is divided into the third category.

[0023] Further, in step S4, the feature point recognition methods for different categories include the concave and convex point positioning method and the slope inversion method;

[0024] The concave and convex point positioning method is to fit a straight line between two adjacent basic reference points using a mathematical formula and calculate the point with the maximum distance from the signal of the adjacent reference points to the straight line; among them, the point with the maximum distance on the left is defined as the concave point, and the point with the maximum distance on the right is defined as the convex point;

[0025] The slope inversion method uses the first derivative of the signal segment (i.e., the slope of the waveform) to detect the change in the slope sign. The point where the slope changes from positive to negative or from negative to positive is called the slope inversion point, which is also the characteristic point to be located.

[0026] The beneficial effects of the present invention are as follows: The method of the present invention is applicable to pathological pulse wave signals, and has the ability to comprehensively identify and accurately locate the characteristic points of multi-category waveforms, and can more accurately capture and analyze the signal characteristics under pathological conditions.

[0027] Based on the identification of characteristic points, the present invention sets up multiple judgment mechanisms. These judgment mechanisms can adaptively adjust the recognition parameters and strategies, so that they can dynamically adjust to adapt to the physiological and pathological states of different patients according to the characteristics of the PPG morphology changes caused by different cardiovascular diseases.

[0028] Other advantages, objectives and features of the present invention will be described to some extent in the subsequent specification, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be described in detail preferably with reference to the accompanying drawings, wherein:

[0030] Figure 1 is the flowchart for the identification and location of characteristic points of the PPG and its derivative signals of the present invention;

[0031] Figure 2 is the schematic diagram of the positions of characteristic points of the PPG and its derivative signals of the present invention;

[0032] Figure 3 is the flowchart of signal preprocessing in the present invention;

[0033] Figure 4 is the flowchart for the detection of basic reference points and the mapping relationship diagram of each waveform characteristic point in the present invention;

[0034] Figure 5 is several category diagrams of waveform classification in the present invention;

[0035] Figure 6 is the schematic diagram of the identification results of characteristic points of the PPG and its derivative signals of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0036] The following describes the embodiments of the present invention through specific examples. Those skilled in the art can easily understand the 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. Various details in this specification can also 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 drawings provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0037] Among them, the drawings are only for illustrative purposes, showing only schematic diagrams, not physical diagrams, and should not be construed as limiting the present invention; in order to better illustrate the embodiments of the present invention, some components in the drawings will be omitted, enlarged or reduced, which does not represent the size of the actual product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0038] In the drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "rear", etc. indicating the orientation or positional relationship, they are based on the orientation or positional relationship shown in the drawings. This is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, the terms describing the positional relationship in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to specific circumstances.

[0039] Please refer to Figures 1 to 6 , this embodiment provides a method for identifying and positioning feature points of PPG and its derivative signals. As Figure 1 shown, the method specifically includes the following steps:

[0040] 1) Signal preprocessing: Use a Butterworth low-pass filter and cubic spline interpolation method to remove high-frequency noise and baseline drift from the original PPG signal, and use the period segmentation method to extract the waveform within the entire period range in the time domain, as Figure 3 shown.

[0041] The cut-off frequency of the Butterworth filter is set to 10 Hz based on the spectral characteristics of the pulse wave signal, effectively eliminating high-frequency noise components above 10 Hz while retaining the key low-frequency information in the signal.

[0042] The cubic spline interpolation method uses the identified wave trough points as the benchmark points for interpolation, fits the entire signal, and then subtracts the fitted curve from the signal after filtering out high-frequency noise to obtain the corrected PPG signal, so as to eliminate the drift caused by non-pulse wave components.

[0043] The period segmentation method directly uses the first wave trough O point and the last wave trough O point identified from the PPG waveform as the benchmarks for waveform segmentation.

[0044] 2) Detection of basic reference points: Based on the time-domain full-cycle PPG signal obtained in step 1), perform point-by-point difference operations on the waveform data of the PPG signal, calculate the first derivative function VPG signal and the second derivative function APG signal of the PPG signal respectively, and use the methods of moving sliding window, threshold determination, and adaptive dynamic adjustment strategy to detect the basic reference points of the PPG, VPG, and APG waveforms, as Figure 4 shown.

[0045] The size w of the moving sliding window is set according to the formula m*fs, where fs is the signal sampling frequency and m is a coefficient with a value range of 0.6 to 1.2. The preset amplitude threshold H is set to 0.6 to 0.8 times the maximum amplitude in the PPG signal. In this embodiment, the optimal values w = 0.6*fs and H = 0.7*max(PPG) are selected to identify the peaks and troughs of the PPG, VPG, and APG waveforms. The adaptive dynamic adjustment strategy includes threshold judgment of the peak interval and missed detection judgment mechanism for low-amplitude waveforms, specifically:

[0046] To avoid misjudging the high-amplitude diastolic peaks w and e as the main wave peaks, calculate the peak interval T according to the peak S point of the PPG signal, and preset the threshold R1 of the misjudgment mechanism of the main wave peaks of the VPG and APG waveforms to 0.6 to 0.8 times the average value of the peak interval T. The optimal value selected in the present invention is 0.7*mean(T). If the peak interval is less than R1, it can be confirmed that there is a situation of peak misdetection. Compare the located peaks in this period and set the peak with a smaller amplitude to zero.

[0047] For the situation that low-amplitude waveforms are prone to missed detection, preset the threshold R2 of the missed detection judgment mechanism to 1.5 to 1.7 times the average value of the peak interval T. The optimal value selected in the present invention is 1.7*mean(T). If the current peak interval is greater than R2, it is considered that there is a missed detection of a low-amplitude peak in the middle, and a lower second threshold is used for backtracking to achieve accurate positioning of the peak. The same applies to the trough.

[0048] Based on the above method, the peaks and troughs of the PPG, VPG, and APG waveforms are respectively mapped to the zero-crossing point L of the VPG waveform, the maximum slope point E of the rising branch of the PPG waveform, and the minimum slope point F of the falling branch of the PPG waveform, as Figure 2As shown in the figure. Based on the VPG waveform recognition, the peak u and trough v points of the signal are segmented. By sorting to find the extreme values, the position of the diastolic peak w of the VPG waveform is determined. The point with the maximum slope between the v point and the w point is calculated and mapped to the e point of the APG waveform. According to the position of the e point, the f point is identified by sorting to find the extreme values. The e point and f point of the APG waveform will assist in the positioning of the N point and D point of the PPG waveform with an unclear dicrotic wave.

[0049] 3) Waveform classification: Based on the basic reference points identified in step 2), the signal segments are divided, and the classification strategies of waveform morphological changes are used to classify the PPG waveforms of two morphologies and the APG waveforms of three morphologies, as Figure 5 shown.

[0050] The classification strategy of waveform morphological changes, whose judgment mechanism is based on extreme value calculation and curvature analysis within the signal segment, is used as the basis for judging whether there are significant extreme points and inflection points in the waveform. Specifically:

[0051] Taking the APG waveform as an example, based on the identified basic reference points, the trough b and diastolic peak e of the APG waveform, the signal segment is divided. The first-order point-by-point difference operation is performed on the signal segment, and the maximum and minimum values in the segment are calculated. If both extreme points exist, the waveform is classified into one category; if not, the second-order point-by-point difference operation is performed, the curvature of the signal segment is calculated and analyzed, and the curvature is used to judge whether there is an inflection point. If there is, the waveform is classified into the second category; if neither the extreme point nor the inflection point exists, it is classified into the third category.

[0052] 4) Identification of feature points of different categories: Based on the PPG waveforms and APG waveforms of different categories obtained by the waveform classification strategy described above, the feature point recognition methods that match the waveform characteristics are adaptively selected to realize the recognition of the N point and D point of the PPG waveform, and the c point and d point of the APG waveform.

[0053] The feature point recognition strategies of different categories are divided into two categories: the concave-convex point positioning method and the slope inversion method. Specifically:

[0054] (1) The concave-convex point positioning method is to fit a straight line between the b point and the e point in the APG waveform with a mathematical formula, and then traverse the data points on the left side of the signal segment. The point with the maximum distance to the straight line is the c point, and the point with the maximum distance on the right side is the d point.

[0055] (2) The slope inversion method: According to the signal from the b point to the e point of the divided segment, the first derivative (i.e., the slope of the waveform) is calculated to detect the change of the slope sign to realize the positioning of the feature points c and d. The point where the slope changes from positive to negative is the c point, and the point where the slope changes from negative to positive is the d point.

[0056] Schematic diagram of the recognition and positioning results of characteristic points of PPG and its derivative signals implemented based on the above steps is as follows Figure 6 shown

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the present technical solution, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A method for identifying and locating feature points of PPG and its derived signals, characterized in that: The method specifically comprises the following steps: S1: Signal preprocessing: Get the original PPG signal, use Butterworth low-pass filter and cubic spline interpolation method to remove high-frequency noise and baseline drift, and use cycle segmentation method to extract the waveform within the entire cycle range of the time domain; S2: Detection of basic reference points: Based on the full-cycle PPG signal in the time domain obtained in step S1, perform point-by-point differential operation on the waveform data of the PPG signal, calculate the first-order derivative function VPG signal and the second-order derivative function APG signal of the PPG signal respectively, and use the method of moving sliding window, threshold judgment and adaptive dynamic adjustment strategy to detect the basic reference points of the PPG, VPG and APG waveforms; S3: Waveform classification: Divide the signal segments based on the basic reference points identified in step S2, and implement effective classification of two different forms of PPG waveforms and three different forms of APG waveforms according to the classification strategy of waveform morphology changes in the signal segments; S4: Identification of feature points of different categories: Based on the classification strategy of waveform morphology changes in step S3, different categories of PPG waveforms and APG waveforms are obtained, and feature points matching the waveform characteristics are adaptively selected to identify the N point and D point of different PPG waveforms, as well as the c point and d point of different APG waveforms; wherein, the N point and the D point are the bisection notch point and the diastolic peak point of the PPG waveform, respectively; the c point is the late systolic increase wave of the APG waveform, and the d point is the late systolic attenuation wave of the APG waveform.

2. The method for feature point recognition and positioning according to claim 1, characterized in that: In step S2, the basic reference points include the peak S and trough O of the PPG waveform, the peak u and trough v of the VPG waveform, the peak a and trough b of the APG waveform, and the characteristic points of the peaks and troughs of each waveform mapped to other waveforms, namely, the maximum slope point E of the rising branch and the minimum slope point F of the falling branch of the PPG waveform, the zero crossing point L of the VPG waveform, the diastolic peak point w of the VPG waveform, and the late systolic point e of the APG waveform.

3. The method for feature point recognition and positioning according to claim 2, characterized in that: In step S2, the width value W of the moving sliding window is set according to the following formula: W=m*fs Wherein, fs is the signal sampling frequency, m is a coefficient, and its value range is 0.6 to 1.2; the preset amplitude threshold H is 0.6 to 0.8 times the maximum amplitude of the waveform data, and the peaks and troughs of the PPG, VPG and APG waveforms are identified; The adaptive dynamic adjustment strategy includes a threshold judgment of the peak interval and a low amplitude missed detection judgment mechanism.

4. The method for feature point recognition and positioning according to claim 3, characterized in that: In step S2, the threshold judgment of the peak interval is to identify the peaks and troughs of the PPG, VPG and APG signals by combining a moving sliding window with an initial threshold, and then calculate the peak interval T based on the peak S point of the PPG signal, and preset the threshold R1 of the main wave peak misjudgment mechanism of the VPG and APG waveforms to 0.6 to 0.8 times the average value of the peak interval T. If the peak interval is less than R1, it is confirmed that there is a peak misdetection. The low-amplitude missed detection judgment mechanism calculates the peak interval T based on the peak S point of the PPG waveform identified by combining the moving sliding window and the initial threshold, and presets the threshold R2 of the low-amplitude missed detection judgment mechanism of the VPG and APG waveforms to 1.5 to 1.7 times the average value of the peak interval T. If the current peak interval is greater than R2, it is considered that there is a low-amplitude peak missed detection in the middle.

5. The method for feature point recognition and positioning according to claim 1, characterized in that: In step S3, the judgment mechanism of the classification strategy of the waveform morphology change is based on extreme value calculation and curvature analysis within the signal segment to determine whether the waveform has extreme value points and inflection points as the basis for PPG and APG waveform classification; The two different forms of PPG waveforms are divided into two categories: one with obvious dicrotic waves and one without obvious dicrotic waves according to the waveform characteristics of points N and D; The three different forms of APG waveforms are divided into three categories according to the waveform characteristics of point c and point d: having significant point c and point d, having relatively obvious point c and point d, and having no clear point c and point d; The method for determining whether a waveform has extreme points and inflection points is to divide the signal segments based on the identified basic reference points. The signal segments are first subjected to a first point-by-point differential operation, and the maximum and minimum values ​​in the segments are calculated. If the two extreme points exist at the same time, the waveform is classified into one category. If not, a second point-by-point differential operation is performed to calculate and analyze the curvature of the signal segment, and the curvature is used to determine whether an inflection point exists. If so, the waveform is classified into the second category. If neither the extreme points nor the inflection points exist, the waveform is classified into the third category.

6. The method for feature point recognition and positioning according to claim 1, characterized in that: In step S4, different types of feature point recognition methods include concave-convex point positioning method and slope inversion method; The concave-convex point positioning method uses a mathematical formula to fit a straight line between two adjacent basic reference points, and calculates the point with the largest distance from the signal of the adjacent reference point to the straight line; wherein the point with the largest distance on the left is defined as a concave point, and the point with the largest distance on the right is defined as a convex point; The slope reversal method uses the first-order derivative of the signal segment, that is, the slope of the waveform, to detect the change in slope sign. The point where the slope changes from positive to negative or from negative to positive is called the slope reversal point, which is also the feature point to be located.

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