Arteriosclerosis evaluation system and method based on multi-stage oscillatory pulse wave feature extraction

Abstract

The application discloses a kind of based on multi-stage oscillation pulse wave feature extraction's arteriosclerosis evaluation system and method, wherein system includes pressure control module, signal acquisition module, signal storage module, arteriosclerosis diagnostic module and display module.Pressure control module controls to generate continuous air release stage and keep stage, signal acquisition module acquires the pulse oscillation wave of subject in air release stage and keep pressure state, signal storage module is used to record data, carry out analog-digital signal conversion, arteriosclerosis diagnostic module is used for pulse signal analysis and calculation, complete pulse signal preprocessing, pulse wave feature analysis and arterial elasticity function evaluation, display module is used to show subject information, waveform and the arteriosclerosis degree of final evaluation.The application can realize the collection and processing of subject pulse signal, non-invasive convenient, can accurately evaluate the arterial elasticity function of subject.

Description

Technical Field

[0001] This invention belongs to the field of signal detection and medical electronics technology, and in particular relates to an arteriosclerosis assessment system and method based on multi-stage oscillating pulse wave feature extraction. Background Technology

[0002] Cardiovascular and cerebrovascular diseases have become the leading cause of death and health threats worldwide. Due to factors such as unhealthy lifestyles, dietary habits, and an aging population, the prevalence of these diseases in my country continues to rise. Arteriosclerosis is the primary pathological basis for many cardiovascular diseases, leading to conditions such as hypertension, myocardial infarction, and aortic aneurysm. Therefore, early assessment and active intervention for abnormal arterial elasticity are crucial for the effective prevention and treatment of cardiovascular and cerebrovascular diseases. Currently, arteriosclerosis patients are increasingly younger, making the prevention and control of these diseases an urgent priority.

[0003] Currently, oscillometric blood pressure measurement has become a widely accepted and effective non-invasive method. This method uses a cuff to block arterial blood flow and detects the oscillating waves of gas within the cuff. These oscillating waves originate from fluctuations in the blood vessel wall and have a certain functional relationship with mean arterial pressure, diastolic pressure, systolic pressure, and vascular elasticity. Hypertension and arteriosclerosis are often closely related. The main clinical technique for evaluating arterial elasticity is arteriography, but this method requires patients to endure the discomfort of arterial puncture and carries risks such as radiation, contrast agent allergies, and contrast agent nephropathy, and is relatively expensive. A commonly used non-invasive method is pulse wave velocity (PWV) measurement, which assesses arterial elasticity by measuring the propagation speed of the pulse wave through two fixed points in the arterial system. Its disadvantages include a calculation formula that is closely related to the subject's height and weight, making it difficult to guarantee accuracy for individual measurements, and its complex operation, making it unsuitable for routine measurements. Recent studies have shown that arterial elasticity changes significantly long before the appearance of obvious stenosis or occlusion in the arterial lumen, and these changes trigger alterations in the characteristics of arterial pulses measured on the body surface. Compared to electrocardiogram (ECG) monitoring, pulse monitoring is simpler to operate. Its advantage lies in its ability to detect the degree of arteriosclerosis and blood pressure status. Arteriosclerosis is the primary pathological basis of cardiovascular disease, with hypertension accounting for nearly 90% of all cardiovascular disease patients. Early detection and intervention of arteriosclerotic lesions are fundamental measures to delay and control cardiovascular events. By analyzing pulse waveforms, hypertension and arteriosclerosis can be diagnosed promptly, especially in the early stages of cardiovascular disease risk, allowing for timely and effective treatment and intervention. This can effectively curb or even reverse the progression of cardiovascular disease, protect target organs, and prevent the occurrence of end-stage cardiovascular disease. Therefore, this study proposes an arteriosclerosis assessment system based on multi-stage oscillatory pulse wave feature extraction. This system measures pulse oscillatory wave signals at appropriate body sites for a suitable duration, ensuring comfort and ease of operation. Multiple feature parameters are then extracted to generate accurate arteriosclerosis detection results, assisting patients in the prevention of arteriosclerosis and having a positive impact on the treatment of cardiovascular diseases. Summary of the Invention

[0004] Addressing the shortcomings of existing arterial elasticity assessment technologies, this invention proposes a non-invasive and accurate arterial elasticity assessment system based on multi-stage oscillatory pulse wave feature extraction. This system can establish a convenient, accurate, and readily usable arterial elasticity assessment framework, providing important reference for the detection of early arteriosclerosis and the treatment of cardiovascular diseases.

[0005] To achieve the objectives of this invention, an arterial elasticity function assessment system based on multi-stage oscillating pulse wave feature extraction is provided, comprising a pressure control module, a signal acquisition module, a signal storage module, an arteriosclerosis diagnosis module, and a display module;

[0006] The pressure control module includes a pressure band, an inflation unit, and a deflation unit, used to generate a new pressure measurement process after the complete blood pressure measurement process, with a holding time of T seconds and a holding pressure of P mmHg, where T≥10 and P≤60; the pressure band is wrapped around the left upper arm, and the inflation and deflation units pressurize and depressurize the pressure band.

[0007] The signal acquisition module includes a signal acquisition unit and a pressure sensor, used to acquire the pulse signal of the subject during the deflation phase and under the pressure maintenance phase, with a sampling frequency of f Hz.

[0008] The signal storage module is connected to the arteriosclerosis diagnosis module, performs analog-to-digital signal conversion, and stores the raw array from the signal acquisition module and the evaluation results from the arteriosclerosis diagnosis module.

[0009] The display module displays basic information about the subject, including name, age, gender, systolic blood pressure, diastolic blood pressure, heart rate, etc., and displays measurement waveforms, evaluation results, and suggestions in real time.

[0010] The arteriosclerosis diagnosis module preprocesses the pulse signal to obtain a one-dimensional array after filtering out interference. This one-dimensional array is then divided into a deflation phase signal array and a maintenance phase signal array according to different stages. Time-domain analysis is performed on the deflation phase signal, and the element values ​​of the one-dimensional array are substituted into a predefined calculation formula to obtain a time-domain index. Pulse decomposition analysis is performed on the maintenance phase signal to obtain pulse wavelets representing physiological significance. Features are extracted from these pulse wavelets to obtain a pulse feature parameter array. This array is then weighted and fused to obtain an arterial elasticity function evaluation index. The time-domain index and the arterial elasticity function evaluation index are combined with factors such as age to obtain an assessment index. The degree of arteriosclerosis in the subject is determined based on this assessment index.

[0011] To achieve the objectives of this invention, a method for assessing arterial elastic function based on multi-stage oscillatory pulse wave feature extraction is provided, comprising the following steps:

[0012] ① The collected pulse signals are preprocessed, including noise removal, baseline drift removal, and normalization.

[0013] ② Peak point identification is performed on the processed deflation phase signal to obtain the amplitude envelope. Threshold analysis is performed on the amplitude envelope to calculate the pressure difference (ASI). The normalized single pulse wave under the holding pressure is decomposed into approximately three log-normal function waves, and the analytical wavelet feature parameters of the log-normal function are extracted. According to the age and gender of the subject, the time-domain features and the analytical wavelet feature parameters are weighted and fused to obtain multiple arterial elasticity function evaluation indicators: PASI, P, Y, PP, YY, H, I, and Q.

[0014] ③ Assess arterial elasticity by using evaluation indicators combined with threshold judgments, determining the category according to the characteristics of analytical wavelets, and obtaining the assessment results of arteriosclerosis;

[0015] Further, the pulse signal is preprocessed by using a bandpass filter to remove low-frequency interference and high-frequency noise, with cutoff frequencies set at 0.3Hz and 15Hz, generating the original data array X1 for the deflation stage and the original data array X2 for the holding stage. The one-dimensional array X1 is then used to identify the minimum value within a window of width H, where H ≈ f / 8 (H is an integer). The minimum value of each window is calculated, and the three adjacent minimum values ​​are compared sequentially. If the middle value is a local minimum, the data at the left and right ends are discarded, and the value of the local minimum is recorded. This process is repeated. The position of the local minimum in the baseline-de-baseline data is found to be the starting point of each pulse signal in the deflation stage. The baseline data L obtained by cubic spline interpolation using the starting point is eliminated, resulting in the baseline-de-baseline data R1. The one-dimensional array X2 is then used to identify the minimum value within a window of width H, where H ≈ f / 6 (H is an integer). The minimum value at the corresponding window position is then calculated, and the three adjacent minimum values ​​are compared sequentially. If the middle value is a local minimum, the data at the left and right ends are discarded, and the value of the local minimum is recorded. The data is recorded sequentially. i =XM n Let S = {s1, s2, ..., s} t}, then s i To maintain the data corresponding to the starting point of each pulse signal in the phase, search for s i The corresponding position in X2 is the starting point Q of each pulse signal during the hold phase. i s i =x Qi Q = {Q1, Q2, ..., Q} t}; for the starting point (Q) i s i Perform cubic spline interpolation to obtain the baseline curve, then use the original data to eliminate the baseline data to obtain the baseline-removed data R2.

[0016] Furthermore, feature extraction and analysis are performed on the pulse signal. The maximum amplitude RRM is found in the baseline-debasement data, and the baseline-debasement data is segmented into individual pulse wave signals R according to the position of the starting point. i RM identification of the amplitude of a single pulse wave signal i The amplitude points are connected sequentially to obtain the amplitude envelope; a reference point of 50% RRM is found on the amplitude envelope to obtain the characteristic parameter ASI for time domain analysis.

[0017] The set calculation formula includes: RRM = max{R}, RM i =max{R i}, ASI = P l -P r ;

[0018] Among them, P l and P r This represents the value corresponding to the reference point in the baseline data L.

[0019] The normalized pulse wave F(x) is fitted using three asymmetric log-normal functions f1(x), f2(x), and f3(x). The analytical wavelet characteristic parameters of the log-normal functions are iteratively derived using particle swarm optimization, with upper and lower limits defined for nine parameters, until the fitting accuracy (MAE) is less than 2%. The analytical wavelet characteristic indices include the wavelet poles P. k Wavelet extreme point Y k Peak value compared to YY l PP during peak period l .

[0020] Specifically, the upper and lower limits of the nine parameters are 1 and 0.01, respectively.

[0021] The log-normal function is: (k=1,2,3), (x=1,2,···,1000);

[0022] The set calculation formula includes: This represents the average absolute error of the sampling.

[0023] Furthermore, the indicators obtained by weighted fusion processing based on the age and gender of the subjects include the characteristic parameter PASI, peak interval H, decay index I, and offset Q.

[0024] The set calculation formula includes:

[0025] PASI=(μ1×log α Age+μ2)×ASI; H=(P2-P1)×log 50 Age;

[0026]

[0027] Where Age represents the subject's age, and μ1 and μ2 are weighting coefficients. When the subject is male, μ1 = 0.65, μ2 = 0.35, and α = 50; when the subject is female, μ1 = 0.75, μ2 = 0.25, and α = 45.

[0028] Furthermore, arterial elasticity function is assessed, and the obtained characteristic parameter PASI is used for threshold judgment. When the index is detected to be less than the first index threshold, the subject's vascular hardening degree is assessed as healthy; when the index is detected to be greater than the second index threshold, the subject's vascular hardening degree is assessed as hard; when the index is detected to be between the first and second index thresholds, the subject's vascular hardening degree is assessed as moderate. The pulse analysis waveform is classified using the peak-to-peak interval YY1. If the detected index is less than or equal to the third index threshold, it is classified into the first category; if the detected index is between the third and fourth index thresholds, it is classified into the second category; if the detected index is greater than or equal to the fourth index threshold, it is classified into the third category. The pulse waveform of the first category is evaluated using the analytical wavelet feature parameter I. If the feature parameter detects that the index is less than or equal to the fifth index threshold, the subject's vascular sclerosis is assessed as healthy; if the detected index is greater than or equal to the sixth index threshold, the subject's vascular sclerosis is assessed as hard; if the detected index is between the fifth and sixth index thresholds, the subject's vascular sclerosis is assessed as moderate. In the same manner, the seventh and eighth thresholds are selected, and the offset degree Q is used to evaluate the pulse waveform of the second category. In the same manner, the ninth and tenth thresholds are selected, and the peak-to-peak interval H is used to evaluate the pulse waveform of the third category.

[0029] The threshold values ​​for the first to the tenth indicators are 50, 64, 0.85, 1.1, 0.52, 0.65, 0.6, 0.7, 180, and 190, respectively.

[0030] Compared with the prior art, the beneficial effects provided by the present invention include:

[0031] 1. The arteriosclerosis assessment system of the present invention realizes non-invasive assessment of arterial elastic function and provides real-time arteriosclerosis assessment results, providing important reference for the early detection of arteriosclerosis and the treatment of cardiovascular diseases;

[0032] 2. The arteriosclerosis assessment system based on multi-stage oscillatory pulse wave feature extraction of the present invention can be applied to a variety of scenarios. The measurement method adopts the widely used oscillometric method, and the analysis method adopts both time domain analysis and pulse analysis method. It overcomes the shortcomings of traditional single evaluation methods in certain application scenarios and has high stability in various scenarios. It can efficiently and accurately detect the arterial elasticity status of various users.

[0033] 3. The arteriosclerosis assessment method based on multi-stage oscillatory pulse wave feature extraction of the present invention has easy pulse feature extraction, low algorithm complexity, strong compatibility, and can effectively avoid problems such as limited computing power.

[0034] 4. The arteriosclerosis assessment method based on multi-stage oscillatory pulse wave feature extraction of the present invention combines age and gender with feature parameters, reducing the impact of different ages and genders on the accuracy of arteriosclerosis assessment, and further improving the accuracy of the assessment algorithm. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the arteriosclerosis detection system based on multi-stage oscillating pulse wave feature extraction according to the present invention.

[0036] Figure 2 This is a flowchart of the arteriosclerosis diagnosis module in the arteriosclerosis detection system based on multi-stage oscillatory pulse wave feature extraction of the present invention.

[0037] Figure 3 This is a diagram showing the effect of pulse wave extraction based on the oscilloscope method in this invention.

[0038] Figure 4 This is a diagram showing the effect of pulse analysis using three log-normal functions in this invention.

[0039] Figure 5 This is an example diagram of a report showing an arterial elasticity assessment grade of "C" according to the present invention. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0041] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments.

[0042] Example 1:

[0043] refer to Figure 1 As shown, Figure 1 This is a schematic diagram of the arteriosclerosis detection system based on multi-stage oscillating pulse wave feature extraction in this embodiment. It includes a pressure control module 11, a signal acquisition module 12, a signal storage module 13, an arteriosclerosis diagnosis module 14, and a display module 15. The pressure control module 11 sets the holding pressure value. The signal acquisition module 12 acquires the pulse signal of the subject during the deflation and holding phases. The acquired pulse signal enters the signal storage module 13. After analog-to-digital signal conversion by the signal storage module 13, it enters the arteriosclerosis diagnosis module 14 for pulse signal analysis and arteriosclerosis assessment. Finally, the display module 15 displays the waveform and the assessed degree of arteriosclerosis and sends the assessment result to the signal storage module 13.

[0044] The pressure control module 11 includes a pressure band, an inflation unit, and a deflation unit, used to generate a new pressure measurement process after the complete blood pressure measurement process, with a holding time of T seconds and a holding pressure of P mmHg, where T≥10 and P≤60; the pressure band is wrapped around the left upper arm, and the inflation unit and deflation unit pressurize and depressurize the pressure band.

[0045] The signal acquisition module 12 includes a signal acquisition unit and a pressure sensor, used to acquire the pulse signal of the subject during the deflation phase and under the pressure maintenance phase, with a sampling frequency of f Hz and a sampling time of t seconds.

[0046] The signal storage module 13 is connected to the arteriosclerosis diagnosis module 14, performs analog-to-digital signal conversion, and stores the raw array from the signal acquisition module 12 and the evaluation results from the arteriosclerosis diagnosis module 14.

[0047] The display module 15 displays basic information about the subject, including name, age, gender, systolic blood pressure, diastolic blood pressure, heart rate, etc., and displays measurement waveforms, evaluation results, and suggestions in real time.

[0048] The arteriosclerosis diagnosis module 14 preprocesses the pulse signal to obtain a one-dimensional array after filtering out interference; the one-dimensional array is divided into a deflation phase signal array and a maintenance phase signal array according to different stages; time-domain analysis is performed on the deflation phase signal, and the element values ​​of the one-dimensional array are substituted into a set calculation formula to calculate and obtain time-domain indicators; pulse decomposition analysis is performed on the maintenance phase signal to obtain pulse wavelets that represent physiological significance, and the pulse wavelets are feature-extracted to obtain a pulse feature parameter array; the pulse feature parameter array is weighted and fused to obtain an arterial elasticity function evaluation index; the time-domain index and the arterial elasticity function evaluation index are combined with factors such as age to obtain an assessment index, and the degree of arteriosclerosis of the subject is determined according to the assessment index.

[0049] Example 2:

[0050] This embodiment provides a method for assessing arteriosclerosis based on multi-stage oscillatory pulse wave feature extraction, such as... Figure 2 As shown, it includes the following steps:

[0051] ① The collected pulse signals are preprocessed, including noise removal, baseline drift removal, and normalization.

[0052] ② Peak point identification is performed on the processed deflation phase signal to obtain the amplitude envelope. Threshold analysis is performed on the amplitude envelope to calculate the pressure difference (ASI). The normalized single pulse wave under the holding pressure is decomposed into approximately three log-normal function waves, and the analytical wavelet feature parameters of the log-normal function are extracted. According to the age and gender of the subject, the time-domain features and the analytical wavelet feature parameters are weighted and fused to obtain multiple arterial elasticity function evaluation indicators: PASI, P, Y, PP, YY, H, I, and Q.

[0053] ③ Assess arterial elasticity by using evaluation indicators combined with threshold judgments, determining the category according to the characteristics of analytical wavelets, and obtaining the assessment results of arteriosclerosis;

[0054] Furthermore, the preprocessing of the pulse signal is described in detail: a bandpass filter is used to remove low-frequency interference and high-frequency noise, with cutoff frequencies set at 0.3Hz and 15Hz, generating the original data array X1 for the deflation stage and the original data array X2 for the holding stage. The one-dimensional array X1 is used to identify the minimum value within a window of width H, where H ≈ f / 8, and H is an integer. The minimum value of each window is calculated, and the three adjacent minimum values ​​are compared sequentially. If the middle value is a local minimum, the data at the left and right ends are discarded, and the value of the local minimum is recorded. This process is repeated. The position of the local minimum in the baseline-de-baseline data is found to be the starting point of each pulse signal in the deflation stage. The baseline data L obtained by cubic spline interpolation using the starting point is eliminated, resulting in the baseline-de-baseline data R1. The one-dimensional array X2 is used to identify the minimum value within a window of width H, where H ≈ f / 6, and H is an integer. Then, the minimum value at the corresponding window position is calculated, and the three adjacent minimum values ​​are compared sequentially. If the middle value is a local minimum, the data at the left and right ends are discarded, and the value of the local minimum is recorded. The values ​​are recorded sequentially as follows: i =XM n Let S = {s1, s2, ..., s} t}, then s i To maintain the data corresponding to the starting point of each pulse signal in the phase, search for s i The corresponding position in X2 is the starting point Q of each pulse signal during the hold phase. i s i =xQi Q = {Q1, Q2, ..., Q} t}; for the starting point (Q) i s i Perform cubic spline interpolation to obtain the baseline curve, then use the original data to eliminate the baseline data to obtain the baseline-removed data R2.

[0055] Furthermore, the feature extraction and analysis of the pulse signal are described in detail: The maximum amplitude value RRM = max{R} is found in the baseline-removed data, and the baseline-removed data is segmented into individual pulse wave signals R according to the position of the starting point. i RM identification of the amplitude of a single pulse wave signal i =max{R i Connecting the amplitude points sequentially yields the amplitude envelope; a reference point of 50% RRM is found on the amplitude envelope, yielding the characteristic parameter ASI = P for time-domain analysis. l -P r P l and P r This represents the value corresponding to the reference point in the baseline data L.

[0056] The normalized pulse wave F(x) is fitted using three asymmetric log-normal functions f1(x), f2(x), and f3(x). The analytical wavelet characteristic parameters of the log-normal functions are iteratively derived using particle swarm optimization, with upper and lower limits defined for nine parameters, until the fitting accuracy (MAE) is <2%. The analytical wavelet characteristic indices include wavelet poles. Wavelet extreme points Peak ratio YY l PP during peak period l .

[0057] Specifically, the upper and lower limits of the nine parameters are 1 and 0.01, respectively.

[0058] The log-normal function is: (k=1,2,3), (x=1,2,···,1000);

[0059] The set calculation formula includes:

[0060] This represents the average absolute error of the sampling.

[0061] PP1 = P2 - P1;

[0062] Furthermore, the index obtained by weighted fusion processing of subject age and gender includes the feature parameter PASI = (μ1 × log α Age + μ2) × ASI, peak interval H = (P2 - P1) × logα Age, decay index and degree of offset Age represents the subject's age, and μ1 and μ2 are weighting coefficients. When the subject is male, μ1 = 0.65, μ2 = 0.35, and α = 50; when the subject is female, μ1 = 0.75, μ2 = 0.25, and α = 45.

[0063] Furthermore, the arterial elasticity function is assessed using the obtained characteristic parameter PASI. When the indicator is detected to be less than a first indicator threshold, the subject's vascular sclerosis is assessed as healthy; when the indicator is detected to be greater than a second indicator threshold, the subject's vascular sclerosis is assessed as hard; and when the indicator is detected to be between the first and second indicator thresholds, the subject's vascular sclerosis is assessed as moderate.

[0064] The pulse analysis waveform is classified using the peak-to-peak interval YY1. If the detected index is less than or equal to the third index threshold, it is classified into the first category; if the detected index is between the third and fourth index thresholds, it is classified into the second category; if the detected index is greater than or equal to the fourth index threshold, it is classified into the third category. The pulse waveform of the first category is evaluated using the analytical wavelet feature parameter I. If the feature parameter detects that the index is less than or equal to the fifth index threshold, the subject's vascular sclerosis is assessed as healthy; if the detected index is greater than or equal to the sixth index threshold, the subject's vascular sclerosis is assessed as hard; if the detected index is between the fifth and sixth index thresholds, the subject's vascular sclerosis is assessed as moderate. In the same manner, the seventh and eighth thresholds are selected, and the offset degree Q is used to evaluate the pulse waveform of the second category. In the same manner, the ninth and tenth thresholds are selected, and the peak-to-peak interval H is used to evaluate the pulse waveform of the third category.

[0065] Specifically, the threshold values ​​for the first to the tenth indicators are selected as 50, 64, 0.85, 1.1, 0.52, 0.65, 0.6, 0.7, 180, and 190, respectively.

[0066] Furthermore, the evaluation results obtained from two different analysis methods are used to determine the result: if both results are "healthy", the result is "A"; if both results are "hard", the result is "E"; if both results are "healthy" and "fair", the result is "B"; if both results are "fair", the result is "C"; if both results are "fair" and "hard", the result is "D"; if both results are "healthy" and "hard", the result is "O", and the result is invalid.

[0067] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0068] It should be noted that the terms "first," "second," and "third" used in the embodiments of this invention are merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first," "second," and "third" can be interchanged in a specific order or sequence where permissible. It should be understood that the objects distinguished by "first," "second," and "third" can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in an order other than those illustrated or described herein.

[0069] The terms "comprising" and "having," and any variations thereof, in this embodiment of the invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or modules is not limited to the steps or modules listed, but may optionally include steps or modules not listed, or may optionally include other steps or modules inherent to such processes, methods, products, or devices.

[0070] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. An arteriosclerosis assessment system based on multi-stage oscillatory pulse wave feature extraction, characterized in that, It includes a pressure control module, a signal acquisition module, a signal storage module, an arteriosclerosis diagnosis module, and a display module; The pressure control module includes a cuff, an inflation unit, and a deflation unit, used to generate a new pressure measurement process that maintains a pressure of P mmHg for a duration of T seconds after the complete blood pressure measurement process. ≥ 10, P ≤ 60; A pressure band is wrapped around the left upper arm, and an inflation unit and a deflation unit pressurize and depressurize the pressure band; The signal acquisition module includes a signal acquisition unit and a pressure sensor, used to acquire the pulse signal of the subject during the deflation and holding phases, with a sampling frequency of f Hz. The signal storage module is connected to the arteriosclerosis diagnosis module, performs analog-to-digital signal conversion, and stores the raw array from the signal acquisition module and the evaluation results from the arteriosclerosis diagnosis module. The display module displays the subject's basic information, including name, age, gender, systolic blood pressure, diastolic blood pressure, and heart rate, and displays the measurement waveform, assessment results, and suggestions in real time. The arteriosclerosis diagnosis module preprocesses the pulse signal to obtain a one-dimensional array after filtering out interference. This one-dimensional array is then divided into a deflation phase signal array and a maintenance phase signal array according to different stages. Time-domain analysis is performed on the deflation phase signal, and the element values ​​of the deflation phase signal array are substituted into a predefined calculation formula to obtain time-domain indices. Pulse decomposition analysis is performed on the maintenance phase signal to obtain pulse wavelets representing physiological significance. Features are extracted from these pulse wavelets to obtain a pulse feature parameter array. This array is then weighted and fused to obtain an arterial elasticity function assessment index. The time-domain index and the arterial elasticity function assessment index are combined with age factors to obtain an assessment index. The degree of arteriosclerosis in the subject is determined based on this assessment index.

2. The arteriosclerosis assessment system based on multi-stage oscillatory pulse wave feature extraction according to claim 1, characterized in that, The pressure control module sets the holding pressure value, and the signal acquisition module collects the pulse signals of the subject during the deflation and holding phases. The collected pulse signals enter the signal storage module, and after analog-to-digital signal conversion, they enter the arteriosclerosis diagnosis module for pulse signal analysis and arteriosclerosis assessment. Finally, the display module displays the waveform and the assessed degree of arteriosclerosis and sends the assessment results to the signal storage module.

3. The arteriosclerosis assessment system based on multi-stage oscillatory pulse wave feature extraction according to claim 2, characterized in that, The evaluation method for the system includes the following steps: ① The collected pulse signals are preprocessed, including noise removal, baseline drift removal, and normalization. ② Peak points are identified in the processed deflation phase signal to obtain the amplitude envelope. Threshold analysis is performed on the amplitude envelope to calculate the time-domain feature, i.e., the pressure difference (ASI). The normalized single pulse wave in the maintenance phase is decomposed into approximately three log-normal function waves. The analytical wavelet feature parameters of the log-normal function are extracted to obtain the arterial elasticity function assessment index in the maintenance phase. Based on the age and gender of the subjects, the time-domain features and analytical wavelet feature parameters are weighted and fused to obtain multiple assessment indicators. ③ Assess the arterial elasticity function, and obtain the arteriosclerosis assessment results based on the assessment index thresholds and the analysis of wavelet characteristic categories.

4. The arteriosclerosis assessment system based on multi-stage oscillatory pulse wave feature extraction according to claim 3, characterized in that, The method used for preprocessing the pulse signal is as follows: a bandpass filter is used to remove low-frequency interference and high-frequency noise, and the cutoff frequency is set at 0.3Hz and 15Hz to generate the original data array X1 for the deflation stage and the original data array X2 for the holding stage. The one-dimensional array X1 is used to identify the minimum value of a window with a width of H, where H ≈ f / 8 and H is an integer. The minimum value of each window is calculated, and the three adjacent minimum values ​​are compared in turn. If the middle value is the minimum value, the data at the left and right ends are discarded and the value of the minimum value is recorded. This process is repeated. The position of the minimum value in the baseline-de-baseline data is the starting point of each pulse signal during the deflation phase. The baseline data L after cubic spline interpolation using the starting point is eliminated to obtain the baseline-de-baseline data R1. Minimize the one-dimensional array X2 according to the window of width H, where H≈f / 6 and H is an integer; calculate the minimum value of each window, and compare the three adjacent minimum values ​​obtained in turn. If the middle value is the minimum value, discard the data at the left and right ends and record the value of the minimum value, and so on. The location of the minimum value in the baseline-de-baseline data is the starting point of each pulse signal in the hold phase. The baseline data L, which is obtained by cubic spline interpolation using the starting point, is then eliminated to obtain the baseline-de-baseline data R2.

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