Pulse wave monitoring method and system based on telemetering photoplethysmography

Abstract

The invention discloses a pulse wave monitoring method and system based on telemetry photoplethysmography, and the method comprises the steps: collecting a video file of a carotid artery region based on telemetry photoplethysmography, obtaining a plurality of frames of images in the video file, and determining a gray level image corresponding to each frame of image; acquiring two regions of interest in the grayscale image, and extracting two original pulse wave signals from the two regions of interest; performing zero phase shift filtering and interpolation processing on the two original pulse wave signals to obtain two processed pulse wave signals; performing normalization processing and cross-correlation calculation on the two processed pulse wave signals to obtain a time difference between the two pulse wave signals; according to the time difference of the two pulse wave signals and the distance between the two interested areas, the pulse wave speed is calculated, and the arteriosclerosis degree is evaluated based on the pulse wave speed. The carotid artery pulse wave is monitored in a non-invasive mode, arteriosclerosis is evaluated, and cardiovascular health is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of biomedical signal processing, and in particular to a pulse wave monitoring method and system based on telemetry photoplethysmography. Background Art

[0002] In the healthcare industry, arteriosclerosis is one of the key factors of cardiovascular disease, and its detection usually relies on traditional methods such as vascular ultrasound and angiography. Although these methods are highly accurate, they require the use of expensive professional equipment and are operated by professionals, making them difficult to be widely promoted in home health monitoring scenarios. In addition, most existing arteriosclerosis assessment methods require the use of large medical equipment or invasive operations, which not only limits their application in clinical settings, but also is not conducive to daily health monitoring for individuals.

[0003] Therefore, the prior art still needs to be improved and enhanced. Summary of the invention

[0004] The technical problem to be solved by the present invention is that, in view of the above-mentioned defects of the prior art, a pulse wave monitoring method and system based on telemetric photoplethysmography is provided, aiming to solve the problems that traditional detection methods are expensive, complicated to operate and mostly invasive examinations, making them difficult to be widely used in daily health monitoring of families and individuals.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0006] In a first aspect, the present invention provides a pulse wave monitoring method based on telemetry photoplethysmography, wherein the method comprises:

[0007] Based on telemetry photoplethysmography, a video file of the carotid artery region is collected, several frames of images in the video file are obtained, and a grayscale image corresponding to each frame of the image is determined;

[0008] Acquire two regions of interest in the grayscale image, and extract two original pulse wave signals from the two regions of interest;

[0009] Performing zero-phase-shift filtering and interpolation processing on the two original pulse wave signals to obtain two processed pulse wave signals;

[0010] Performing normalization processing and cross-correlation calculation on the two processed pulse wave signals to obtain a time difference between the two pulse wave signals;

[0011] The pulse wave velocity is calculated according to the time difference between the two pulse wave signals and the distance between the two regions of interest, and the degree of arteriosclerosis is evaluated based on the pulse wave velocity.

[0012] In one implementation, the step of performing zero-phase-shift filtering on the two original pulse wave signals includes:

[0013] Processing the two original pulse wave signals using a low-pass filter to remove high-frequency noise in the two original pulse wave signals;

[0014] Perform forward filtering on the two original pulse wave signals processed by the low-pass filter;

[0015] The two original pulse wave signals after forward filtering are reversely filtered to achieve zero phase shift filtering of the two original pulse wave signals.

[0016] In one implementation, the normalizing and cross-correlation calculation of the two processed pulse wave signals to obtain the time difference of the two pulse wave signals includes:

[0017] performing normalization processing on the two processed pulse wave signals;

[0018] Performing cross-correlation calculation on the two normalized pulse wave signals to obtain a cross-correlation curve of the two pulse wave signals;

[0019] The cross-correlation calculation is implemented based on the cross-correlation function, which is R(τ):

[0020]

[0021] Wherein τ is the time delay, x(t) and y(t) are the two pulse wave signals after the normalization processing;

[0022] Obtain the maximum value of the ordinate on the cross-correlation curve, and obtain the corresponding abscissa value τ based on the maximum value of the ordinate max ;

[0023] Get the preset sampling rate as f s , then based on the sampling rate f s and the abscissa value τ max , determine the time difference between the two pulse wave signals as

[0024] In one implementation, the calculating the pulse wave velocity according to the time difference between the two pulse wave signals and the distance between the two regions of interest includes:

[0025] Acquire the time difference Δt between the two pulse wave signals;

[0026] Get the distance Δd between two regions of interest;

[0027] Based on the time difference and the distance, the pulse wave velocity is calculated

[0028] In one implementation, the assessing the degree of arteriosclerosis based on the pulse wave velocity includes:

[0029] Acquiring a mapping relationship between the pulse wave velocity and the Young's modulus of the arterial wall;

[0030] Based on the mapping relationship and the pulse wave velocity, determining the Young's modulus of the arterial wall corresponding to the pulse wave velocity;

[0031] Based on the Young's modulus of the arterial wall, the degree of arteriosclerosis was evaluated.

[0032] In one implementation, the pulse wave monitoring method based on telemetry photoplethysmography further includes:

[0033] The effectiveness of the telemetry photoplethysmography in pulse wave monitoring was verified by measuring heart rate values.

[0034] In one implementation, verifying the effectiveness of the telemetry photoplethysmography in pulse wave monitoring by measuring the heart rate includes:

[0035] Performing Fourier transform on the two original pulse wave signals to obtain a peak frequency in the range of 0.8 Hz to 1.3 Hz to obtain a first heart rate value;

[0036] Calculating the average peak distance of the two original pulse wave signals after zero-phase-shift filtering and interpolation processing to obtain a second heart rate value;

[0037] If the difference between the first heart rate value and the second heart rate value is less than a preset threshold, it is determined that the effectiveness of the telemetry photoplethysmography in pulse wave monitoring is verified.

[0038] In a second aspect, an embodiment of the present invention further provides a pulse wave monitoring system based on telemetry photoplethysmography, wherein the system comprises:

[0039] A grayscale image acquisition module is used to collect a video file of the carotid artery region based on telemetry photoplethysmography, obtain a number of frames of images in the video file, and determine a grayscale image corresponding to each frame of the image;

[0040] An original pulse wave signal acquisition module is used to acquire two regions of interest in the grayscale image and extract two original pulse wave signals from the two regions of interest;

[0041] A processing module, used for performing zero-phase-shift filtering and interpolation processing on the two original pulse wave signals to obtain two processed pulse wave signals;

[0042] A time difference acquisition module, used for performing normalization processing and cross-correlation calculation on the two processed pulse wave signals to obtain the time difference between the two pulse wave signals;

[0043] The pulse wave velocity calculation and evaluation module is used to calculate the pulse wave velocity according to the time difference between the two pulse wave signals and the distance between the two regions of interest, and to evaluate the degree of arteriosclerosis based on the pulse wave velocity.

[0044] In a third aspect, an embodiment of the present invention further provides a terminal, wherein the terminal includes a memory, a processor, and a pulse wave monitoring program based on telemetric photoplethysmography stored in the memory and capable of running on the processor, and when the processor executes the pulse wave monitoring program based on telemetric photoplethysmography, the steps of the pulse wave monitoring method based on telemetric photoplethysmography of any one of the above-mentioned schemes are implemented.

[0045] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein a pulse wave monitoring program based on telemetric photoplethysmography is stored on the computer-readable storage medium, and when the pulse wave monitoring program based on telemetric photoplethysmography is executed by a processor, the steps of the pulse wave monitoring method based on telemetric photoplethysmography described in any one of the above-mentioned schemes are implemented.

[0046] Beneficial effects: The present invention provides a pulse wave monitoring method based on telemetry photoplethysmography. Compared with the prior art, the present invention first collects video files of the carotid artery area based on telemetry photoplethysmography, obtains high-quality image data in a non-contact manner, avoids discomfort to the subject, and can monitor the changes in the pulse wave in real time and continuously. Subsequently, several frames of images in the video file are obtained, and the grayscale image corresponding to each frame of the image is determined to reduce the computational complexity in the image processing process and improve the processing efficiency. Next, two regions of interest in the grayscale image are obtained, and two original pulse wave signals are extracted from the two regions of interest. This selection process utilizes the information of the specific position and improves the accuracy and reliability of the signal. Then, the two original pulse wave signals are subjected to zero-phase shift filtering and interpolation processing to obtain two processed pulse wave signals. The zero-phase shift filtering and interpolation processing can effectively remove noise interference while ensuring that the signal is not distorted, thereby obtaining a purer pulse wave signal. Afterwards, the two processed pulse wave signals are normalized and cross-correlated to obtain the time difference between the two pulse wave signals. This step helps to eliminate the impact of signal strength differences and ensure the accuracy of time difference measurement. Finally, the pulse wave velocity is calculated based on the time difference between the two pulse wave signals and the distance between the two regions of interest, and the degree of arteriosclerosis is evaluated based on the pulse wave velocity. This step accurately quantifies the changes in arterial wall elasticity based on the pulse wave velocity, provides an important quantitative indicator for early diagnosis and intervention of arteriosclerosis, and helps prevent and manage cardiovascular diseases. The method proposed in the present invention non-invasively collects carotid artery area videos, accurately extracts and processes pulse wave signals, and calculates pulse wave velocity to evaluate the degree of arteriosclerosis. It has the advantages of simple operation, high accuracy, and patient-friendliness, and provides a powerful tool for early assessment of cardiovascular health. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 The present invention provides a flowchart of a specific implementation of a pulse wave monitoring method based on telemetry photoplethysmography.

[0048] Figure 2 A flowchart of performing zero phase shift filtering in a pulse wave monitoring method based on telemetry photoplethysmography provided in an embodiment of the present invention.

[0049] Figure 3 A flow chart of obtaining the time difference in the pulse wave monitoring method based on telemetry photoplethysmography provided in an embodiment of the present invention.

[0050] Figure 4 A flow chart for evaluating the degree of arteriosclerosis in a pulse wave monitoring method based on telemetry photoplethysmography provided in an embodiment of the present invention.

[0051] Figure 5 It is a principle block diagram of a pulse wave monitoring system based on telemetry photoplethysmography provided by an embodiment of the present invention.

[0052] Figure 6 This is a block diagram of the internal structure principle of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and effect of the present invention clearer and more specific, the present invention is further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0054] In the healthcare field, arteriosclerosis is a key indicator of cardiovascular disease, and its detection has traditionally relied on methods such as vascular ultrasound and angiography. Although these methods provide highly accurate results, they require the use of expensive professional equipment and must be operated by professionally trained technicians, making them difficult to popularize in home health monitoring environments. In addition, most existing methods for assessing arteriosclerosis require the use of large medical equipment or invasive measures, which not only limits their convenient application in clinical settings, but also is not conducive to individuals conducting regular self-health monitoring.

[0055] In order to solve the above problems, this embodiment provides a pulse wave monitoring method based on telemetry photoplethysmography. In specific implementation, this embodiment first collects video files of the carotid artery area based on telemetry photoplethysmography, obtains high-quality image data in a non-contact manner, avoids discomfort to the subject, and can monitor the changes of the pulse wave in real time and continuously. Subsequently, several frames of images in the video file are obtained, and the grayscale image corresponding to each frame of the image is determined to reduce the computational complexity in the image processing process and improve the processing efficiency. Next, two regions of interest in the grayscale image are obtained, and two original pulse wave signals are extracted from the two regions of interest. This selection process utilizes the information of a specific position and improves the accuracy and reliability of the signal. Then, the two original pulse wave signals are subjected to zero-phase shift filtering and interpolation processing to obtain two processed pulse wave signals. The zero-phase shift filtering and interpolation processing can effectively remove noise interference while ensuring that the signal is not distorted, thereby obtaining a purer pulse wave signal. Afterwards, the two processed pulse wave signals are normalized and cross-correlated to obtain the time difference between the two pulse wave signals. This step helps to eliminate the impact of signal strength differences and ensure the accuracy of time difference measurement. Finally, the pulse wave velocity is calculated based on the time difference between the two pulse wave signals and the distance between the two regions of interest, and the degree of arteriosclerosis is evaluated based on the pulse wave velocity. This step accurately quantifies the changes in arterial wall elasticity based on the pulse wave velocity, provides an important quantitative indicator for early diagnosis and intervention of arteriosclerosis, and helps prevent and manage cardiovascular diseases. The method proposed in the present invention non-invasively collects carotid artery area videos, accurately extracts and processes pulse wave signals, and calculates pulse wave velocity to evaluate the degree of arteriosclerosis. It has the advantages of simple operation, high accuracy, and patient-friendliness, and provides a powerful tool for early assessment of cardiovascular health.

[0056] For example, suppose a user wants to regularly monitor his arteriosclerosis at home to prevent cardiovascular disease. In this case, the user can use the method proposed by the present invention. First, based on telemetry photoplethysmography, a video file of the user's carotid artery area is collected. The non-contact video collection method can make the user feel comfortable and avoid the discomfort that may be caused by traditional detection methods. Subsequently, several frames of images in the video file of the user's carotid artery area are obtained, and the grayscale image corresponding to each frame of the image is determined. This step reduces the computing resources required for subsequent analysis and improves the analysis speed. Next, two regions of interest in the grayscale image are obtained, and two original pulse wave signals of the user are extracted from the two regions of interest. By selecting a specific position, the quality and accuracy of the signal can be ensured. Then, the two original pulse wave signals are subjected to zero-phase shift filtering and interpolation processing to remove background noise and maintain the true characteristics of the signal, thereby obtaining a purer pulse wave signal. Afterwards, the two processed pulse wave signals are normalized and cross-correlated to eliminate the influence of signal intensity differences and accurately measure the time difference between the two signals. Finally, based on the calculated time difference and the actual distance between the two regions of interest, the user's pulse wave velocity is accurately calculated, and the user's degree of arteriosclerosis is assessed accordingly. The entire process is not only easy to operate and highly accurate, but also non-invasive, which improves patient acceptance. In this way, users can easily complete the assessment of arteriosclerosis at home, identify potential cardiovascular risk factors in a timely manner, and take appropriate preventive measures, which is crucial for the early prevention and management of cardiovascular diseases.

[0057] This embodiment provides a pulse wave monitoring method based on telemetry photoplethysmography, which can be applied to smart terminals, such as Figure 1 As shown in , the specific steps include:

[0058] Step S100: Based on telemetry photoplethysmography, a video file of the carotid artery region is collected, a plurality of frames of images in the video file are obtained, and a grayscale image corresponding to each frame of image is determined.

[0059] In this embodiment, first, based on telemetry photoplethysmography, a high-resolution camera equipped with a laser transmitter of a specific wavelength is used to non-contactly collect video files of the carotid artery area, and high-quality image data is obtained in a non-contact manner, avoiding discomfort to the subject. Subsequently, software such as MATLAB is used to read the video file, extract a series of frame images, and convert them into grayscale images to reduce the computing resources required for subsequent analysis and improve the analysis speed. This embodiment uses telemetry photoplethysmography technology to collect video data of the carotid artery area and extract grayscale images from it, providing basic image data for subsequent detection and analysis of pulse wave signals.

[0060] Step S200: Acquire two regions of interest in the grayscale image, and extract two original pulse wave signals from the two regions of interest.

[0061] In this embodiment, it is first necessary to identify and mark two regions of interest in each frame of the acquired grayscale image. The selection of the two regions of interest should be based on their relationship with the carotid artery, and usually two locations near the carotid artery are selected. Then, by analyzing the change of the grayscale value of the pixels in the two regions of interest over time, the photoplethysmography technology is used to capture the small volume changes caused by blood flow, and two original pulse wave signals are extracted from the two regions of interest. This process provides basic data for subsequent signal processing.

[0062] Step S300: Perform zero-phase-shift filtering and interpolation processing on the two original pulse wave signals to obtain two processed pulse wave signals.

[0063] In this embodiment, first, the two original pulse wave signals are subjected to zero phase shift filtering to remove noise while keeping the signal characteristics from phase shift, thereby ensuring the authenticity of the pulse wave morphology. Then, the two pulse wave signals subjected to zero phase shift filtering are interpolated to supplement the information within the sampling interval, improve the time resolution of the signal, and make the subsequent analysis more accurate and reliable. In this embodiment, the two original pulse wave signals are subjected to zero phase shift filtering and interpolation processing to obtain the processed two pulse wave signals, thereby improving the quality and accuracy of the two pulse wave signals and ensuring that the time difference measurement of the pulse wave signals in the subsequent steps is more accurate and reliable.

[0064] Specifically, Figure 2 As shown in , the step S300 includes the following steps:

[0065] Step S301: Process the two original pulse wave signals using a low-pass filter to remove high-frequency noise in the two original pulse wave signals;

[0066] Step S302, forward filtering the two original pulse wave signals processed by the low-pass filter;

[0067] Step S303: reversely filter the two original pulse wave signals after forward filtering to achieve zero phase shift filtering of the two original pulse wave signals.

[0068] In one implementation, after obtaining the two original pulse wave signals, first, the two original pulse wave signals are processed using a low-pass filter to remove high-frequency noise in the two original pulse wave signals. For example, if the two original pulse wave signals contain noise such as respiratory frequency, vibration generated by muscle movement, environmental noise or interference from other electronic devices, these will be filtered out by the low-pass filter. The low-pass filter includes but is not limited to Butterworth filter, Chebyshev filter and elliptic filter. The selection of which filter depends on the specific signal characteristics and the required filtering effect. Next, the two original pulse wave signals processed by the low-pass filter are forward filtered, that is, filtered in the direction of the time series. Forward filtering helps to further smooth the signal and reduce irregular fluctuations in a short time. The forward filtering includes but is not limited to the use of moving average filtering or higher-order filtering technology such as Kalman filtering. Then, the two original pulse wave signals after forward filtering are reverse filtered, that is, filtered again in the opposite direction of the time series. Reverse filtering combined with forward filtering can achieve zero phase shift filtering. Zero phase shift filtering does not cause phase delay in the signal, which is essential for maintaining the time synchronization of the pulse wave signal. For example, if FIR filters are used for forward and reverse filtering, it can be ensured that the filtering process does not introduce additional phase distortion.

[0069] In one implementation, after obtaining two pulse wave signals that have been filtered by zero phase shift, the two pulse wave signals are interpolated, and the interpolation processing methods include but are not limited to linear interpolation, polynomial interpolation, and spline interpolation. The main purpose of interpolation processing is to improve the temporal resolution of the signal, making it smoother and more continuous, so as to facilitate subsequent precise analysis. Specifically, by increasing the number of sampling points, subtle changes in the pulse wave can be captured, which is crucial for accurately evaluating the speed of the pulse wave. For example, if the original frame number of the pulse wave signal is 600 frames (equivalent to a sampling rate of 600 frames / second), in order to more finely analyze the details of the pulse wave, the interpolation technology can be used to increase it to 1200 frames (i.e., to 1200 frames / second), so that the peak value and other important features of the pulse wave can be more accurately estimated, thereby improving the accuracy of subsequent analysis.

[0070] Step S400: performing normalization processing and cross-correlation calculation on the two processed pulse wave signals to obtain a time difference between the two pulse wave signals.

[0071] In this embodiment, first, the two pulse wave signals after zero phase shift filtering and interpolation processing are normalized to ensure that the two pulse wave signals have the same amplitude range, thereby eliminating the influence of signal strength difference on time difference measurement. Then, the two pulse wave signals are cross-correlated to determine the best time alignment between the two pulse wave signals, that is, to find the best time delay of one pulse wave signal relative to the other pulse wave signal so that the similarity between them is maximized, thereby obtaining the time difference between the two pulse wave signals. This embodiment performs normalization processing and cross-correlation calculation on the two pre-processed pulse wave signals, and can accurately measure the time difference between the two pulse wave signals. This time difference reflects the propagation time of the pulse wave between two different positions, and provides key data for the subsequent calculation of the pulse wave velocity and the evaluation of the degree of arteriosclerosis.

[0072] Specifically, Figure 3 As shown in , the step S400 includes the following steps:

[0073] Step S401, normalizing the two processed pulse wave signals;

[0074] Step S402, performing cross-correlation calculation on the two normalized pulse wave signals to obtain a cross-correlation curve of the two pulse wave signals;

[0075] Step S403: Obtain the maximum value of the ordinate on the cross-correlation curve, and obtain the corresponding abscissa value τ based on the maximum value of the ordinate max .

[0076] Step S404: Obtain the preset sampling rate as f s , based on the sampling rate f s and the abscissa value τ max , determine the time difference between the two pulse wave signals as

[0077] In one implementation, first, the two pulse wave signals after zero-phase-shift filtering and interpolation processing are normalized. The main goal of this step is to eliminate the difference in signal amplitudes and ensure that the two pulse wave signals are compared on the same scale. The normalization method includes but is not limited to subtracting the signal from its average value and dividing it by its standard deviation. For example, assuming that the maximum amplitude of one pulse wave signal obtained from two different positions is 100 units and the other is 50 units, after normalization, the maximum amplitudes of the two signals will be standardized to 1 unit. The advantage of this is that the difference in signal intensity caused by factors such as sensor sensitivity, skin color, and lighting conditions is eliminated, making subsequent analysis more accurate. Next, the two normalized pulse wave signals are cross-correlated to obtain a cross-correlation curve of the two pulse wave signals. The cross-correlation calculation is implemented based on a cross-correlation function, and the cross-correlation function is Where τ is the time delay, x(t) and y(t) are the two normalized pulse wave signals. Then, the maximum value of the ordinate on the cross-correlation curve is obtained, and the corresponding abscissa value τ is obtained based on the maximum value of the ordinate. max After that, get the preset sampling rate as f s , based on the sampling rate f s and the abscissa value τ max , determine the time difference between the two pulse wave signals as This embodiment obtains the time difference between the two processed pulse wave signals by normalizing and cross-correlating the two signals. The advantage of this process is that it can accurately quantify the time delay of the propagation of the two pulse wave signals, thereby providing key data for the subsequent calculation of the pulse wave velocity. Normalization ensures direct comparability between different signals, while cross-correlation calculation can effectively detect and quantify the similarity and time offset between the two signals, and maintain high measurement accuracy even when the signals are interfered by noise. This precise time difference measurement is crucial for accurately assessing the degree of arteriosclerosis.

[0078] Step S500: Calculate the pulse wave velocity according to the time difference between the two pulse wave signals and the distance between the two regions of interest, and evaluate the degree of arteriosclerosis based on the pulse wave velocity.

[0079] In this embodiment, the pulse wave velocity is calculated by the time difference between the two pulse wave signals and the actual physical distance between the two regions of interest, and the degree of arteriosclerosis is evaluated based on the pulse wave velocity. The pulse wave velocity value obtained by non-invasive means can effectively reflect the health status of an individual's cardiovascular system and provide a basis for early detection and prevention of cardiovascular disease. At the same time, the method is simple to operate, non-invasive and highly repeatable, which is conducive to its wide application in clinical practice.

[0080] Specifically, Figure 4 As shown in , the step S500 includes the following steps:

[0081] Step S501, obtaining the time difference Δt between the two pulse wave signals;

[0082] Step S502, obtaining the distance Δd between two regions of interest;

[0083] Step S503: Calculate the pulse wave velocity based on the time difference and the distance

[0084] Step S504, obtaining a mapping relationship between the pulse wave velocity and the Young's modulus of the arterial wall;

[0085] Step S505: determining the Young's modulus of the arterial wall corresponding to the pulse wave velocity based on the mapping relationship and the pulse wave velocity;

[0086] Step S506: Evaluate the degree of arteriosclerosis based on the Young's modulus of the arterial wall.

[0087] In one implementation, in order to calculate the pulse wave velocity, first, the time difference Δt of the two pulse wave signals and the distance Δd between the two regions of interest are obtained. In order to accurately measure the distance Δd between the two regions of interest, the following steps can be used: first, a reference object of known size is placed near the photographed area and ensured to appear in each frame of the video to perform image calibration; then, the pixel size of the reference object in the image is measured by image processing technology, and a proportional relationship between pixels and actual sizes is established accordingly; then, two key points in the regions of interest are selected as marking points in each frame of the image; the pixel distance between the two marking points on the image is calculated using image processing software or programming language; finally, the pixel distance is converted into the actual physical distance Δd according to the proportional relationship established previously, and the accuracy is improved by averaging the distance measurements of multiple frames of images. After obtaining the time difference Δt of the two pulse wave signals and the distance Δd between the two regions of interest, the pulse wave velocity is calculated using the formula The pulse wave velocity is calculated.

[0088] In one implementation, in order to evaluate the degree of arteriosclerosis based on the pulse wave velocity, first, according to the Moens-Korteweg formula, a mapping relationship between the pulse wave velocity and the Young's modulus of the arterial wall is obtained. The Moens-Korteweg formula is: Wherein: h is the thickness of the blood vessel wall, r is the inner diameter of the blood vessel, ρ is the density of blood, and E is the Young's modulus of the blood vessel wall. Therefore, the Moens-Korteweg formula illustrates the mapping relationship between the pulse wave velocity PWV and the Young's modulus E of the arterial wall, that is, the pulse wave velocity is proportional to the Young's modulus of the arterial wall. Then, based on the mapping relationship and the pulse wave velocity, the Young's modulus of the arterial wall corresponding to the pulse wave velocity is determined, that is, the larger the pulse wave velocity, the larger the Young's modulus of the arterial wall. Finally, based on the Young's modulus of the arterial wall, the degree of arteriosclerosis is evaluated. The larger the Young's modulus of the arterial wall, the harder the arterial wall, indicating that the elasticity of the arterial wall is reduced and the degree of arteriosclerosis is increased. In summary, this embodiment can accurately calculate PWV by analyzing the time difference between two pulse wave signals and the distance between two regions of interest. Since PWV is an important indicator reflecting vascular elasticity and is closely related to the degree of arteriosclerosis, the PWV value obtained by this method can effectively evaluate the arterial health of an individual and provide an important basis for risk prediction and early intervention of cardiovascular diseases. In addition, this method is easy to operate, low-cost and does not require the use of radioactive substances, making it suitable for large-scale population screening and long-term follow-up monitoring.

[0089] In one implementation, the method further includes: verifying the effectiveness of the telemetric photoplethysmography in pulse wave monitoring by measuring a heart rate value.

[0090] In this embodiment, first, the two original pulse wave signals are subjected to Fourier transform to extract frequency domain information. The advantage of this step is that the main frequency components in the signal can be accurately identified, and the peak frequency can be found in the range of 0.8 Hz to 1.3 Hz, so as to obtain the first heart rate value A and the first heart rate value B corresponding to the heart rate frequency. For example, the original pulse wave signal A has a significant peak at 1.02 Hz, and the original pulse wave signal B has a significant peak at 1.05 Hz, then it can be preliminarily determined that these two frequencies correspond to the heart rates of the original pulse wave signals A and B, that is, the first heart rate value A = 1.02 Hz × 60 minutes / hour = 61.2 times / minute, and the first heart rate value B = 1.05 Hz × 60 minutes / hour = 63.0 times / minute. Then, the average peak spacing of the two original pulse wave signals after zero phase shift filtering and interpolation processing is calculated to obtain the second heart rate value A and the second heart rate value B. In order to calculate the second heart rate value by averaging the peak spacing: first, the position of the peak is detected from the original pulse wave signal by setting a threshold, using a derivative or a second-order derivative, etc.; next, the time interval between adjacent peaks is calculated; then, the average of the time differences between all adjacent peaks is calculated to obtain the average peak spacing; finally, the formula is used: Convert the average peak spacing to heart rate. This is a method for directly measuring heart rate, and it is highly robust and is not affected by changes in signal amplitude. Therefore, the heart rate can be accurately measured even if the signal strength fluctuates. For example, the average peak spacing of the original pulse wave signal A is 0.97 seconds, which is converted to a heart rate of about 61.8 beats / minute, which is the second heart rate value A; the average peak spacing of the original pulse wave signal B is 0.95 seconds, and the corresponding second heart rate value B is 63.0 beats / minute. Finally, the first heart rate value and the second heart rate value are compared. If the difference between the first heart rate value and the second heart rate value is less than a preset threshold, it is determined that the effectiveness of the telemetry photoplethysmography in pulse wave monitoring is verified. For example, the first heart rate value A is 1.02Hz (61.2 beats / minute), and the second heart rate value A is 61.8 beats / minute. The first heart rate value B is 1.05Hz (63.0 beats / minute), and the second heart rate value B is 63.0 beats / minute. Assuming that the preset threshold is ±3 beats / minute, the difference between the first heart rate value A and the second heart rate value A is 0.6 beats / minute, which is less than the threshold; similarly, the difference between the first heart rate value B and the second heart rate value B is 0 beats / minute, which is less than the threshold. Therefore, based on these data, it can be concluded that the effectiveness of the telemetry photoplethysmography in pulse wave monitoring has been verified. Through this step, not only can the performance of the telemetry photoplethysmography technology be effectively evaluated, but also the acquired data can be ensured to be highly accurate and reliable.

[0091] In summary, this embodiment first collects video files of the carotid artery area based on telemetry photoplethysmography, obtains high-quality image data in a non-contact manner, avoids discomfort to the subject, and can monitor the changes of the pulse wave in real time and continuously. Subsequently, several frames of images in the video file are obtained, and the grayscale image corresponding to each frame of the image is determined to reduce the computational complexity in the image processing process and improve the processing efficiency. Next, two regions of interest in the grayscale image are obtained, and two original pulse wave signals are extracted from the two regions of interest. This selection process utilizes the information of the specific position and improves the accuracy and reliability of the signal. Then, the two original pulse wave signals are subjected to zero-phase shift filtering and interpolation processing to obtain two processed pulse wave signals. Zero-phase shift filtering and interpolation processing can effectively remove noise interference and ensure that the signal is not distorted, thereby obtaining a purer pulse wave signal. Afterwards, the two processed pulse wave signals are normalized and cross-correlated to obtain the time difference of the two pulse wave signals. This step helps to eliminate the influence of the signal strength difference and ensure the accuracy of the time difference measurement. Finally, the pulse wave velocity is calculated according to the time difference between the two pulse wave signals and the distance between the two regions of interest, and the degree of arteriosclerosis is evaluated based on the pulse wave velocity. This step accurately quantifies the changes in arterial wall elasticity based on the pulse wave velocity, provides an important quantitative indicator for early diagnosis and intervention of arteriosclerosis, and helps prevent and manage cardiovascular diseases. The method proposed in the present invention non-invasively collects carotid artery area videos, accurately extracts and processes pulse wave signals, and calculates pulse wave velocity to evaluate the degree of arteriosclerosis. It has the advantages of simple operation, high accuracy, and patient-friendliness, and provides a powerful tool for early assessment of cardiovascular health.

[0092] like Figure 5As shown in , this embodiment also provides a pulse wave monitoring system based on telemetry photoplethysmography, which includes: a grayscale image acquisition module 10, an original pulse wave signal acquisition module 20, a processing module 30, a time difference acquisition module 40 and a pulse wave velocity calculation and evaluation module 50. Specifically, the grayscale image acquisition module 10 is used to collect a video file of the carotid artery area based on telemetry photoplethysmography, obtain a number of frames of images in the video file, and determine the grayscale image corresponding to each frame of image. The original pulse wave signal acquisition module 20 is used to obtain two regions of interest in the grayscale image and extract two original pulse wave signals from the two regions of interest. The processing module 30 is used to perform zero-phase shift filtering and interpolation processing on the two original pulse wave signals to obtain two processed pulse wave signals. The time difference acquisition module 40 is used to perform normalization processing and cross-correlation calculation on the two processed pulse wave signals to obtain the time difference of the two pulse wave signals. The pulse wave velocity calculation and evaluation module 50 is used to calculate the pulse wave velocity according to the time difference between the two pulse wave signals and the distance between the two regions of interest, and to evaluate the degree of arteriosclerosis based on the pulse wave velocity.

[0093] In one implementation, the processing module 30 includes:

[0094] A low-pass filtering unit, configured to process the two original pulse wave signals using a low-pass filter to remove high-frequency noise in the two original pulse wave signals;

[0095] A forward filtering unit, used for performing forward filtering on the two original pulse wave signals processed by the low-pass filter;

[0096] The reverse filtering unit is used to reversely filter the two original pulse wave signals after forward filtering, so as to implement zero phase shift filtering on the two original pulse wave signals.

[0097] In one implementation, the time difference acquisition module 40 includes:

[0098] A normalization processing unit, used for performing normalization processing on the two processed pulse wave signals;

[0099] A cross-correlation curve acquisition unit, used for performing cross-correlation calculation on the two normalized pulse wave signals to obtain a cross-correlation curve of the two pulse wave signals;

[0100] Abscissa value τ max An acquisition unit is used to acquire the maximum value of the ordinate on the cross-correlation curve, and acquire the corresponding abscissa value τ based on the maximum value of the ordinate max ;

[0101] The time difference determination unit is used to obtain a preset sampling rate of f s , based on the sampling rate f s and the abscissa value τ max , determine the time difference between the two pulse wave signals as

[0102] In one implementation, the pulse wave velocity calculation and evaluation module 50 includes:

[0103] A time difference acquisition unit, used for acquiring the time difference Δt between the two pulse wave signals;

[0104] A distance acquisition unit, used for acquiring a distance Δd between two regions of interest;

[0105] A pulse wave velocity calculation unit, configured to calculate the pulse wave velocity based on the time difference and the distance.

[0106] A mapping relationship acquisition unit, used to acquire a mapping relationship between the pulse wave velocity and the Young's modulus of the arterial wall;

[0107] an arterial wall Young's modulus determination unit, configured to determine the Young's modulus of the arterial wall corresponding to the pulse wave velocity based on the mapping relationship and the pulse wave velocity;

[0108] The arteriosclerosis degree evaluation unit is used to evaluate the arteriosclerosis degree based on the Young's modulus of the arterial wall.

[0109] In one implementation, the system further includes a verification module for verifying the validity of the telemetric photoplethysmography in pulse wave monitoring by measuring heart rate values.

[0110] In one implementation, the verification module includes:

[0111] a first heart rate value acquisition unit, configured to perform Fourier transform on the two original pulse wave signals, acquire a peak frequency in the range of 0.8 Hz to 1.3 Hz, and obtain a first heart rate value;

[0112] A second heart rate value acquisition unit is used to calculate the average peak distance between the two original pulse wave signals that have undergone zero-phase shift filtering and interpolation processing to obtain a second heart rate value;

[0113] A verification unit is used to determine that the effectiveness of the telemetry photoplethysmography in pulse wave monitoring is verified if the difference between the first heart rate value and the second heart rate value is less than a preset threshold.

[0114] The working principles of each module in the pulse wave monitoring system based on telemetry photoplethysmography of this embodiment are the same as the principles of each step in the above method embodiment, and will not be repeated here.

[0115] Based on the above embodiment, the present invention further provides a terminal, the principle block diagram of the terminal can be as follows: Figure 6 The terminal may include one or more processors 100 ( Figure 6 Only one is shown in the figure), a memory 101 and a computer program 102 stored in the memory 101 and executable on one or more processors 100, for example, a pulse wave monitoring program based on telemetry photoplethysmography. When one or more processors 100 execute the computer program 102, the various steps in the embodiment of the pulse wave monitoring method based on telemetry photoplethysmography can be implemented. Alternatively, when one or more processors 100 execute the computer program 102, the functions of the various modules / units in the embodiment of the pulse wave monitoring method based on telemetry photoplethysmography can be implemented, which is not limited here.

[0116] In one embodiment, the processor 100 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0117] In one embodiment, the memory 101 may be an internal storage unit of an electronic device, such as a hard disk or memory of the electronic device. The memory 101 may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device. Further, the memory 101 may also include both an internal storage unit of the electronic device and an external storage device. The memory 101 is used to store computer programs and other programs and data required by the terminal. The memory 101 may also be used to temporarily store data that has been output or is to be output.

[0118] Those skilled in the art will understand that Figure 6The principle block diagram shown in the figure is only a block diagram of a partial structure related to the scheme of the present invention, and does not constitute a limitation on the terminal to which the scheme of the present invention is applied. The specific terminal may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0119] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, operating database or other media used in the embodiments provided by the present invention may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double operational data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0120] 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 it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A pulse wave monitoring method based on telemetry photoplethysmography, characterized in that: The method comprises: Based on telemetry photoplethysmography, a video file of the carotid artery region is collected, several frames of images in the video file are obtained, and a grayscale image corresponding to each frame of the image is determined; Acquire two regions of interest in the grayscale image, and extract two original pulse wave signals from the two regions of interest; Performing zero-phase-shift filtering and interpolation processing on the two original pulse wave signals to obtain two processed pulse wave signals; Performing normalization processing and cross-correlation calculation on the two processed pulse wave signals to obtain a time difference between the two pulse wave signals; The pulse wave velocity is calculated according to the time difference between the two pulse wave signals and the distance between the two regions of interest, and the degree of arteriosclerosis is evaluated based on the pulse wave velocity.

2. The pulse wave monitoring method based on telemetry photoplethysmography according to claim 1, characterized in that: The step of performing zero-phase-shift filtering on the two original pulse wave signals comprises: Processing the two original pulse wave signals using a low-pass filter to remove high-frequency noise in the two original pulse wave signals; Perform forward filtering on the two original pulse wave signals processed by the low-pass filter; The two original pulse wave signals after forward filtering are reversely filtered to achieve zero phase shift filtering of the two original pulse wave signals.

3. The pulse wave monitoring method based on telemetry photoplethysmography according to claim 1, characterized in that: The step of performing normalization processing and cross-correlation calculation on the two processed pulse wave signals to obtain a time difference between the two pulse wave signals includes: performing normalization processing on the two processed pulse wave signals; Performing cross-correlation calculation on the two normalized pulse wave signals to obtain a cross-correlation curve of the two pulse wave signals; The cross-correlation calculation is implemented based on the cross-correlation function, which is R(τ): Wherein τ is the time delay, x(t) and y(t) are the two pulse wave signals after the normalization processing; Obtain the maximum value of the ordinate on the cross-correlation curve, and obtain the corresponding abscissa value τ based on the maximum value of the ordinate max ; Get the preset sampling rate f s , based on the sampling rate f s and the abscissa value τ max , determine the time difference between the two pulse wave signals as 4. The pulse wave monitoring method based on telemetry photoplethysmography according to claim 3, characterized in that: The calculating the pulse wave velocity according to the time difference between the two pulse wave signals and the distance between the two regions of interest comprises: Acquire the time difference Δt between the two pulse wave signals; Get the distance Δd between two regions of interest; Based on the time difference and the distance, the pulse wave velocity is calculated 5. The pulse wave monitoring method based on telemetry photoplethysmography according to claim 4, characterized in that: The method of assessing the degree of arteriosclerosis based on the pulse wave velocity comprises: Acquiring a mapping relationship between the pulse wave velocity and the Young's modulus of the arterial wall; Based on the mapping relationship and the pulse wave velocity, determining the Young's modulus of the arterial wall corresponding to the pulse wave velocity; Based on the Young's modulus of the arterial wall, the degree of arteriosclerosis was evaluated.

6. The pulse wave monitoring method based on telemetry photoplethysmography according to claim 1, characterized in that: The method further comprises: The effectiveness of the telemetry photoplethysmography in pulse wave monitoring was verified by measuring heart rate values.

7. The pulse wave monitoring method based on telemetry photoplethysmography according to claim 6, characterized in that: The verification of the effectiveness of the telemetry photoplethysmography in pulse wave monitoring by measuring the heart rate comprises: Performing Fourier transform on the two original pulse wave signals to obtain a peak frequency in the range of 0.8 Hz to 1.3 Hz to obtain a first heart rate value; Calculating the average peak distance of the two original pulse wave signals after zero-phase-shift filtering and interpolation processing to obtain a second heart rate value; If the difference between the first heart rate value and the second heart rate value is less than a preset threshold, it is determined that the effectiveness of the telemetry photoplethysmography in pulse wave monitoring is verified.

8. A pulse wave monitoring system based on telemetry photoplethysmography, characterized in that: The system comprises: A grayscale image acquisition module is used to collect a video file of the carotid artery region based on telemetry photoplethysmography, obtain a number of frames of images in the video file, and determine a grayscale image corresponding to each frame of the image; An original pulse wave signal acquisition module is used to acquire two regions of interest in the grayscale image and extract two original pulse wave signals from the two regions of interest; A processing module, used for performing zero-phase-shift filtering and interpolation processing on the two original pulse wave signals to obtain two processed pulse wave signals; A time difference acquisition module, used for performing normalization processing and cross-correlation calculation on the two processed pulse wave signals to obtain the time difference between the two pulse wave signals; The pulse wave velocity calculation and evaluation module is used to calculate the pulse wave velocity according to the time difference between the two pulse wave signals and the distance between the two regions of interest, and to evaluate the degree of arteriosclerosis based on the pulse wave velocity.

9. A terminal, characterized in that: The terminal includes a memory, a processor, and a pulse wave monitoring program based on telemetric photoplethysmography stored in the memory and executable on the processor. When the processor executes the pulse wave monitoring program based on telemetric photoplethysmography, the steps of the pulse wave monitoring method based on telemetric photoplethysmography as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a pulse wave monitoring program based on telemetric photoplethysmography. When the pulse wave monitoring program based on telemetric photoplethysmography is executed by the processor, the steps of the pulse wave monitoring method based on telemetric photoplethysmography as described in any one of claims 1 to 7 are implemented.

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