Continuous blood pressure monitoring method and system based on elastic cavity model and pulse wave velocity equation, terminal and storage medium

Abstract

The invention relates to the technical field of blood pressure monitoring, and discloses a continuous blood pressure monitoring method and system based on an elastic cavity model and a pulse wave velocity equation, a terminal and a storage medium, and the method comprises the following steps: reflecting a dynamic process among cardiac ejection, artery compliance and peripheral resistance from a system level by using a two-element elastic cavity model; a time domain coupling relationship between blood pressure and blood flow is established, and a pulse wave velocity equation is introduced to calculate artery compliance C parameters of different objects so as to calibrate model compliance C parameters, so that a targeted blood pressure monitoring result is obtained. Individualized physiological information is introduced, and the accuracy and interpretability of blood pressure prediction on different objects are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of blood pressure monitoring, and particularly relates to a continuous blood pressure monitoring method, system, terminal and computer readable storage medium based on an elastic cavity model and a pulse wave velocity equation. BACKGROUND

[0002] Blood pressure is an important physiological indicator reflecting the function state of the human circulatory system and plays a core role in maintaining the homeostasis of the cardiovascular system.

[0003] Existing non-invasive blood pressure measurement techniques are mostly based on the pulse wave velocity equation or the elastic cavity model to establish a blood pressure estimation model alone, but the former is difficult to depict the dynamic process between cardiac pumping and peripheral resistance, and the latter can describe the transient response of pressure-flow, but is insufficiently sensitive to the spatial distribution of blood vessel elasticity changes.

[0004] Therefore, the prior art still needs to be improved and developed. SUMMARY

[0005] The main purpose of the present application is to provide a continuous blood pressure monitoring method, system, terminal and computer readable storage medium based on an elastic cavity model and a pulse wave velocity equation, aiming to solve the problem of inaccurate blood pressure measurement caused by the incomplete physical mechanism, insufficient model universality and significant individual difference in existing non-invasive continuous blood pressure measurement techniques.

[0006] To achieve the above-mentioned purpose, the present application provides a continuous blood pressure monitoring method based on an elastic cavity model and a pulse wave velocity equation, which comprises the following steps: Collecting a plurality of physiological signals of a target object, pre-processing all the physiological signals to obtain corresponding standard physiological signals, and obtaining a plurality of conventional parameters from all the standard physiological signals; Constructing an elastic cavity model, adding all the conventional parameters to the elastic cavity model to construct a state equation, and extracting features from all the standard physiological signals to obtain morphological features, statistical features and sequence features; Constructing a mapping between the morphological features, the statistical features and the sequence features and a plurality of parameters in the state equation to obtain a training sample set, and training the elastic cavity model using the training sample set to obtain a prediction model; Obtaining a pulse wave velocity from a plurality of the standard physiological signals, calculating an arterial wall Young's modulus according to the pulse wave velocity, constructing a pulse wave velocity equation, adding the arterial wall Young's modulus to the pulse wave velocity equation to obtain a specific expression of an arterial compliance value C parameter; The arterial compliance value C parameter of the target object is calculated based on the specific expression, and the model compliance C parameter in the prediction model is adjusted. The blood pressure of the target object is monitored using the adjusted prediction model to obtain a continuous blood pressure estimate.

[0007] Optionally, in the continuous blood pressure monitoring method based on the elastic cavity model and pulse wave velocity equation, the conventional parameters include: cardiac systolic ejection time, cardiac diastolic time, cardiac cycle time, and blood flow parameters. The process involves collecting multiple physiological signals from the target object, preprocessing all the physiological signals to obtain corresponding standard physiological signals, and extracting multiple conventional parameters from all the standard physiological signals, specifically including: Data is collected from the target object using multiple sensors to obtain photoplethysmography (PPG), electrocardiogram (ECG), and cardiac impedance cardiogram (CIK). The first noise in the photoplethysmography (PPG), electrocardiogram (ECG), and impedance cardiogram is filtered out using a first filter, and the second noise in the PPG, ECG, and impedance cardiogram is filtered out using a second filter, resulting in a standard PPG, standard ECG, and standard impedance cardiogram. Extract the cardiac systolic ejection time, cardiac diastolic time, and cardiac cycle time of the target object from the standard photoplethysmography pulse wave; Extract the blood flow parameters of the target object from the standard impedance cardiogram: ; in, This represents blood flow parameters. Indicates blood density, and These respectively represent blood from Flowing to Point and Time at point, and They represent Point and The arterial wall is thick at the point. Indicates the length of the blood vessel. Indicates the starting point of the monitoring.

[0008] Optionally, the continuous blood pressure monitoring method based on the elastic cavity model and pulse wave velocity equation, wherein constructing the elastic cavity model involves adding all the conventional parameters to the elastic cavity model to construct a state equation, and extracting features from all standard physiological signals to obtain morphological features, statistical features, and sequence features, specifically includes: Construct an elastic cavity model of the target object, and add the cardiac systolic ejection time, the cardiac diastolic time, and the cardiac cycle time to the elastic cavity model: ; ; in, Indicates the first Current input at any given time, Indicates cardiac output. Indicates the duration of the cardiac cycle. It indicates the duration of cardiac contraction and ejection. A time index representing the entire cardiac cycle. Indicates the number of cardiac cycles. The C-parameter represents the model's compliance. Indicates the first Voltage at time, Indicates resistance; Based on the cardiac cycle time, statistical features are obtained by statistically analyzing the standard photoplethysmography pulse wave, the standard electrocardiogram, and the standard impedance cardiogram. The morphological features are then obtained by averaging the statistical features. Acquire photoplethysmography (PPG) signals or impedance cardiogram (ETC) signals from multiple cardiac cycles, calculate the variation signals of the PPG signals or ETC signals, and obtain sequence characteristics.

[0009] Optionally, the continuous blood pressure monitoring method based on the elastic cavity model and pulse wave velocity equation, wherein constructing the mapping between the morphological features, the statistical features, and the sequence features and multiple parameters in the state equation to obtain a training sample set, and using the training sample set to train the elastic cavity model to obtain a prediction model, specifically includes: For the cardiac cycle time, a preset algorithm is used to construct a mapping from the morphological features, statistical features, and sequence features to the resistance and initial model compliance C parameter in the state equation, resulting in multiple feature-parameter pairs; All the feature-parameter pairs are divided into a training sample set, a validation sample set, and a test sample set, wherein the validation sample set is used to validate the prediction model, and the test sample set is used to test the prediction model. The training sample set is input into the elastic cavity model for prediction. A weighted loss term is constructed based on the prediction results and each actual value in the training sample set. The elastic cavity model is iteratively optimized based on the weighted loss term to obtain the prediction model.

[0010] Optionally, the continuous blood pressure monitoring method based on the elastic cavity model and pulse wave velocity equation, wherein the step of constructing a weighted loss term based on the prediction result and each actual value in the training sample set, and iteratively optimizing the elastic cavity model based on the weighted loss term to obtain the prediction model, specifically includes: A weighted loss term is constructed based on the actual value of each training sample in the training sample set and the prediction result: ; ; in, Indicates the weighted loss term. This represents the total number of training samples. Indicates the first Each sample weight, Indicates the first One actual value, Indicates the prediction result. Indicates the first training samples, Indicates the first Weights for the next iteration Indicates the first In the next iteration, the training samples are input into the output value of the weak regressor. This represents the input training samples. Indicates the number of iterations. This represents the median; The sample weights are initialized, and the initial pseudo-residuals are calculated to train the weak regressor, obtaining the minimized weighted squared error: ; in, Indicates the first The first iteration in the round Individual sample weights; ; in, Indicates the first The first iteration in the round The pseudo residuals of each training sample Indicates the first Prediction results in round iteration; ; in, Indicates the first Minimize the weighted squared error during rounds of iterative training. Indicates the first The first round of iterative training The sample weights of each sample. Indicates the first In the nth iteration Each training sample is input into the output value of the weak regressor; An absolute error is constructed based on the output value and the corresponding pseudo-residual, and the average weighted loss is calculated using the absolute error and the sample weights of the current round: ; ; in, Indicates the first In the nth iteration One absolute error, Indicates the first The average weighted loss of the next iteration Indicates the first In the nth iteration Individual sample weights; For the current round, the learner weights are calculated using the average weighted loss, and the sample weights are updated using these learner weights to obtain the sample weights for the next round. This process continues until the updated parameters that minimize the weighted loss term are obtained, and the prediction model is constructed using these updated parameters. ; ; in, Indicates the first Learner weights in the next iteration Indicates the first The th iteration in the Individual sample weights.

[0011] Optionally, the continuous blood pressure monitoring method based on the elastic cavity model and pulse wave velocity equation, wherein the step of obtaining pulse wave velocity from multiple standard physiological signals, calculating the Young's modulus of the arterial wall based on the pulse wave velocity, constructing a pulse wave velocity equation, and adding the Young's modulus of the arterial wall to the pulse wave velocity equation to obtain a specific expression for the arterial compliance value C parameter, specifically includes: Based on the peak time difference in the standard photoplethysmography pulse wave or the pulse conduction time in the standard electrocardiogram, the pulse wave velocity is obtained, and the Young's modulus of the arterial wall is calculated according to the calculation expression of the pulse wave velocity. ; ; in, Indicates pulse wave velocity, This represents the Young's modulus of the arterial wall. Indicates thick arterial wall. Indicates blood density, Indicates the radius of the artery; Constructing the pulse wave velocity equation: ; ; ; in, The C parameter represents the arterial compliance value. Indicates volume change, Indicates pressure changes. Indicates the length of the blood vessel. Indicates changes in arterial radius; By adding the Young's modulus of the arterial wall to the pulse velocity equation, a specific expression for the arterial compliance value C parameter is constructed: ; in, This indicates the volume of the cavity.

[0012] Optionally, the continuous blood pressure monitoring method based on the elastic cavity model and pulse wave velocity equation, wherein calculating the arterial compliance value C parameter of the target object according to the specificity expression, adjusting the model compliance C parameter in the prediction model, and monitoring the blood pressure of the target object using the adjusted prediction model to obtain a continuous blood pressure estimate, specifically includes: The arterial compliance value C parameter of the target object is calculated based on the blood density, pulse wave velocity, and lumen volume of the target object. The model compliance C parameter in the prediction model is adjusted using the arterial compliance value C parameter to obtain the final prediction model; The blood pressure of the target object is continuously monitored using the final prediction model to obtain continuous blood pressure estimates.

[0013] Furthermore, to achieve the above objectives, the present invention also provides a continuous blood pressure monitoring system based on an elastic cavity model and a pulse wave velocity equation, wherein the continuous blood pressure monitoring system based on the elastic cavity model and the pulse wave velocity equation includes: The parameter acquisition module is used to acquire multiple physiological signals of the target object, preprocess all the physiological signals to obtain corresponding standard physiological signals, and obtain multiple conventional parameters from all the standard physiological signals. The feature extraction module is used to construct an elastic cavity model, add all the conventional parameters to the elastic cavity model to construct a state equation, and extract features from all standard physiological signals to obtain morphological features, statistical features and sequence features. The model training module is used to construct the mapping between the morphological features, the statistical features, and the sequence features and multiple parameters in the state equation, to obtain a training sample set, and to train the elastic cavity model using the training sample set to obtain a prediction model. The constraint parameter construction module is used to obtain pulse wave velocity from multiple standard physiological signals, calculate the Young's modulus of the arterial wall based on the pulse wave velocity, construct the pulse wave velocity equation, add the Young's modulus of the arterial wall to the pulse wave velocity equation, and obtain a specific expression for the arterial compliance value C parameter. The blood pressure monitoring module is used to calculate the arterial compliance value C parameter of the target object according to the specific expression, adjust the model compliance C parameter in the prediction model, and use the adjusted prediction model to monitor the blood pressure of the target object to obtain continuous blood pressure estimates.

[0014] Furthermore, to achieve the above objectives, the present invention also provides a terminal, wherein the terminal includes: a memory, a processor, and a continuous blood pressure monitoring program based on an elastic cavity model and a pulse wave velocity equation stored in the memory and executable on the processor, wherein when the continuous blood pressure monitoring program based on an elastic cavity model and a pulse wave velocity equation is executed by the processor, it implements the steps of the continuous blood pressure monitoring method based on an elastic cavity model and a pulse wave velocity equation as described above.

[0015] Furthermore, to achieve the above objectives, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a continuous blood pressure monitoring program based on an elastic cavity model and a pulse wave velocity equation, and when the continuous blood pressure monitoring program based on the elastic cavity model and the pulse wave velocity equation is executed by a processor, it implements the steps of the continuous blood pressure monitoring method based on the elastic cavity model and the pulse wave velocity equation as described above.

[0016] In this invention, multiple physiological signals of the target object are collected, and all the physiological signals are preprocessed to obtain corresponding standard physiological signals. Multiple conventional parameters are then obtained from all the standard physiological signals. An elastic cavity model is constructed, and all the conventional parameters are added to the elastic cavity model to construct a state equation. Feature extraction is performed on all standard physiological signals to obtain morphological features, statistical features, and sequence features. A mapping is constructed between the morphological features, statistical features, and sequence features and the multiple parameters in the state equation to obtain a training sample set. The elastic cavity model is trained using the training sample set to obtain a prediction model. Pulse wave velocity is obtained from the multiple standard physiological signals, and the Young's modulus of the arterial wall is calculated based on the pulse wave velocity. A pulse wave velocity equation is constructed, and the Young's modulus of the arterial wall is added to the pulse wave velocity equation to obtain a specific expression for the arterial compliance value C parameter. The arterial compliance value C parameter of the target object is calculated based on the specific expression, and the model compliance C parameter in the prediction model is adjusted. The adjusted prediction model is used to monitor the blood pressure of the target object to obtain a continuous blood pressure estimate. This invention incorporates individualized physiological information, improving the accuracy and biological authenticity of blood pressure prediction for different subjects. Attached Figure Description

[0017] Figure 1 This is a flowchart of a preferred embodiment of the continuous blood pressure monitoring method based on the elastic cavity model and pulse wave velocity equation of the present invention. Figure 2 This is a flowchart of the multi-physiological signal acquisition and processing of a preferred embodiment of the continuous blood pressure monitoring method based on the elastic cavity model and pulse wave velocity equation of the present invention. Figure 3 This is an overall flowchart of a preferred embodiment of the continuous blood pressure monitoring method based on the elastic cavity model and pulse wave velocity equation of the present invention. Figure 4 This is a schematic diagram of the elastic cavity model of a preferred embodiment of the continuous blood pressure monitoring method based on the elastic cavity model and pulse wave velocity equation of the present invention. Figure 5 This is a structural diagram of a preferred embodiment of the continuous blood pressure monitoring system based on the elastic cavity model and pulse wave velocity equation of the present invention; Figure 6 This is a structural diagram of a preferred embodiment of the terminal of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, 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 of the invention and are not intended to limit the invention.

[0019] The preferred embodiment of the present invention describes a continuous blood pressure monitoring method based on an elastic cavity model and a pulse wave velocity equation, such as... Figure 1 As shown, the continuous blood pressure monitoring method based on the elastic cavity model and pulse wave velocity equation includes the following steps: Step S10: Collect multiple physiological signals of the target object, preprocess all the physiological signals to obtain corresponding standard physiological signals, and obtain multiple conventional parameters from all the standard physiological signals.

[0020] Most existing non-invasive blood pressure monitoring methods rely on empirical regression or data-driven models (such as machine learning and deep learning) to directly fit blood pressure values. While these methods can achieve high accuracy on specific datasets, they lack direct physiological mechanism support and struggle to explain the physiological origins of blood pressure changes. Therefore, this invention first acquires multiple physiological signals from the target subject and extracts several conventional parameters to construct a WindKessel model (i.e., an elastic cavity model, referred to as the WK model below). The WK model reflects the time-domain process of hemodynamics in the target subject, thereby describing the transient response of pressure-flow and improving the accuracy of blood pressure monitoring.

[0021] The physiological signals include: photoplethysmography (PPG signal), electrocardiogram (ECG signal), and impedance cardiogram (ICG signal). The corresponding standard physiological signals include: standard photoplethysmography (PPG signal), standard electrocardiogram (ECG signal), and standard impedance cardiogram (ICG signal). The routine parameters include: cardiac systolic ejection time, cardiac diastolic time, cardiac cycle time, and blood flow parameters.

[0022] Specifically, multiple sensors are used to collect data from the target object, obtaining photoplethysmography (PPG), electrocardiogram (ECG), and cardiac impedance cardiogram (IAC). A first filter is used to filter out first noise in the PPG, ECG, and IAC, and a second filter is used to filter out second noise in the PPG, ECG, and IAC, resulting in standard PPG, standard ECG, and standard IAC. The cardiac systolic ejection time, diastolic time, and cardiac cycle time of the target object are extracted from the standard PPG. The blood flow parameters of the target object are extracted from the standard IAC. ; in, This represents blood flow parameters. Indicates blood density, and These respectively represent blood from Flowing to Point and Time at point, and They represent Point and The arterial wall is thick at the point. Indicates the length of the blood vessel. Indicates the starting point of the monitoring.

[0023] In order to achieve continuous blood pressure monitoring, this invention collects physiological signals of the target object through a variety of sensors, including photoplethysmography (PPG) sensors, electrocardiogram (ECG) sensors, and impedance cardiography (ICP) sensors. These sensors are placed in wearable devices (such as wristbands or chest straps) and fit non-invasively against the human skin, improving the convenience of blood pressure monitoring.

[0024] Furthermore, during data acquisition, a specific frequency can be set to capture subtle waveform changes within the cardiac cycle, improving data integrity. It is important to note that noise often accompanies the extraction of physiological signals; therefore, preprocessing the physiological signals is crucial. Noise is primarily categorized into low-frequency and high-frequency interference: low-frequency interference mainly manifests as baseline drift in the waveform, primarily originating from low-frequency signals generated by human respiration; high-frequency interference mainly manifests as small-amplitude fluctuations and spikes in the waveform signal, originating from electromyographic noise generated by normal physiological activities of human tissues and electrical signal interference from the acquisition equipment itself.

[0025] Furthermore, during the preprocessing of physiological signals, it is necessary to preserve the original signal waveform while minimizing baseline drift and power frequency interference to ensure that the quality of the physiological signals is not affected. For example... Figure 2 As shown, in the embodiments disclosed in this invention, for low-frequency noise, a Butterworth high-pass filter with a cutoff frequency of 0.5 Hz and an order of 5 can be used to remove baseline drift; for high-frequency noise, a fourth-order Butterworth filter with a cutoff frequency of 10 Hz can be used to process and eliminate high-frequency noise in the signal, and finally obtain standard physiological signals (standard photoplethysmography pulse wave, standard electrocardiogram and standard cardiac impedance cardiogram).

[0026] Furthermore, several routine parameters need to be extracted from these standard physiological signals to construct a WK model adapted to the target subject, thereby improving the accuracy of blood pressure monitoring. In the WK model, the routine parameters that need to be obtained (such as...) Figure 3 (As shown) includes cardiac contraction and ejection time. diastolic time Heart rate cycle time (HR, equivalent to heart rate), cardiac output Stroke volume (SV, which refers to the amount of blood ejected from one ventricle during a single heartbeat) and heart rate can be used to further calculate stroke volume.

[0027] Among them, cardiac contraction and ejection time diastolic time Heart rate cycle time Obtained from a standard PPG signal, the cardiac cycle is divided into segments based on each trough of the standard PPG signal, with each trough serving as the starting point. Within the same cardiac cycle, the time from the starting point to the peak is... The time from the peak to the start of the next cardiac cycle is The entire cardiac cycle lasts for 1 hour. For SV, it is obtained from the standard ICG signal. Cardiac function and blood flow parameters are analyzed based on the C (starting point of the c wave in the impedance diagram), B (starting point of ventricular ejection), and X (ending point of ventricular ejection) points in the ICG signal.

[0028] Step S20: Construct an elastic cavity model, add all the conventional parameters to the elastic cavity model to construct a state equation, extract features from all standard physiological signals to obtain morphological features, statistical features and sequence features.

[0029] Existing continuous blood pressure monitoring methods often fail to fully utilize signal features and lack dynamic correlation modeling. Most methods estimate blood pressure using only simple waveform features such as PPG signals or Pulse Transit Time (PTT), neglecting the coupling relationship between pulse wave propagation characteristics, vascular compliance, and hemodynamic parameters. This superficial feature-based modeling approach struggles to fully extract the deep vascular dynamic information contained within multiple physiological signals, leading to decreased model stability and accuracy under complex physiological changes or motion conditions. Therefore, this invention discloses the WK model, incorporating various standard physiological signals of the target object to reflect the dynamic processes between cardiac ejection, arterial compliance, and peripheral resistance at the system level. This establishes a temporal coupling relationship between blood pressure and blood flow, thereby achieving interpretable blood pressure monitoring.

[0030] Specifically, an elastic cavity model of the target object is constructed, and the cardiac contraction and ejection time, the cardiac diastolic time, and the cardiac cycle time are added to the elastic cavity model: ; ; in, Indicates the first Current input at any given time, Indicates cardiac output. Indicates the duration of the cardiac cycle. It indicates the duration of cardiac contraction and ejection. A time index representing the entire cardiac cycle. Indicates the number of cardiac cycles. The C-parameter represents the model's compliance. Indicates the first Voltage at time, The resistance is represented; based on the cardiac cycle time, statistical features are obtained by statistically analyzing the standard photoplethysmography (PPG), the standard electrocardiogram (ECG), and the standard impedance cardiogram (ETC); morphological features are obtained by averaging the statistical features; PPG signals or ETC signals from multiple cardiac cycles are acquired, and the variation signals of the PPG signals or ETC signals are calculated to obtain sequence features.

[0031] The WK model is designed using resistors and capacitors, such as... Figure 4 As shown, the resistor R and capacitor (i.e., model compliance C parameters) are connected in parallel, and then the current is input to obtain the WK model. The state equation can be derived from this model.

[0032] In the embodiments disclosed in this invention, feature extraction is performed on the acquired standard physiological signals (standard PPG signal, standard ECG signal, and standard ICG signal) to obtain, as follows: Figure 3 The waveform features shown (i.e., the morphological features, statistical features, and sequence features described in this invention) are used for parameter prediction in the subsequent WK model, ensuring the comprehensiveness and physiological relevance of the model input.

[0033] Furthermore, morphological features are extracted based on a single cardiac cycle. The ECG signal is monitored for R-peaks to divide the cardiac cycle, and then the features extracted from each cardiac cycle are averaged to obtain the final feature values. These morphological features have been widely defined in existing research and specifically include time parameters, signal amplitude, area parameters, and proportionality coefficients between these features. Statistical features are similar to morphological features, also extracted based on a single cardiac cycle and then averaged. Specifically, these statistical features include the mean, median, standard deviation, variance, maximum, minimum, difference between maximum and minimum values, skewness, and kurtosis of the PPG and ICG signals. Unlike morphological and statistical features, sequence features are not extracted based on a single cardiac cycle but are obtained based on a segment of PPG or ICG signal. In the embodiments disclosed in this invention, a heart rate analysis toolkit can be used to calculate the variability of the signal segment to describe the change pattern of the signal sequence, including changes in heart rate intervals, short-term and long-term scatter plot standard deviations in Poincaré plot analysis, and also to calculate the sample entropy and approximate entropy of the signal sequence to capture the nonlinear dynamic characteristics of the physiological signal.

[0034] Step S30: Construct a mapping between the morphological features, statistical features, and sequence features and multiple parameters in the state equation to obtain a training sample set, and use the training sample set to train the elastic cavity model to obtain a prediction model.

[0035] In this process, following the feature extraction in step S20, a specific algorithm is further used to convert multi-waveform features into WK model parameters (such as...). Figure 4 As shown, the resistance R and the initial model compliance C parameter are... The mapping of ).

[0036] Specifically, for the cardiac cycle time, a preset algorithm is used to construct a mapping from the morphological features, statistical features, and sequence features to the resistance and initial model compliance C parameter in the state equation, resulting in multiple feature-parameter pairs. All feature-parameter pairs are divided into a training sample set, a validation sample set, and a test sample set, wherein the validation sample set is used to validate the prediction model, and the test sample set is used to test the prediction model. The training sample set is input into the elastic cavity model for prediction. A weighted loss term is constructed based on the prediction results and each actual value in the training sample set. The elastic cavity model is iteratively optimized based on the weighted loss term to obtain the prediction model.

[0037] In the embodiments disclosed in this invention, the collected multiple physiological signals and their corresponding WK ​​model parameters are first divided at the sample level into a training set (80%), a validation set (10%), and a test set (10%), with each sample forming a feature-parameter pair for a cardiac cycle.

[0038] Further, a weighted loss term is constructed based on the actual value of each training sample in the training sample set and the prediction result: ; ; in, Indicates the weighted loss term. This represents the total number of training samples. Indicates the first Each sample weight, Indicates the first One actual value, Indicates the prediction result. Indicates the first training samples, Indicates the first Weights for the next iteration Indicates the first In the next iteration, the training samples are input into the output value of the weak regressor. This represents the input training samples. Indicates the number of iterations. The median is represented; the sample weights are initialized, and the initial pseudo-residuals are calculated to train a weak regressor, obtaining the minimized weighted squared error: ; in, Indicates the first The first iteration in the round Individual sample weights; ; in, Indicates the first The first iteration in the round The pseudo residuals of each training sample Indicates the first Prediction results in round iteration; ; in, Indicates the first Minimize the weighted squared error during rounds of iterative training. Indicates the first The first round of iterative training The sample weights of each sample. Indicates the first In the nth iteration The output value of the weak regressor is input into each training sample; an absolute error is constructed based on the output value and the corresponding pseudo residual; and the average weighted loss is calculated using the absolute error and the sample weights of the current round. ; ; in, Indicates the first In the nth iteration One absolute error, Indicates the first The average weighted loss of the next iteration Indicates the first In the nth iteration Each sample has a weight; for the current round, the learner weights are calculated using the average weighted loss, and the sample weights are updated using the learner weights to obtain the sample weights for the next round, until the update parameters that minimize the weighted loss term are obtained, and the prediction model is constructed using the update parameters. ; ; in, Indicates the first Learner weights in the next iteration Indicates the first The th iteration in the Individual sample weights.

[0039] Among them, the prediction results The weighted median of the weak learner sequence is used. After constructing the weighted loss, iterative optimization can be performed, starting from the initial prediction. The process begins with T iterations: For each iteration, pseudo-residuals are first calculated to train a weak learner, thereby minimizing the weighted squared error. Based on this, the normalized absolute error is calculated, and the average weighted loss is calculated to obtain the learner weights. Finally, the sample weights for the next iteration are updated based on the learner weights. The parameters are continuously updated during iterations to continuously reduce the loss function, thus achieving the final model construction. By minimizing the prediction residuals and physiological biases, the convergence of the iterative calibration process is accelerated and its robustness is improved, avoiding the parameter drift and overfitting risks caused by purely empirical calibration. Ultimately, this significantly improves the overall model's accuracy and generalization ability in individualized blood pressure prediction.

[0040] Step S40: Obtain pulse wave velocity from multiple standard physiological signals, calculate the Young's modulus of the arterial wall based on the pulse wave velocity, construct a pulse wave velocity equation, add the Young's modulus of the arterial wall to the pulse wave velocity equation, and obtain a specific expression for the arterial compliance value C parameter.

[0041] Existing technologies for blood pressure monitoring rely solely on either the MK equation or the WK model. The former focuses on describing the relationship between pulse wave velocity and vascular elasticity, while the latter focuses on the transient dynamic response of pressure and flow. Neither can simultaneously reflect the spatial distribution characteristics of vascular elasticity changes nor the temporal characteristics of cardiac pumping and peripheral resistance, resulting in limited accuracy in blood pressure estimation and an inability to comprehensively characterize the true physiological processes of the cardiovascular system. Therefore, this invention, based on the construction and optimization of the WK model to obtain a predictive model, further introduces the MK equation, achieving a fusion of the MK equation and the WK model. This not only overcomes the insufficient predictive accuracy of the arterial compliance value C parameter in the single WK model but also establishes the relationship between vascular elasticity, vascular geometry, and pulse wave velocity, enabling a description of vascular compliance and achieving high-precision, stable, continuous, and interpretable monitoring of blood pressure.

[0042] Specifically, the pulse wave velocity is obtained based on the peak time difference in the standard photoplethysmography pulse wave or the pulse conduction time in the standard electrocardiogram, and the Young's modulus of the arterial wall is calculated based on the calculation expression of the pulse wave velocity. ; ; in, Indicates pulse wave velocity, This represents the Young's modulus of the arterial wall. Indicates thick arterial wall. Indicates blood density, Represent the artery radius; construct the pulse wave velocity equation: ; ; ; in, The C parameter represents the arterial compliance value. Indicates volume change, Indicates pressure changes. Indicates the length of the blood vessel. This represents the change in arterial radius; by adding the Young's modulus of the arterial wall to the pulse velocity equation, a specific expression for the arterial compliance value C parameter is constructed: ; in, This indicates the volume of the cavity.

[0043] The MK equation allows for the construction of an expression to calculate pulse wave velocity, which in turn enables the derivation of Young's modulus of the arterial wall. Pulse wave velocity is the reciprocal of PTT (pulse time to pulse), obtainable from standard ECG and PPG signals. Specifically, the cardiac cycle is divided based on the R-peak of the standard ECG signal. Then, the standard ECG and PPG signals are segmented into cardiac cycles, with PPT representing the event from the R-peak to the peak of the standard PPG signal. Once PPT is obtained, the pulse wave velocity can be derived.

[0044] Furthermore, in the MK equation, the unit of pulse wave velocity is m / s; the unit of Young's modulus of the arterial wall is Pa, reflecting the stiffness characteristics of the arterial wall; blood density is approximately 1050 kg / m³; and the unit of arterial radius is m. After deriving the Young's modulus of the arterial wall, an expression for calculating the arterial compliance C-parameter (i.e., the ratio of volume change to pressure change) is further constructed. For an artery simulated as a circular tube, the change in arterial radius is imported into this expression to obtain the volume change. Combined with the expression for calculating the arterial compliance C-parameter, the arterial compliance C-parameter can be calculated. This provides a calibration method for the arterial compliance C-parameter based on the physical mechanism of pulse wave propagation. Moreover, individual differences are considered in the calculation, enabling individualized adjustment of the arterial compliance C-parameter to ensure its physiological rationality.

[0045] Step S50: Calculate the arterial compliance value C parameter of the target object according to the specificity expression, adjust the model compliance C parameter in the prediction model, and use the adjusted prediction model to monitor the blood pressure of the target object to obtain a continuous blood pressure estimate.

[0046] Specifically, based on the target object's blood density, pulse wave velocity, and lumen volume, the arterial compliance value C parameter of the target object is calculated; the model compliance C parameter in the prediction model is adjusted using the arterial compliance value C parameter to obtain the final prediction model; the blood pressure of the target object is continuously monitored using the final prediction model to obtain a continuous blood pressure estimate.

[0047] Specifically, the arterial compliance value C parameter calculated from the MK equation is used as a soft physiological constraint on the model compliance C parameter to correct it. By constraining the arterial compliance value C parameter, the model's predicted pressure and clinically observed pressure are jointly optimized, rather than through unconstrained fitting or a single loss function as in existing technologies. This ensures that the model compliance C parameter is always anchored to non-invasive physiological measurements, thereby avoiding the problem of parameters being out of sync with actual arterial elasticity in existing technologies and improving the biomechanical realism of the model.

[0048] Furthermore, such as Figure 3 As shown, besides the C parameter of model compliance (Figure 3 In addition to correcting the C parameter in the model, the resistance R predicted by waveform features is also used as input to monitor the blood pressure of the target object together with the model compliance C parameter, thereby improving the accuracy and biological authenticity of blood pressure prediction for different objects.

[0049] Furthermore, such as Figure 5 As shown, based on the above-mentioned continuous blood pressure monitoring method based on the elastic cavity model and pulse wave velocity equation, the present invention also provides a continuous blood pressure monitoring system based on the elastic cavity model and pulse wave velocity equation, wherein the continuous blood pressure monitoring system based on the elastic cavity model and pulse wave velocity equation includes: The parameter acquisition module 51 is used to acquire multiple physiological signals of the target object, preprocess all the physiological signals to obtain corresponding standard physiological signals, and obtain multiple conventional parameters from all the standard physiological signals. The feature extraction module 52 is used to construct an elastic cavity model, add all the conventional parameters to the elastic cavity model to construct a state equation, and extract features from all standard physiological signals to obtain morphological features, statistical features and sequence features. The model training module 53 is used to construct the mapping between the morphological features, the statistical features, and the sequence features and multiple parameters in the state equation, to obtain a training sample set, and to use the training sample set to train the elastic cavity model to obtain a prediction model. The constraint parameter construction module 54 is used to obtain pulse wave velocity from multiple standard physiological signals, calculate the Young's modulus of the arterial wall based on the pulse wave velocity, construct a pulse wave velocity equation, add the Young's modulus of the arterial wall to the pulse wave velocity equation, and obtain a specific expression for the arterial compliance value C parameter. The blood pressure monitoring module 55 is used to calculate the arterial compliance value C parameter of the target object according to the specific expression, adjust the model compliance C parameter in the prediction model, and use the adjusted prediction model to monitor the blood pressure of the target object to obtain a continuous blood pressure estimate.

[0050] Furthermore, such as Figure 6 As shown, based on the above-mentioned continuous blood pressure monitoring method and system based on the elastic cavity model and pulse wave velocity equation, the present invention also provides a terminal, which includes a processor 10, a memory 20 and a display 30. Figure 6 Only some of the terminal components are shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0051] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard disk or memory. In other embodiments, the memory 20 may be an external storage device of the terminal, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc. Further, the memory 20 may include both internal and external storage devices. The memory 20 is used to store application software and various types of data installed on the terminal, such as the program code installed on the terminal. The memory 20 can also be used to temporarily store data that has been output or will be output. In one embodiment, the memory 20 stores a continuous blood pressure monitoring program 40 based on an elastic cavity model and pulse wave velocity equation. This continuous blood pressure monitoring program 40 can be executed by the processor 10, thereby implementing the continuous blood pressure monitoring method based on the elastic cavity model and pulse wave velocity equation of this application.

[0052] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run program code stored in the memory 20 or process data, such as executing the continuous blood pressure monitoring method based on the elastic cavity model and pulse wave velocity equation.

[0053] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display 30 is used to display information on the terminal and to display a visual user interface. The components of the terminal communicate with each other via a system bus.

[0054] In one embodiment, when the processor 10 executes the continuous blood pressure monitoring program 40 based on the elastic cavity model and pulse wave velocity equation in the memory 20, it implements the steps of the continuous blood pressure monitoring method based on the elastic cavity model and pulse wave velocity equation as described above.

[0055] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a continuous blood pressure monitoring program based on an elastic cavity model and a pulse wave velocity equation, wherein when the continuous blood pressure monitoring program based on an elastic cavity model and a pulse wave velocity equation is executed by a processor, it implements the steps of the continuous blood pressure monitoring method based on an elastic cavity model and a pulse wave velocity equation as described above.

[0056] In summary, this invention provides a continuous blood pressure monitoring method and related equipment based on an elastic cavity model and pulse wave velocity equation. The method includes: acquiring multiple physiological signals of a target object; preprocessing all the physiological signals to obtain corresponding standard physiological signals; and obtaining multiple conventional parameters from all the standard physiological signals; constructing an elastic cavity model; adding all the conventional parameters to the elastic cavity model to construct a state equation; extracting features from all the standard physiological signals to obtain morphological features, statistical features, and sequence features; and constructing the relationship between the morphological features, statistical features, and sequence features and the multiple parameters in the state equation. A mapping between numbers is used to obtain a training sample set, which is then used to train the elastic cavity model to obtain a prediction model. Pulse wave velocities are obtained from multiple standard physiological signals, and the Young's modulus of the arterial wall is calculated based on these velocities. A pulse wave velocity equation is constructed, and the Young's modulus is added to this equation to obtain a specific expression for the arterial compliance value C parameter. The arterial compliance value C parameter of the target object is calculated based on this specific expression, and the model compliance C parameter in the prediction model is adjusted. The adjusted prediction model is then used to monitor the blood pressure of the target object to obtain continuous blood pressure estimates. This invention introduces individualized physiological information, improving the accuracy and biological realism of blood pressure prediction for different subjects.

[0057] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal that includes that element.

[0058] Of course, those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.). The program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The computer-readable storage medium can be a memory, magnetic disk, optical disk, etc.

[0059] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A continuous blood pressure monitoring method based on an elastic cavity model and a pulse wave velocity equation, characterized in that, The continuous blood pressure monitoring method based on the elastic cavity model and pulse wave velocity equation includes: Multiple physiological signals of the target object are collected, all of the physiological signals are preprocessed to obtain corresponding standard physiological signals, and multiple conventional parameters are obtained from all of the standard physiological signals. An elastic cavity model is constructed, and all the conventional parameters are added to the elastic cavity model to construct a state equation. Feature extraction is performed on all standard physiological signals to obtain morphological features, statistical features, and sequence features. A mapping is constructed between the morphological features, statistical features, and sequence features and multiple parameters in the state equation to obtain a training sample set. The training sample set is then used to train the elastic cavity model to obtain a prediction model. Pulse wave velocity is obtained from multiple standard physiological signals, the Young's modulus of the arterial wall is calculated based on the pulse wave velocity, a pulse wave velocity equation is constructed, and the Young's modulus of the arterial wall is added to the pulse wave velocity equation to obtain a specific expression for the arterial compliance value C parameter. The arterial compliance value C parameter of the target object is calculated based on the specific expression, and the model compliance C parameter in the prediction model is adjusted. The blood pressure of the target object is monitored using the adjusted prediction model to obtain a continuous blood pressure estimate.

2. The continuous blood pressure monitoring method based on the elastic cavity model and pulse wave velocity equation according to claim 1, characterized in that, The conventional parameters include: cardiac contraction and ejection time, cardiac diastolic time, cardiac cycle time, and blood flow parameters; The process involves collecting multiple physiological signals from the target object, preprocessing all the physiological signals to obtain corresponding standard physiological signals, and extracting multiple conventional parameters from all the standard physiological signals, specifically including: Data is collected from the target object using multiple sensors to obtain photoplethysmography (PPG), electrocardiogram (ECG), and cardiac impedance cardiogram (CIK). The first noise in the photoplethysmography (PPG), electrocardiogram (ECG), and impedance cardiogram is filtered out using a first filter, and the second noise in the PPG, ECG, and impedance cardiogram is filtered out using a second filter, resulting in a standard PPG, standard ECG, and standard impedance cardiogram. Extract the cardiac systolic ejection time, cardiac diastolic time, and cardiac cycle time of the target object from the standard photoplethysmography pulse wave; Extract the blood flow parameters of the target object from the standard impedance cardiogram: ； in, This represents blood flow parameters. Indicates blood density, and These respectively represent blood from Flowing to Point and Time at point, and They represent Point and The arterial wall is thick at the point. Indicates the length of the blood vessel. Indicates the starting point of the monitoring.

3. The continuous blood pressure monitoring method based on the elastic cavity model and pulse wave velocity equation according to claim 2, characterized in that, The construction of the elastic cavity model involves adding all the conventional parameters to the elastic cavity model to construct a state equation, and extracting features from all standard physiological signals to obtain morphological features, statistical features, and sequence features. Specifically, this includes: Construct an elastic cavity model of the target object, and add the cardiac systolic ejection time, the cardiac diastolic time, and the cardiac cycle time to the elastic cavity model: ； ； in, Indicates the first Current input at any given time, Indicates cardiac output. Indicates the duration of the cardiac cycle. It indicates the duration of cardiac contraction and ejection. A time index representing the entire cardiac cycle. Indicates the number of cardiac cycles. The C-parameter represents the model's compliance. Indicates the first Voltage at time, Indicates resistance; Based on the cardiac cycle time, statistical features are obtained by statistically analyzing the standard photoplethysmography pulse wave, the standard electrocardiogram, and the standard impedance cardiogram. The morphological features are then obtained by averaging the statistical features. Acquire photoplethysmography (PPG) signals or impedance cardiogram (ETC) signals from multiple cardiac cycles, calculate the variation signals of the PPG signals or ETC signals, and obtain sequence characteristics.

4. The continuous blood pressure monitoring method based on the elastic cavity model and pulse wave velocity equation according to claim 2, characterized in that, The process of constructing a mapping between the morphological features, statistical features, and sequence features and multiple parameters in the state equation to obtain a training sample set, and then using the training sample set to train the elastic cavity model to obtain a prediction model, specifically includes: For the cardiac cycle time, a preset algorithm is used to construct a mapping from the morphological features, statistical features, and sequence features to the resistance and initial model compliance C parameter in the state equation, resulting in multiple feature-parameter pairs; All the feature-parameter pairs are divided into a training sample set, a validation sample set, and a test sample set, wherein the validation sample set is used to validate the prediction model, and the test sample set is used to test the prediction model. The training sample set is input into the elastic cavity model for prediction. A weighted loss term is constructed based on the prediction results and each actual value in the training sample set. The elastic cavity model is iteratively optimized based on the weighted loss term to obtain the prediction model.

5. The continuous blood pressure monitoring method based on the elastic cavity model and pulse wave velocity equation according to claim 4, characterized in that, The step of constructing a weighted loss term based on the prediction results and each actual value in the training sample set, and iteratively optimizing the elastic cavity model based on the weighted loss term to obtain the prediction model, specifically includes: A weighted loss term is constructed based on the actual value of each training sample in the training sample set and the prediction result: ； ； in, Indicates the weighted loss term. This represents the total number of training samples. Indicates the first Each sample weight, Indicates the first One actual value, Indicates the prediction result. Indicates the first training samples, Indicates the first Weights for the next iteration Indicates the first In the next iteration, the training samples are input into the output value of the weak regressor. This represents the input training samples. Indicates the number of iterations. This represents the median; The sample weights are initialized, and the initial pseudo-residuals are calculated to train the weak regressor, obtaining the minimized weighted squared error: ； in, Indicates the first The first iteration in the round Individual sample weights; ； in, Indicates the first The first iteration in the round The pseudo residuals of each training sample Indicates the first Prediction results in round iteration; ； in, Indicates the first Minimize the weighted squared error during rounds of iterative training. Indicates the first The first round of iterative training The sample weights of each sample. Indicates the first In the nth iteration Each training sample is input into the output value of the weak regressor; An absolute error is constructed based on the output value and the corresponding pseudo-residual, and the average weighted loss is calculated using the absolute error and the sample weights of the current round: ； ； in, Indicates the first In the nth iteration One absolute error, Indicates the first The average weighted loss of the next iteration Indicates the first In the nth iteration Individual sample weights; For the current round, the learner weights are calculated using the average weighted loss, and the sample weights are updated using these learner weights to obtain the sample weights for the next round. This process continues until the updated parameters that minimize the weighted loss term are obtained, and the prediction model is constructed using these updated parameters. ； ； in, Indicates the first Learner weights in the next iteration Indicates the first The th iteration in the Individual sample weights.

6. The continuous blood pressure monitoring method based on the elastic cavity model and pulse wave velocity equation according to claim 2, characterized in that, The process of obtaining pulse wave velocities from multiple standard physiological signals, calculating the Young's modulus of the arterial wall based on the pulse wave velocities, constructing a pulse wave velocity equation, and adding the Young's modulus of the arterial wall to the pulse wave velocity equation to obtain a specific expression for the arterial compliance value C parameter specifically includes: Based on the peak time difference in the standard photoplethysmography pulse wave or the pulse conduction time in the standard electrocardiogram, the pulse wave velocity is obtained, and the Young's modulus of the arterial wall is calculated according to the calculation expression of the pulse wave velocity. ； ； in, Indicates pulse wave velocity, This represents the Young's modulus of the arterial wall. Indicates thick arterial wall. Indicates blood density, Indicates the radius of the artery; Constructing the pulse wave velocity equation: ； ； ； in, The C parameter represents the arterial compliance value. Indicates volume change, Indicates pressure changes. Indicates the length of the blood vessel. Indicates changes in arterial radius; By adding the Young's modulus of the arterial wall to the pulse velocity equation, a specific expression for the arterial compliance value C parameter is constructed: ； in, This indicates the volume of the cavity.

7. The continuous blood pressure monitoring method based on the elastic cavity model and pulse wave velocity equation according to claim 1, characterized in that, The step of calculating the arterial compliance value C parameter of the target object based on the specificity expression, adjusting the model compliance C parameter in the prediction model, and using the adjusted prediction model to monitor the blood pressure of the target object to obtain continuous blood pressure estimates specifically includes: The arterial compliance value C parameter of the target object is calculated based on the blood density, pulse wave velocity, and lumen volume of the target object. The model compliance C parameter in the prediction model is adjusted using the arterial compliance value C parameter to obtain the final prediction model; The blood pressure of the target object is continuously monitored using the final prediction model to obtain continuous blood pressure estimates.

8. A continuous blood pressure monitoring system based on an elastic cavity model and a pulse wave velocity equation, characterized in that, The continuous blood pressure monitoring system based on the elastic cavity model and pulse wave velocity equation is used to implement the continuous blood pressure monitoring method based on the elastic cavity model and pulse wave velocity equation as described in any one of claims 1-7, wherein the continuous blood pressure monitoring system based on the elastic cavity model and pulse wave velocity equation comprises: The parameter acquisition module is used to acquire multiple physiological signals of the target object, preprocess all the physiological signals to obtain corresponding standard physiological signals, and obtain multiple conventional parameters from all the standard physiological signals. The feature extraction module is used to construct an elastic cavity model, add all the conventional parameters to the elastic cavity model to construct a state equation, and extract features from all standard physiological signals to obtain morphological features, statistical features and sequence features. The model training module is used to construct the mapping between the morphological features, the statistical features, and the sequence features and multiple parameters in the state equation, to obtain a training sample set, and to train the elastic cavity model using the training sample set to obtain a prediction model. The constraint parameter construction module is used to obtain pulse wave velocity from multiple standard physiological signals, calculate the Young's modulus of the arterial wall based on the pulse wave velocity, construct the pulse wave velocity equation, add the Young's modulus of the arterial wall to the pulse wave velocity equation, and obtain a specific expression for the arterial compliance value C parameter. The blood pressure monitoring module is used to calculate the arterial compliance value C parameter of the target object according to the specific expression, adjust the model compliance C parameter in the prediction model, and use the adjusted prediction model to monitor the blood pressure of the target object to obtain continuous blood pressure estimates.

9. A terminal, characterized in that, The terminal includes: a memory, a processor, and a continuous blood pressure monitoring program based on an elastic cavity model and a pulse wave velocity equation stored in the memory and executable on the processor. When the continuous blood pressure monitoring program based on an elastic cavity model and a pulse wave velocity equation is executed by the processor, it implements the steps of the continuous blood pressure monitoring method based on an elastic cavity model and a pulse wave velocity equation as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a continuous blood pressure monitoring program based on an elastic cavity model and a pulse wave velocity equation. When the continuous blood pressure monitoring program based on the elastic cavity model and the pulse wave velocity equation is executed by a processor, it implements the steps of the continuous blood pressure monitoring method based on the elastic cavity model and the pulse wave velocity equation as described in any one of claims 1-7.

Images