Continuous blood pressure monitoring method based on multiple physiological signals and electric network and related equipment

Abstract

The invention discloses a continuous blood pressure monitoring method based on multiple physiological signals and an electric network and related equipment. An electric network model is constructed in advance based on the first resistor, the second resistor, the capacitor and the inductor; acquiring a physiological signal set of the user; predicting a model parameter combination of the electrical network model through the prediction model according to the signal features of the physiological signal set; the prediction model parameter combination is applied to the electric network model, a corresponding simulated blood pressure waveform is generated through simulation, and a blood pressure monitoring result of the user is determined according to the simulated blood pressure waveform. According to the method, the signal characteristics related to the blood pressure change are extracted from the multiple physiological signals, the model parameters of the electric network model are predicted, and non-invasive continuous blood pressure monitoring is achieved according to the relation between the model parameters and the blood pressure change. The multiple physiological signals can provide more comprehensive information by fully utilizing the advantages and complementary characteristics of different physiological signals, and the electrical network model can simulate the change of a cardiovascular system, so that the accuracy and reliability of continuous blood pressure monitoring are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of continuous blood pressure detection, and in particular to a continuous blood pressure monitoring method based on multiple physiological signals and an electrical network model and related equipment. Background Art

[0002] Blood pressure, as a vital physiological indicator in the human circulatory system, plays a vital role in maintaining the balance of the cardiovascular system. Accurate and timely blood pressure monitoring is critical to preventing hypertension and related cardiovascular diseases.

[0003] At present, common continuous non-invasive blood pressure monitoring methods cover a variety of technologies, including pressure sensor-based technology, ultrasound-based technology, and photoplethysmography-based technology. Continuous non-invasive blood pressure monitoring can more comprehensively and conveniently reflect an individual's daily blood pressure changes, blood pressure levels at different times, blood pressure control conditions, and blood pressure trends after specific training.

[0004] However, existing non-invasive continuous blood pressure detection methods generally use a single signal, which is difficult to fully reflect the condition of the cardiovascular system, thus affecting the accuracy of continuous blood pressure monitoring. In addition, existing methods are still insufficient in clinical physiological interpretation, and have limited ability to reflect cardiovascular system and hemodynamic information, making it difficult to reveal the impact of changes in the cardiovascular system on blood pressure.

[0005] Therefore, the existing technology still needs to be improved and developed. Summary of the invention

[0006] The technical problem to be solved by the present invention is that, in view of the above-mentioned defects of the prior art, a continuous blood pressure monitoring method and related equipment based on multiple physiological signals and electrical networks are provided, aiming to solve the problem that the existing non-invasive continuous blood pressure detection methods generally use a single signal, which is difficult to fully reflect the condition of the cardiovascular system, thereby affecting the accuracy of continuous blood pressure monitoring.

[0007] The technical solution adopted by the present invention to solve the problem is as follows:

[0008] In a first aspect, an embodiment of the present invention provides a continuous blood pressure monitoring method based on multiple physiological signals and an electrical network, the method comprising:

[0009] Acquire an electrical network model pre-constructed based on a first resistor, a second resistor, a capacitor, and an inductor; wherein the current input corresponding to the electrical network model is determined based on a cardiac cycle time, a cardiac systolic ejection time, and a cardiac output; the model parameters of the electrical network model include a variable heart rate, a stroke volume, a first resistance value, and a capacitance value, and a constant cardiac systolic ejection time, a second resistance value, and an inductor value;

[0010] Acquire a physiological signal set of a target user; wherein the physiological signal set includes a plurality of physiological signals of different types;

[0011] Extracting features from the physiological signal set, and predicting model parameters of the electrical network model according to signal features of the physiological signal set using a preset prediction model to obtain a prediction model parameter combination;

[0012] The prediction model parameter combination is applied to the electrical network model, and a corresponding target simulated blood pressure waveform is simulated to generate a corresponding blood pressure monitoring result of the target user is determined according to the target simulated blood pressure waveform.

[0013] In a second aspect, an embodiment of the present invention provides a continuous blood pressure monitoring system based on multiple physiological signals and an electrical network, the system comprising:

[0014] An electrical network model, wherein the electrical network model is pre-constructed based on a first resistor, a second resistor, a capacitor and an inductor; the current input corresponding to the electrical network model is determined based on a cardiac cycle time, a cardiac systolic ejection time and a cardiac output; the model parameters of the electrical network model include a variable heart rate, a stroke volume, a first resistance value and a capacitance value, and a constant cardiac systolic ejection time, a second resistance value and an inductor value;

[0015] A signal acquisition module, used to acquire a physiological signal set of a target user; wherein the physiological signal set includes a plurality of physiological signals of different types;

[0016] A signal processing module, used for extracting features from the physiological signal set, and predicting model parameters of the electrical network model according to the signal features of the physiological signal set through a preset prediction model to obtain a prediction model parameter combination;

[0017] The data simulation module is used to apply the prediction model parameter combination to the electrical network model, and simulate and generate a corresponding target simulated blood pressure waveform, and determine the blood pressure monitoring result corresponding to the target user according to the target simulated blood pressure waveform.

[0018] In a third aspect, an embodiment of the present invention provides a terminal, comprising a memory and one or more processors; the memory stores one or more programs; the program includes instructions for executing any of the continuous blood pressure monitoring methods based on multiple physiological signals and electrical networks as described above; and the processor is used to execute the program.

[0019] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium on which multiple instructions are stored, characterized in that the instructions are suitable for being loaded and executed by a processor to implement the steps of any of the above-mentioned continuous blood pressure monitoring methods based on multiple physiological signals and electrical networks.

[0020] Beneficial effects of the present invention: The embodiment of the present invention extracts signal features related to blood pressure changes from multiple physiological signals, predicts model parameters of the electrical network model, and implements non-invasive and continuous blood pressure monitoring based on the relationship between the model parameters and blood pressure changes. Multiple physiological signals can make full use of the advantages and complementary characteristics of different physiological signals to provide more comprehensive information, and the electrical network model can simulate changes in the cardiovascular system, thereby enhancing the accuracy and reliability of continuous blood pressure monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 It is a schematic flow chart of the steps of a continuous blood pressure monitoring method based on multiple physiological signals and electrical networks provided in an embodiment of the present invention.

[0023] Figure 2 It is a structural schematic diagram of an electrical network model provided by an embodiment of the present invention.

[0024] Figure 3 It is a schematic diagram of some morphological features provided by an embodiment of the present invention.

[0025] Figure 4 It is a schematic diagram of a partial simulated blood pressure waveform provided by an embodiment of the present invention.

[0026] Figure 5 It is a schematic diagram of matching a simulation signal and a real signal provided by an embodiment of the present invention.

[0027] Figure 6 It is a module schematic diagram of a continuous blood pressure monitoring system based on multiple physiological signals and electrical networks provided in an embodiment of the present invention.

[0028] Figure 7 It is a principle block diagram of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The present invention discloses a continuous blood pressure monitoring method based on multiple physiological signals and an electrical network and related equipment. 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.

[0030] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The term "and / or" used herein includes all or any unit and all combinations of one or more associated listed items.

[0031] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as herein.

[0032] In view of the above-mentioned defects of the prior art, the present invention provides a continuous blood pressure monitoring method based on multiple physiological signals and an electrical network, wherein the method obtains an electrical network model pre-constructed based on a first resistor, a second resistor, a capacitor and an inductor; wherein the current input corresponding to the electrical network model is determined based on a cardiac cycle time, cardiac systolic ejection time and cardiac output; the model parameters of the electrical network model include variable heart rate, stroke volume, first resistance value and capacitance value, constant cardiac systolic ejection time, second resistance value and inductance value; obtain a physiological signal set of a target user; wherein the physiological signal set includes several physiological signals of different types; extract features from the physiological signal set, and predict the model parameters of the electrical network model according to the signal features of the physiological signal set through a preset prediction model to obtain a prediction model parameter combination; apply the prediction model parameter combination to the electrical network model, and simulate and generate a corresponding target simulated blood pressure waveform, and determine the corresponding blood pressure monitoring result of the target user according to the target simulated blood pressure waveform. The present invention extracts signal features related to blood pressure changes from multiple physiological signals, predicts model parameters of the electrical network model, and realizes non-invasive and continuous blood pressure monitoring according to the relationship between model parameters and blood pressure changes. Multiple physiological signals can make full use of the advantages and complementary characteristics of different physiological signals to provide more comprehensive information, and the electrical network model can simulate the changes in the cardiovascular system, thereby enhancing the accuracy and reliability of continuous blood pressure monitoring.

[0033] like Figure 1 As shown, the method specifically comprises the following steps:

[0034] Step S100, obtaining an electrical network model pre-constructed based on a first resistor, a second resistor, a capacitor and an inductor; wherein the current input corresponding to the electrical network model is determined based on a cardiac cycle time, a cardiac contraction ejection time, and a cardiac output; the model parameters of the electrical network model include a variable heart rate, a stroke volume, a first resistance value and a capacitance value, and a constant cardiac contraction ejection time, a second resistance value and an inductor value.

[0035] Specifically, the electrical network model is a mathematical model that reflects the physiological characteristics of the cardiovascular system. The model can mathematically link the blood flow in the arterial blood vessels to the blood pressure. The electrical network model pre-constructed in this embodiment is composed of two resistors, a capacitor and an inductor. The model parameters of the electrical network model are divided into two categories: heart rate (HR), stroke volume (SV), first resistance value (R1) and capacitance value (C) are variable model parameters; cardiac systolic ejection time (T s ), the second resistance value (R2) and the inductance value (L) are preset constant model parameters. The current input of the electrical network model will be based on the cardiac cycle time (T c ), cardiac systolic ejection time (T s ), and cardiac output (CO).

[0036] For example, the electrical network model has the following parameters: T s : Cardiac systolic ejection time; T c : The time of a cardiac cycle, which is equivalent to the heart rate (HR); CO: Cardiac output, which can be combined with HR to obtain the stroke volume (SV); R1: The value of the first resistor; R2: The value of the second resistor; C: The value of the capacitor; L: The value of the inductor. Among the above parameters, the parameter that needs to be preset as a constant is T s , R2 and L can be preset to 0.25, 0.05 and 0.005 respectively.

[0037] The current input of the circuit is I(t), and its change expression is shown in the following formula, where T c is the time of one cardiac cycle, T s is the cardiac contraction ejection time, CO is the cardiac output (i.e. the amount of blood ejected by the heart per minute), and n is the nth cardiac cycle.

[0038]

[0039]

[0040] The electrical network model can be implemented in Simulink simulation, or its state equation can be derived based on the principle, such as the following formula for programming:

[0041]

[0042] Where P(t) is the voltage.

[0043] This embodiment is simulated and implemented using Simulink.

[0044] In another implementation, electrical network model parameters that are inconsistent with the present embodiment may be preset.

[0045] In one implementation, the first resistor is connected in parallel with the inductor to form a first parallel module; the second resistor is connected in parallel with the capacitor to form a second parallel module; and the first parallel module and the second parallel module are connected in series.

[0046] Specifically, this embodiment uses the first resistor, the second resistor, the capacitor and the inductor to design a four-element second-order electrical network model. Figure 2 As shown, the first resistor R1 is connected in parallel with the inductor L, the second resistor R2 is connected in parallel with the capacitor C, and then the two parts are connected in series. This embodiment uses a four-component electrical network for modeling, which can effectively simulate the cardiovascular system, more deeply understand the impact of changes in the cardiovascular system on changes in blood pressure, and explore the impact of changes in different parameter values ​​of the electrical network model on changes in blood pressure, and further analyze the differences in model parameters for users with different blood pressure conditions.

[0047] In one implementation, the analogy relationship between the model parameters of the four-element electrical network model and the physiological characteristics of the cardiovascular system is: total peripheral resistance is analogous to resistance; arterial compliance is analogous to capacitance; blood inertia is analogous to inductance; blood flow from the ventricle to the artery is analogous to time-varying current; and arterial pressure waves are analogous to time-varying voltage.

[0048] Specifically, the elasticity of the human blood vessel wall and the peripheral resistance of the blood vessel have a regular effect on the formation of physiological signals, and the characteristics of the above-mentioned multiple physiological signals can reflect the numerical changes of the electrical network model parameters resistance, capacitance and inductance to a certain extent. Therefore, the electrical network model used in this embodiment can simulate the cardiovascular system, that is, effectively model the model, so that the model has strong interpretability and can accurately and reliably realize continuous blood pressure monitoring.

[0049] In another implementation, different electrical network models may be designed, such as a five-element or six-element electrical network model.

[0050] Step S200: Acquire a physiological signal set of a target user; wherein the physiological signal set includes a plurality of physiological signals of different types.

[0051] Specifically, multiple physiological signals can make full use of the advantages and complementary characteristics of different physiological signals to provide more comprehensive information. Therefore, for users who need to perform continuous blood pressure monitoring, this embodiment first needs to obtain several physiological signals of different types from the user, that is, to obtain a physiological signal set. Subsequently, continuous blood pressure monitoring can be performed using the user's physiological signal set, which can effectively enhance the accuracy and reliability of continuous blood pressure monitoring.

[0052] In one implementation, after obtaining the physiological signal set of the target user, the method further includes:

[0053] Each physiological signal in the physiological signal set is preprocessed to remove noise; wherein the preprocessing includes: processing low-frequency interference in each physiological signal through wavelet transform, and processing high-frequency interference in each physiological signal through a filter of a preset frequency.

[0054] Specifically, since the acquired physiological signals are often accompanied by certain noise, preprocessing is crucial. Noise is mainly divided into low-frequency and high-frequency interference: low-frequency interference is mainly manifested as baseline drift of the waveform, which is mainly derived from the low-frequency signal generated by human breathing; high-frequency interference is mainly manifested as small-amplitude undulation burrs on the waveform signal, and its sources include myoelectric noise generated by normal physiological activities of human tissue and electrical signal interference of the acquisition equipment itself. In the process of signal preprocessing, it is necessary to reduce baseline drift and power frequency interference as much as possible while retaining the waveform characteristics of the original signal to improve the quality of the signal. For low-frequency noise, such as respiratory movement, which can cause changes in the volume of the chest and abdomen, thereby causing changes in the signal, this embodiment uses wavelet transform for processing to reduce the impact of respiratory interference on the signal. For high-frequency noise, this embodiment uses a preset frequency filter for processing, such as a 4th-order Butterworth filter with a cutoff frequency of 10Hz for processing to eliminate high-frequency noise in the signal.

[0055] Step S300: extracting features from the physiological signal set, and predicting model parameters of the electrical network model according to the signal features of the physiological signal set using a preset prediction model to obtain a prediction model parameter combination.

[0056] Specifically, this embodiment pre-builds a prediction model using a machine learning algorithm, the input data of the prediction model is the signal characteristics of multiple physiological signals, and the output data is a combination of model parameters predicted based on the signal characteristics of multiple physiological signals. This embodiment obtains specific parameters of the electrical network model from the characteristics of multiple physiological signals through the prediction model, thereby realizing accurate continuous blood pressure monitoring using the electrical network model.

[0057] In one implementation, extracting features from the physiological signal set includes:

[0058] Dividing each physiological signal in the physiological signal set according to the cardiac cycle to obtain signal segments corresponding to each physiological signal;

[0059] Each of the signal segments is divided into multiple rounds for feature extraction, wherein each of the signal segments in the same round corresponds to the same cardiac cycle.

[0060] Specifically, this embodiment divides each physiological signal into signal segments according to the cardiac cycle, and extracts signal features from the signal segments divided based on the cardiac cycle, so that the electrical network model can update model parameters according to the cardiac cycle beats, thereby realizing non-invasive blood pressure monitoring according to the cardiac cycle.

[0061] In one implementation, the physiological signal set includes an ECG signal, an ICG signal, and a PPG signal; and the prediction model is used to:

[0062] predicting the heart rate of the electrical network model according to the signal characteristics of the ECG signal;

[0063] predicting the beat-per-beat output of the electrical network model according to the signal characteristics of the ICG signal;

[0064] Predicting a first resistance value and a first capacitance value of the electrical network model according to a signal characteristic of the PPG signal.

[0065] Specifically, multiple physiological signals can fully reflect the changes in physiological information of the human body and carry information about changes in blood pressure. The multiple physiological signals used in this embodiment, that is, the physiological signals include: electrocardiogram (ECG), photoelectric volumetric pulse wave (PPG) and cardiac impedance (ICG). Among them, the ECG signal can reflect the electrical activity of the heart and the rhythm of the whole heart beat; the PPG signal can reflect the situation of peripheral arterial blood vessels and blood flow; the ICG signal can reflect the changes in blood flow in the chest cavity (aorta) and the volume changes of blood. The three physiological signals carry different information respectively, and the complementarity between the signals can more comprehensively reflect the situation of the human body, especially the cardiovascular system. Combining the three signals can more accurately monitor changes in blood pressure. In actual application scenarios, for ECG signals, it is mainly used for the segmentation of cardiac cycles, and the time of cardiac cycles is equivalent to heart rate, so signal features can be extracted from ECG signals for setting the heart rate in the electrical network model parameters. For ICG signals, it is mainly used to analyze cardiac function and calculate blood flow parameters, and the stroke volume in the electrical network model parameters can be calculated according to ICG signals. The PPG signal mainly reflects the conditions of the peripheral arterial blood vessels and blood flow, so features can be extracted from the PPG signal to set the first resistance value and capacitance value in the electrical network model parameters.

[0066] For example, after preprocessing each physiological signal, feature point extraction will be performed:

[0067] For ECG signals, we mainly focus on its R peak for segmentation of cardiac cycle.

[0068] For ICG signals, we mainly focus on points C, B, and X in the signal. These characteristic points are crucial for analyzing cardiac function and calculating blood flow parameters. SV can be obtained from ICG signals, as shown in the following formula:

[0069]

[0070] LVET=t X -t B , (6);

[0071] Where, LVET is left ventricular ejection time; h c is the amplitude at point C; h B is the amplitude at point B; t X is the time of point X; t B The moment of point B.

[0072] For PPG signals, we mainly focus on one or more of the following features: morphological features, derivative features, statistical features, sequence features, and frequency domain features. Compared with the traditional method of studying the waveform features and morphological features based on physiological signals, we can dig out deeper features from multiple levels such as morphological features, derivative features, statistical features, sequence features, and frequency domain features.

[0073] The morphological features may include one or more of the following features: the amplitude of the starting point, the amplitude of the peak point, the duration of the systolic period, the duration of the diastolic period, the amplitude and duration of the systolic period at 10%, 25%, 33%, 50%, 66%, 75%, the amplitude and duration of the diastolic period at 10%, 25%, 33%, 50%, 66%, 75%, etc. 38 features, some of which are as follows Figure 3 shown.

[0074] The derivative features may include one or more of the following features: the time at which the maximum value of the first-order derivative is located and the amplitude in the original signal, the time at which the maximum value of the second-order derivative is located and the amplitude in the original signal, etc. 12 features.

[0075] Statistical features may include one or more of the following features: signal mean, variance, skewness, and other 9 features.

[0076] The sequence features may include one or more of the following features: 15 features such as approximate entropy and sample entropy 4 seconds before and after the cycle.

[0077] The frequency domain features may include one or more of the following features: the sum of spectrum energy, maximum spectrum energy, and other 14 features.

[0078] There are 88 features in five categories.

[0079] In another implementation, physiological signal feature extraction that is not completely consistent with the present invention may be performed.

[0080] In one implementation, the prediction model is optimized in advance using training data, and the method for generating the training data includes:

[0081] Predetermine the numerical values ​​corresponding to each constant model parameter, and set parameter grid intervals for each variable model parameter to perform traversal and combination, so as to obtain a plurality of model parameter combinations;

[0082] Simulating the electrical network model according to each of the model parameter combinations to obtain simulated blood pressure waveforms corresponding to each of the model parameter combinations;

[0083] Acquire a plurality of real physiological signal sets and real blood pressure waveforms corresponding to the real physiological signal sets;

[0084] Performing parameter matching on each of the real blood pressure waveforms and each of the simulated blood pressure waveforms, and determining the corresponding relationship between each of the real physiological signal sets and each of the model parameter combinations according to the matching result;

[0085] A plurality of the training data are generated according to each of the real physiological signal sets and each of the model parameter combinations, wherein each of the training data includes a pair of the real physiological signal sets and the model parameter combinations having a corresponding relationship, the real physiological signal sets are input into the prediction model as training samples, and the model parameter combinations are used as target values ​​corresponding to the training samples to evaluate the accuracy of the predicted values ​​output by the prediction model.

[0086] Specifically, the prediction model needs to learn the complex mapping relationship between the real physiological signal set and the model parameter combination through a large amount of training data in advance, so that the trained prediction model can accurately predict the corresponding model parameter combination based on the input physiological signal set. In practical application, the values ​​of each constant model parameter are first set, and then the parameter grid intervals are set for each variable model parameter to traverse and combine, so as to obtain multiple model parameter combinations. The electrical network model under each model parameter combination is simulated to generate a simulated blood pressure waveform. The number of simulated blood pressure waveforms generated is consistent with the total number of model parameter combinations. Then a large number of real physiological signal sets (ICG signal + ECG signal) and the real blood pressure waveform corresponding to each real physiological signal set are obtained. For each real physiological signal set, the simulated blood pressure waveform closest to the real physiological signal set is matched by comparing important parameters, such as comparing systolic pressure, diastolic pressure, heart rate and stroke volume. Since there is a one-to-one correspondence between each simulated blood pressure waveform and each model parameter combination, the corresponding relationship between each real physiological signal set and each model parameter combination can be indirectly determined according to the matching result, and then multiple training data are generated according to the corresponding relationship. Each training data consists of a set of real physiological signals (input into the prediction model as training samples) and a corresponding combination of model parameters (used as target values / real labels to evaluate the prediction accuracy of the prediction model).

[0087] For example, a constant model parameter T is pre-set s , R2 and L values ​​can be preset to 0.25, 0.05 and 0.005 respectively. The parameter grid intervals of other model parameters are set to traverse and combine, such as HR is set between 50-120, with a step size of 10; SV is set between 50-100, with a step size of 10; R1 and C are set between 0.5-4, with a step size of 0.01. Simulink simulation is performed on the electrical network model under the above model parameter combination to generate simulated blood pressure waveforms. Some example waveforms are shown in Figure 1. Figure 4As shown. After generating simulated blood pressure waveforms under many different model parameter combinations, the following information contained in these simulated blood pressure waveforms is obtained: systolic pressure, diastolic pressure, heart rate and stroke volume. For real physiological signals, the stroke volume in each cardiac cycle is obtained from the ICG signal, the heart rate is obtained from the ECG signal of the corresponding cardiac cycle, and the systolic and diastolic pressure values ​​are obtained from the blood pressure waveform of the corresponding cardiac cycle. From the numerous simulated blood pressure waveforms, find a simulated blood pressure waveform whose parameters are closest to the real physiological signal, so as to achieve the matching of the real blood pressure waveform and the simulated blood pressure waveform (such as Figure 5 As shown). Through the above method, the real physiological signals (i.e. ECG, continuous blood pressure, ICG signals) can be matched one by one with the model parameter combination of the electrical network model to achieve data matching. The prediction model can learn the mapping from multiple physiological signals to the model parameter combination of the electrical network model through the results of data matching.

[0088] In one implementation, all data are divided at the sample level, with the ratio of training set, validation set, and test set being 8:1:1.

[0089] In one implementation, the prediction model is constructed using a gradient boosted decision tree, and the model optimization method of the prediction model includes:

[0090] For each round of multiple rounds of iterative optimization, a training sample of the round is obtained, and a prediction value is generated according to the training sample through all decision trees of the prediction model;

[0091] Calculate the model residual corresponding to the round according to the predicted value and the corresponding target value;

[0092] Determine whether the model residual converges to the optimization target; if the model residual does not converge to the optimization target, construct a new decision tree according to the model residual, and determine the splitting point of the sub-leaf of the decision tree according to the greedy algorithm;

[0093] Obtain the next round of training samples, and continue to execute the step of generating prediction values ​​according to the training samples through all decision trees of the prediction model until the model residual converges to the optimization target.

[0094] Specifically, the prediction model of this embodiment is constructed using a gradient boosting decision tree, which is an iterative decision tree algorithm. Each round of iteration adds a new decision tree to fit the residual of the previous round, and uses the new and old trees together for prediction. The construction of the decision tree is based on a greedy algorithm, which recursively selects splitting points to maximize the gain. Different sub-leaves in the decision tree correspond to different prediction values, and the splitting point is to split a certain sub-leaves to obtain a more detailed prediction value. Multiple rounds of iterative optimization are performed until the residual of the prediction model meets the standard, which means that the prediction accuracy of the prediction model meets the standard, and the training optimization of the prediction model is completed.

[0095] For example, the prediction model is based on the gradient boosting tree, and the specific steps of building / optimizing the algorithm are as follows:

[0096] First, initialize the model prediction value and set the initialization value to the mean of the target value of the training data set:

[0097]

[0098] In the formula, is the initialization value; n is the total amount of training data; y i is the target value of the i-th training data.

[0099] Then, the prediction model is optimized using multiple iterations, with each iteration adding a new tree to fit the residual of the previous round. In each iteration, the residual needs to be calculated, that is, the difference between the target value (such as the true R1 or C) and the predicted value output by the current prediction model:

[0100]

[0101] In the formula, is the residual of round t.

[0102] A new decision tree is constructed based on the residual so that the output of the tree can best fit the residual. The construction of the decision tree is based on a greedy algorithm, which recursively selects split points to maximize the gain. Different sub-leaves in the decision tree correspond to different prediction values. The split point is to split a sub-leave to obtain a more detailed prediction value. For example, the prediction value corresponding to a sub-leave is 1, which can be split into 0.5 and 1.5.

[0103] The gain formula of the split point is:

[0104]

[0105] Among them, G and H are the gradient and the sum of the second-order derivative of the leaf node, respectively. L and H L is the sum of the gradient and the second-order derivative of the left child node, G R and H Ris the sum of the gradient and the second-order derivative of the right child node, λ is the regularization parameter, and γ is the penalty term for the leaf node.

[0106] Next, use the learning rate to update the model's predictions:

[0107]

[0108] Among them, f t is the newly constructed tree in the tth round; η is the learning rate, which controls the contribution of each tree to the final prediction value.

[0109] The loss function that the prediction model minimizes in each round of iteration is the objective function, which includes the training error Obj(θ) and the regularization term Ω(f):

[0110]

[0111] Where l is the loss function (e.g., squared error); Ω is the regularization term used to control model complexity; T is the number of leaf nodes in the tree; ω j is the weight of the leaf node; γ and λ are hyperparameters used to control the degree of regularization.

[0112] Finally, the parameters are continuously updated in the iteration so that the loss function is continuously reduced, thus achieving the final construction / optimization of the prediction model.

[0113] In another implementation, other machine learning algorithms or deep learning algorithms may be used to predict the model parameter combination of the electrical network model.

[0114] Step S400: Apply the prediction model parameter combination to the electrical network model, simulate and generate a corresponding target simulated blood pressure waveform, and determine the blood pressure monitoring result corresponding to the target user according to the target simulated blood pressure waveform.

[0115] Specifically, the predicted parameters of the electrical network model are applied to the electrical network model, and simulation software is used to perform simulation to obtain a corresponding simulated blood pressure waveform. The user's blood pressure monitoring results, such as systolic pressure and diastolic pressure, can be obtained through the simulated blood pressure waveform, thereby achieving non-invasive continuous blood pressure monitoring.

[0116] For example, in the model testing phase, the parameters of the electrical network model predicted by the test set can be applied to the electrical network model, and Simulink can be used for simulation to generate a simulated blood pressure waveform to obtain the systolic and diastolic pressures.

[0117] The advantages of the present invention are:

[0118] 1. The present invention proposes to fuse multiple physiological signals, which helps to give full play to the complementarity between different signals, and can provide more comprehensive and accurate information related to the cardiovascular system and blood pressure changes, making up for the limitations of the information provided by a single signal in blood pressure monitoring, thereby enhancing the accuracy and reliability of blood pressure monitoring.

[0119] 2. The present invention proposes to use a four-element electrical network model as a model for continuous blood pressure monitoring, which is more physiologically interpretable than the existing black box model methods based on deep learning or machine learning. Specifically, the electrical network model can well model the changes in the cardiovascular system and can intuitively reflect the process of blood pressure changes.

[0120] 3. The present invention extracts signal features from signal segments divided according to the cardiac cycle, so that the electrical network model can update parameters according to the cardiac cycle beats, thereby realizing non-invasive blood pressure monitoring according to the cardiac cycle.

[0121] Based on the above embodiments, the present invention also provides a continuous blood pressure monitoring system based on multiple physiological signals and electrical networks, such as Figure 6 As shown, the system comprises:

[0122] An electrical network model 01, wherein the electrical network model is pre-constructed based on a first resistor, a second resistor, a capacitor and an inductor; the current input corresponding to the electrical network model is determined based on a cardiac cycle time, a cardiac systolic ejection time and a cardiac output; the model parameters of the electrical network model include a variable heart rate, a stroke volume, a first resistance value and a capacitance value, and a constant cardiac systolic ejection time, a second resistance value and an inductor value;

[0123] The signal acquisition module 02 is used to acquire a physiological signal set of a target user; wherein the physiological signal set includes a plurality of physiological signals of different types;

[0124] The signal processing module 03 is used to extract features from the physiological signal set, and predict the model parameters of the electrical network model according to the signal features of the physiological signal set through a preset prediction model to obtain a prediction model parameter combination;

[0125] The data simulation module 04 is used to apply the prediction model parameter combination to the electrical network model, and simulate and generate a corresponding target simulated blood pressure waveform, and determine the blood pressure monitoring result corresponding to the target user according to the target simulated blood pressure waveform.

[0126] Based on the above embodiment, the present invention further provides a terminal, whose principle block diagram can be shown as follows: Figure 7As shown. The terminal includes a processor, a memory, a network interface, and a display screen connected through a system bus. Among them, the processor of the terminal is used to provide computing and control capabilities. The memory of the terminal includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the terminal is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a continuous blood pressure monitoring method based on multiple physiological signals and an electrical network is implemented. The display screen of the terminal can be a liquid crystal display screen or an electronic ink display screen.

[0127] Those skilled in the art will understand that Figure 7 The 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.

[0128] In one implementation, the terminal has one or more programs stored in its memory and is configured to be executed by one or more processors. The one or more programs include instructions for performing a continuous blood pressure monitoring method based on multiple physiological signals and an electrical network.

[0129] Those skilled 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, database or other media used in the embodiments provided by the present invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double 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).

[0130] In summary, the present invention discloses a continuous blood pressure monitoring method based on multiple physiological signals and an electrical network and related equipment. The method obtains an electrical network model pre-constructed based on a first resistor, a second resistor, a capacitor and an inductor; wherein the current input corresponding to the electrical network model is determined based on a cardiac cycle time, a cardiac systolic ejection time, and a cardiac output; the model parameters of the electrical network model include variable heart rate, stroke volume, a first resistance value and a capacitance value, and constant cardiac systolic ejection time, a second resistance value and an inductance value; obtains a physiological signal set of a target user; wherein the physiological signal set includes several physiological signals of different types; extracts features from the physiological signal set, and predicts the model parameters of the electrical network model according to the signal features of the physiological signal set through a preset prediction model to obtain a prediction model parameter combination; applies the prediction model parameter combination to the electrical network model, and simulates and generates a corresponding target simulated blood pressure waveform, and determines the corresponding blood pressure monitoring result of the target user according to the target simulated blood pressure waveform. The present invention extracts signal features related to blood pressure changes from multiple physiological signals, predicts the model parameters of the electrical network model, and realizes non-invasive and continuous blood pressure monitoring according to the relationship between the model parameters and blood pressure changes. Multiple physiological signals can make full use of the advantages and complementary characteristics of different physiological signals to provide more comprehensive information, and the electrical network model can simulate the changes in the cardiovascular system, thereby enhancing the accuracy and reliability of continuous blood pressure monitoring.

[0131] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A continuous blood pressure monitoring method based on multiple physiological signals and electrical networks, characterized in that: The method comprises: Acquire an electrical network model pre-constructed based on a first resistor, a second resistor, a capacitor, and an inductor; wherein the current input corresponding to the electrical network model is determined based on a cardiac cycle time, a cardiac systolic ejection time, and a cardiac output; the model parameters of the electrical network model include a variable heart rate, a stroke volume, a first resistance value, and a capacitance value, and a constant cardiac systolic ejection time, a second resistance value, and an inductor value; Acquire a physiological signal set of a target user; wherein the physiological signal set includes a plurality of physiological signals of different types; Extracting features from the physiological signal set, and predicting model parameters of the electrical network model according to signal features of the physiological signal set using a preset prediction model to obtain a prediction model parameter combination; The prediction model parameter combination is applied to the electrical network model, and a corresponding target simulated blood pressure waveform is simulated to generate a corresponding blood pressure monitoring result of the target user is determined according to the target simulated blood pressure waveform.

2. The continuous blood pressure monitoring method based on multiple physiological signals and electrical network according to claim 1, characterized in that: The first resistor is connected in parallel with the inductor to form a first parallel module; the second resistor is connected in parallel with the capacitor to form a second parallel module; the first parallel module and the second parallel module are connected in series.

3. The continuous blood pressure monitoring method based on multiple physiological signals and electrical network according to claim 1, characterized in that: After obtaining the target user's physiological signal set, the following steps are also included: Each physiological signal in the physiological signal set is preprocessed to remove noise; wherein the preprocessing includes: processing low-frequency interference in each physiological signal through wavelet transform, and processing high-frequency interference in each physiological signal through a filter of a preset frequency.

4. The continuous blood pressure monitoring method based on multiple physiological signals and electrical network according to claim 1, characterized in that: Extracting features from the physiological signal set includes: Dividing each physiological signal in the physiological signal set according to the cardiac cycle to obtain signal segments corresponding to each physiological signal; Each of the signal segments is divided into multiple rounds for feature extraction, wherein each of the signal segments in the same round corresponds to the same cardiac cycle.

5. The continuous blood pressure monitoring method based on multiple physiological signals and electrical network according to claim 1, characterized in that: The physiological signal set includes ECG signals, ICG signals and PPG signals; the prediction model is used for: predicting the heart rate of the electrical network model according to the signal characteristics of the ECG signal; predicting the beat-per-beat output of the electrical network model according to the signal characteristics of the ICG signal; Predicting a first resistance value and a first capacitance value of the electrical network model according to a signal characteristic of the PPG signal.

6. The continuous blood pressure monitoring method based on multiple physiological signals and electrical network according to claim 1, characterized in that: The prediction model is optimized in advance by training data, and the method for generating the training data includes: Predetermine the numerical values ​​corresponding to each constant model parameter, and set parameter grid intervals for each variable model parameter to perform traversal and combination, so as to obtain a plurality of model parameter combinations; Simulating the electrical network model according to each of the model parameter combinations to obtain simulated blood pressure waveforms corresponding to each of the model parameter combinations; Acquire a plurality of real physiological signal sets and real blood pressure waveforms corresponding to the real physiological signal sets; Performing parameter matching on each of the real blood pressure waveforms and each of the simulated blood pressure waveforms, and determining the corresponding relationship between each of the real physiological signal sets and each of the model parameter combinations according to the matching result; A plurality of the training data are generated according to each of the real physiological signal sets and each of the model parameter combinations, wherein each of the training data includes a pair of the real physiological signal sets and the model parameter combinations having a corresponding relationship, the real physiological signal sets are input into the prediction model as training samples, and the model parameter combinations are used as target values ​​corresponding to the training samples to evaluate the accuracy of the predicted values ​​output by the prediction model.

7. The continuous blood pressure monitoring method based on multiple physiological signals and electrical network according to claim 6, characterized in that: The prediction model is constructed using a gradient boosted decision tree, and the model optimization method of the prediction model includes: For each round of multiple rounds of iterative optimization, a training sample of the round is obtained, and a prediction value is generated according to the training sample through all decision trees of the prediction model; Calculate the model residual corresponding to the round according to the predicted value and the corresponding target value; Determine whether the model residual converges to the optimization target; if the model residual does not converge to the optimization target, construct a new decision tree according to the model residual, and determine the splitting point of the sub-leaf of the decision tree according to the greedy algorithm; Obtain the next round of training samples, and continue to execute the step of generating prediction values ​​according to the training samples through all decision trees of the prediction model until the model residual converges to the optimization target.

8. A continuous blood pressure monitoring system based on multiple physiological signals and electrical networks, characterized in that: The system comprises: An electrical network model, wherein the electrical network model is pre-constructed based on a first resistor, a second resistor, a capacitor and an inductor; the current input corresponding to the electrical network model is determined based on a cardiac cycle time, a cardiac systolic ejection time and a cardiac output; the model parameters of the electrical network model include a variable heart rate, a stroke volume, a first resistance value and a capacitance value, and a constant cardiac systolic ejection time, a second resistance value and an inductor value; A signal acquisition module, used to acquire a physiological signal set of a target user; wherein the physiological signal set includes a plurality of physiological signals of different types; A signal processing module, used for extracting features from the physiological signal set, and predicting model parameters of the electrical network model according to the signal features of the physiological signal set through a preset prediction model to obtain a prediction model parameter combination; The data simulation module is used to apply the prediction model parameter combination to the electrical network model, and simulate and generate a corresponding target simulated blood pressure waveform, and determine the blood pressure monitoring result corresponding to the target user according to the target simulated blood pressure waveform.

9. A terminal, characterized in that: The terminal includes a memory and one or more processors; the memory stores one or more programs; the program contains instructions for executing the continuous blood pressure monitoring method based on multiple physiological signals and electrical networks as described in any one of claims 1-7; and the processor is used to execute the program.

10. A computer-readable storage medium having a plurality of instructions stored thereon, characterized in that: The instructions are suitable for being loaded and executed by a processor to implement the steps of the continuous blood pressure monitoring method based on multiple physiological signals and electrical networks as described in any one of claims 1 to 7 above.

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