Method, system and equipment for improving heart rate acquisition accuracy and medium

Abstract

The invention relates to the technical field of intelligent wearable devices, and discloses a method, system and device for improving heart rate acquisition accuracy and a medium. The method comprises the following steps: acquiring an electrocardiogram signal and a photoelectric volume pulse wave signal; the pulse rate is calculated according to the photoelectric volume pulse wave signal, and heart rate variability parameters are obtained through heart rate variability analysis; according to the electrocardiogram signal, the heart rate is obtained through calculation; according to the heart rate variability parameters, heart rate category classification is obtained through a linear discriminant analysis algorithm; according to the heart rate, a heart rate predicted value is obtained through a heart rate model; according to the heart rate predicted value, the heart rate category classification, the photoelectric volume pulse wave signal and the heart rate, a final heart rate output value is obtained through an error back propagation neural network. The method has the following effects: the heart rate acquisition accuracy is improved by considering the heart rate caused by individual difference and the heart rate influence generated by movement or actions with different intensities.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent wearable devices, and in particular to a method, system, device and medium for improving the accuracy of heart rate acquisition. Background Art

[0002] Heart rate can be used as an important indicator to reflect the health status of the human body. In the past, due to technical limitations, heart rate data could only be obtained through professional ECG acquisition equipment. With the advancement of technology, wearable devices such as smart bracelets and smart watches have become mainstream electronic products because of their function of monitoring heart rate and good user experience. Most of the smart watches and bracelets sold on the market today have ECG electrocardiogram sensors and PPG photoelectric sensors, which measure heart rate based on different theoretical foundations, namely ECG heart rate based on ECG sensors and PPG heart rate based on photoelectric volume pulsography. However, the accuracy of these two is largely affected by multiple factors such as individual heart rate, exercise intensity and heart rate variability, which makes the heart rate data collected by the same instrument for different individuals or in different scenarios very different. Therefore, how to fully utilize data and improve the reliability of heart rate data and improve algorithm models is one of the keys.

[0003] At present, relevant research results have been accumulated in terms of the accuracy of heart rate collection. For example, the algorithm based on the joint processing of ECG and PPG, in collaboration with ECG and PPG, can effectively improve the detection results of heart rate, such as improving the noise resistance and heart rate detection range. However, the existing technology does not consider the impact of heart rate differences between individuals and situational factors on the accuracy of heart rate collection, and the influence of the physiological parameter of heart rate variability (Autonomic Nervous System, ANS) is not considered in the heart rate estimation. And it is not effective for the heart rate estimation deviation caused by individual differences, and the impact of heart rate caused by exercise or different intensity actions is not fully considered, resulting in low accuracy of heart rate collection. Summary of the invention

[0004] The present invention provides a method, system, device and medium for improving the accuracy of heart rate collection, so as to improve the accuracy of heart rate collection.

[0005] In a first aspect, in order to solve the above technical problems, the present invention provides a method for improving the accuracy of heart rate collection, comprising:

[0006] Acquire electrocardiogram signals and photoplethysmogram signals;

[0007] Calculating the pulse rate according to the photoplethysmographic signal, and obtaining the heart rate variability parameter through heart rate variability analysis;

[0008] Calculating the heart rate according to the electrocardiogram signal;

[0009] According to the heart rate variability parameters, a heart rate category classification is obtained by using a linear discriminant analysis algorithm;

[0010] According to the heart rate, a predicted heart rate value is obtained through a heart rate model;

[0011] According to the heart rate prediction value, the heart rate category classification, the photoplethysmography signal and the heart rate, a final heart rate output value is obtained through an error back propagation neural network.

[0012] Preferably, the pulse rate is calculated according to the photoplethysmography signal, and the heart rate variability parameter is obtained by heart rate variability analysis, including:

[0013] Performing frequency domain filtering on the photoplethysmogram signal to obtain a filtered photoplethysmogram signal;

[0014] The pulse rate is calculated using the following formula:

[0015]

[0016] Among them, R bpm is the pulse rate, T rp According to the time interval between two adjacent peaks in the filtered photoplethysmogram signal;

[0017] Extracting the heartbeat peak interval of each heartbeat cycle according to the filtered photoplethysmogram signal;

[0018] The heart rate variability parameters include respiratory sinus arrhythmia, the root mean square of the difference between the peak intervals of adjacent heartbeats, and the standard deviation of the normal peak intervals of heartbeats;

[0019] Respiratory sinus arrhythmia was calculated by the following formula:

[0020]

[0021] Among them, RSA is respiratory sinus arrhythmia, F H is the high frequency proportion of the filtered photoplethysmogram signal, F L is the low frequency proportion of the filtered photoplethysmogram signal;

[0022] The root mean square of the difference between the peak intervals of adjacent heartbeats is calculated by the root mean square formula;

[0023] The standard deviation of the normal heartbeat peak interval is calculated using the standard deviation formula.

[0024] Preferably, the heart rate category classification is obtained according to the heart rate variability parameter by a linear discriminant analysis algorithm, including:

[0025] The heart rate categories include: resting heart rate, active heart rate and exercise heart rate;

[0026] When the value of respiratory sinus arrhythmia and the root mean square of the difference between the peak intervals of adjacent heartbeats in the heart rate variability parameter are higher than the preset threshold, and the standard deviation of the normal heartbeat peak interval is less than the preset threshold, it is determined to be an exercise heart rate;

[0027] When the value of respiratory sinus arrhythmia and the root mean square of the difference between adjacent heartbeat peak intervals in the heart rate variability parameter are lower than the preset threshold, and the standard deviation of the normal heartbeat peak interval is greater than the preset threshold, it is determined to be a resting heart rate.

[0028] Preferably, obtaining a predicted heart rate value according to the heart rate through a heart rate model comprises:

[0029] The heart rate model formula is:

[0030] PPG=c1·HR+c2·HR 2 -c3 HR 3 +c4 HR 4 -c5 HR 5

[0031] Where HR is the heart rate, PPG is the predicted heart rate, c1, c2, c3, c4 and c5 are set coefficients;

[0032] The heart rate model is obtained through historical big data training, and the heart rate model can obtain a predicted heart rate value based on an input heart rate.

[0033] Preferably, obtaining a final heart rate output value through an error back propagation neural network according to the heart rate prediction value, the heart rate category classification, the photoplethysmography signal and the heart rate includes:

[0034] Using the heart rate prediction value, the heart rate category classification, the photoplethysmography signal and the heart rate as neurons of the input layer of the error back propagation neural network;

[0035] Obtaining a neural network predicted heart rate through the error back propagation neural network;

[0036] When the heart rate category is classified as active heart rate, the heart rate predicted by the neural network is used as the final heart rate output value.

[0037] Preferably, calculating the heart rate according to the electrocardiogram signal includes:

[0038] Performing frequency domain filtering on the electrocardiogram signal to obtain a filtered electrocardiogram signal;

[0039] The heart rate is calculated using the following formula:

[0040]

[0041] Among them, H r is the heart rate, T r According to the time interval between two adjacent peaks in the filtered electrocardiogram signal.

[0042] Preferably, the method further comprises:

[0043] When the heart rate category is classified as a resting heart rate, the heart rate prediction value is used as the final heart rate output value.

[0044] In a second aspect, the present invention provides a system for improving the accuracy of heart rate collection, comprising:

[0045] A data acquisition module, used for acquiring electrocardiogram signals and photoelectric volume pulse wave signals;

[0046] A heart rate parameter module, used to calculate the pulse rate according to the photoplethysmography signal, and obtain the heart rate variability parameter through heart rate variability analysis;

[0047] A heart rate calculation module, used to calculate the heart rate according to the electrocardiogram signal;

[0048] A heart rate classification module, used to obtain a heart rate category classification according to the heart rate variability parameter by a linear discriminant analysis algorithm;

[0049] A heart rate prediction module, used to obtain a heart rate prediction value according to the heart rate through a heart rate model;

[0050] The heart rate output module is used to obtain a final heart rate output value through an error back propagation neural network according to the heart rate prediction value, the heart rate category classification, the photoplethysmogram signal and the heart rate.

[0051] In a third aspect, the present invention further provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements any one of the above-mentioned methods for improving the accuracy of heart rate collection when executing the computer program.

[0052] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned methods for improving the accuracy of heart rate collection.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] The present invention discloses a method for improving the accuracy of heart rate acquisition, the method comprising acquiring an electrocardiogram signal and a photoplethysmogram signal; calculating the pulse rate according to the photoplethysmogram signal, and obtaining a heart rate variability parameter through heart rate variability analysis; calculating the heart rate according to the electrocardiogram signal; obtaining a heart rate category classification according to the heart rate variability parameter through a linear discriminant analysis algorithm; obtaining a heart rate prediction value according to the heart rate through a heart rate model; obtaining a final heart rate output value through an error back propagation neural network according to the heart rate prediction value, the heart rate category classification, the photoplethysmogram signal and the heart rate. The two different signal acquisition methods of ECG and PPG are combined to make full use of their respective advantages. ECG provides high-precision electrocardiogram information, while PPG is suitable for continuous monitoring. This multimodal fusion can more comprehensively reflect the user's heart rate status. At the same time, through a personalized model, it can adapt to the physiological characteristics of different users and reduce the heart rate estimation deviation caused by individual differences. Furthermore, the LDA algorithm is used to classify the heart rate variability parameters and distinguish the three states of quiet, active and exercising, so that different heart rate calculation methods can be used in different situations to improve the accuracy of the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flowchart of a method for improving the accuracy of heart rate collection provided by the first embodiment of the present invention;

[0056] Figure 2 It is a schematic diagram of the structure of a system for improving the accuracy of heart rate collection provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] Heart rate can be used as an important indicator to reflect the health status of human body. In the past, due to technical limitations, it could only be obtained through professional ECG acquisition equipment.

[0059] Heart rate data. With the advancement of technology, wearable devices such as smart bracelets and smart watches have become mainstream electronic products because of their heart rate monitoring functions and good user experience. Most of the smart watches and bracelets sold on the market today have ECG electrocardiogram sensors and PPG photoelectric sensors, which measure heart rate based on different theoretical foundations, namely ECG heart rate based on electrocardiogram sensors and PPG heart rate based on photoelectric volume pulsation. However, the accuracy of these two is largely affected by multiple factors such as individual heart rate, exercise intensity and heart rate variability, which makes the heart rate data collected by the same instrument for different individuals or in different scenarios vary greatly. Therefore, how to fully utilize the data and improve the reliability of heart rate data and improve the algorithm model is one of the keys.

[0060] At present, relevant research results have been accumulated in terms of the accuracy of heart rate collection. For example, the algorithm based on the joint processing of ECG and PPG, in collaboration with ECG and PPG, can effectively improve the detection results of heart rate, such as improving the noise resistance and heart rate detection range. However, the existing technology does not consider the impact of heart rate differences between individuals and situational factors on the accuracy of heart rate collection, and the influence of the physiological parameter of heart rate variability (Autonomic Nervous System, ANS) is not considered in the heart rate estimation. And it is not effective for the heart rate estimation deviation caused by individual differences, and the impact of heart rate caused by exercise or different intensity actions is not fully considered, resulting in low accuracy of heart rate collection.

[0061] Reference Figure 1 The first embodiment of the present invention provides a method for improving the accuracy of heart rate collection, comprising the following steps:

[0062] S11, acquiring electrocardiogram signal and photoplethysmogram signal;

[0063] S12, calculating the pulse rate according to the photoplethysmography signal, and obtaining a heart rate variability parameter through heart rate variability analysis;

[0064] S13, calculating the heart rate according to the electrocardiogram signal;

[0065] S14, obtaining a heart rate category classification by a linear discriminant analysis algorithm according to the heart rate variability parameter;

[0066] S15, obtaining a predicted heart rate value according to the heart rate through a heart rate model;

[0067] S16, obtaining a final heart rate output value through an error back propagation neural network according to the heart rate prediction value, the heart rate category classification, the photoplethysmography signal and the heart rate.

[0068] In step S11, an electrocardiogram signal and a photoplethysmogram signal are acquired.

[0069] In one embodiment, an electrocardiogram signal is obtained using a smart wearable device such as a smart bracelet, a smart watch, or a professional electrocardiograph. One or more electrocardiogram electrodes are placed on the user's chest, usually near the sternum or on the left chest; a reference electrode is attached to a position away from the heart, such as an arm or a leg; a ground electrode is placed on the abdomen or other appropriate position to provide circuit grounding. The electrical signals of the heart are collected through the electrodes and digitally sampled. The sampling frequency is 250Hz to ensure that sufficiently detailed electrocardiogram information is captured. Further, a smart wearable device such as a smart bracelet or a smart watch is used to obtain a photoelectric volumetric pulse wave signal, and its sensor is a light-emitting diode sensor or a photodiode. The smart wearable device is worn on the user's arm, usually on the wrist, to ensure that the device is worn firmly to avoid the absorption of light by the back of the hand or the bracelet, and the light intensity emitted by the light-emitting diode is about 60mW. The light-emitting diode emits a light signal to a fixed position under the skin. Part of the light signal is absorbed by the blood vessels in the skin, resulting in a reduction in light flux. The photodiode receives the light signal that passes through the skin and converts it into an electrical signal. The signal received by the photodiode is digitally sampled, with a common sampling frequency of 100 Hz.

[0070] In step S12, the pulse rate is calculated based on the photoplethysmography signal, and the heart rate variability parameter is obtained through heart rate variability analysis.

[0071] In one implementation, the photoplethysmography signal is subjected to frequency domain filtering to obtain a filtered photoplethysmography signal;

[0072] The pulse rate is calculated using the following formula:

[0073]

[0074] Among them, R bpm is the pulse rate, T rp is a time interval between two adjacent peaks in the filtered photoplethysmogram signal;

[0075] Extracting the heartbeat peak interval of each heartbeat cycle according to the filtered photoplethysmogram signal;

[0076] The heart rate variability parameters include respiratory sinus arrhythmia, the root mean square of the difference between the peak intervals of adjacent heartbeats, and the standard deviation of the normal peak intervals of heartbeats;

[0077] Respiratory sinus arrhythmia was calculated by the following formula:

[0078]

[0079] Among them, RSA is respiratory sinus arrhythmia, F H is the high frequency proportion of the filtered photoplethysmogram signal, F L is the low frequency proportion of the filtered photoplethysmogram signal;

[0080] The root mean square of the difference between the peak intervals of adjacent heartbeats is calculated by the root mean square formula;

[0081] The standard deviation of the normal heartbeat peak interval is calculated using the standard deviation formula.

[0082] It is worth noting that the frequency domain filtering process adopts the fast Fourier transform method to remove noise such as baseline drift, electromyographic interference, motion artifacts and power frequency interference. Respiratory sinus arrhythmia reflects the impact of breathing on heart rate. The filtered photoelectric volume pulse wave signal is divided according to the standard frequency band to determine the high-frequency and low-frequency parts, where the high-frequency part corresponds to the respiratory frequency and the low-frequency part corresponds to the sympathetic and parasympathetic nerve activities. The standard deviation of the normal heartbeat peak interval reflects the overall degree of heart rate fluctuation. The root mean square of the difference between adjacent heartbeat peak intervals reflects the short-term heart rate fluctuation characteristics. These parameters can comprehensively reflect the user's heart rate condition and the regulatory ability of the autonomic nervous system, thereby improving the accuracy and reliability of heart rate monitoring.

[0083] In step S13, the heart rate is calculated based on the electrocardiogram signal.

[0084] In one embodiment, the electrocardiogram signal is subjected to frequency domain filtering to obtain a filtered electrocardiogram signal;

[0085] The heart rate is calculated using the following formula:

[0086]

[0087] Among them, H r is the heart rate, T r According to the time interval between two adjacent peaks in the filtered electrocardiogram signal.

[0088] It is worth noting that the ECG signal is filtered in the range of 0.5Hz-40Hz using a bandpass filter to remove baseline drift, myoelectric interference, motion artifacts, power frequency interference and other noises. At the same time, the peak value of the ECG signal can be determined using the threshold method, derivative method, adaptive threshold method or template matching method.

[0089] In step S14, a heart rate category classification is obtained according to the heart rate variability parameters through a linear discriminant analysis algorithm.

[0090] In one embodiment, the heart rate category classification includes: resting heart rate, active heart rate and exercise heart rate;

[0091] When the value of respiratory sinus arrhythmia in the heart rate variability parameter and the root mean square of the difference between adjacent heartbeat peaks are higher than the preset threshold, and the standard deviation of the normal heartbeat peak interval is less than the preset threshold, it is judged as exercise heart rate; when the value of respiratory sinus arrhythmia in the heart rate variability parameter and the root mean square of the difference between adjacent heartbeat peaks are lower than the preset threshold, and the standard deviation of the normal heartbeat peak interval is greater than the preset threshold, it is judged as resting heart rate.

[0092] It is worth noting that respiratory sinus arrhythmia reflects the effect of breathing on heart rate. Usually, during breathing, heart rate changes with breathing. High respiratory sinus arrhythmia values ​​usually indicate that the heart is strongly affected by breathing, which is common in exercise; low respiratory sinus arrhythmia values ​​indicate that the heart is less affected by breathing, which is common in a quiet state. The root mean square of the difference between adjacent heartbeat peaks reflects the short-term heart rate fluctuation characteristics. High root mean square values ​​of the difference between adjacent heartbeat peaks usually indicate large heart rate fluctuations, which are common in exercise; low root mean square values ​​of the difference between adjacent heartbeat peaks indicate small heart rate fluctuations, which are common in a quiet state. The standard deviation of normal heartbeat peak intervals reflects the overall degree of heart rate fluctuation. High standard deviation values ​​of normal heartbeat peak intervals usually indicate large heart rate fluctuations, which are common in a quiet state; low standard deviation values ​​of normal heartbeat peak intervals indicate small heart rate fluctuations, which are common in exercise.

[0093] In one embodiment, the respiratory sinus rhythm, the root mean square of the difference between the intervals of adjacent heartbeat peaks, and the standard deviation of the normal heartbeat peak intervals are calculated respectively, and by comparing with the preset thresholds, these thresholds can be determined according to experimental data. Since different individuals have different physiques, the setting of the threshold is related to the group, age, and race. As the product usage time increases and the amount of data actually used by the user increases, the threshold can be adjusted in stages to achieve the high accuracy target of the threshold setting. When the value of the respiratory sinus arrhythmia in the heart rate variability parameter and the root mean square of the difference between the intervals of adjacent heartbeat peaks are higher than the preset threshold, and the standard deviation of the normal heartbeat peak interval is less than the preset threshold, it is determined to be the exercise heart rate; when the value of the respiratory sinus arrhythmia in the heart rate variability parameter and the root mean square of the difference between the intervals of adjacent heartbeat peaks are lower than the preset threshold, and the standard deviation of the normal heartbeat peak interval is greater than the preset threshold, it is determined to be the resting heart rate. When the above two conditions are not met, it is determined to be the active heart rate.

[0094] In step S15, a predicted heart rate value is obtained according to the heart rate through a heart rate model.

[0095] In one implementation, the heart rate model formula is:

[0096] PPG=c1·HR+c2·HR2 -c3 HR 3 +c4 HR 4 -c5 HR 5

[0097] Where HR is the heart rate, PPG is the predicted heart rate, c1, c2, c3, c4 and c5 are set coefficients;

[0098] Among them, the heart rate model is obtained through historical big data training, and the heart rate model can obtain a heart rate prediction value based on the input heart rate. The model is built based on the Kubicek model, using the electrocardiogram signal to calculate the mean heart rate, calculating the average value of HR as input, and calculating the heart rate variance as an influencing factor of the heart rate prediction value. The mean heart rate reflects the average level of the user's heart rate over a period of time. The heart rate variance reflects the degree of heart rate fluctuation. A larger variance indicates a larger change in heart rate, which is affected by factors such as activity status and emotions. The mean heart rate and heart rate variance are used as input features to train the heart rate prediction model. A machine learning algorithm is used to build a prediction model.

[0099] In one embodiment, c1=1.41, c2=0.013, c3=0.007, c4=0.0001 and c5=0.000001. The parameters are obtained by collecting a large amount of experimental data, including the actual measured heart rate and the corresponding photoplethysmographic signal. These data cover different heart rate ranges and different activity states including quiet, active, and exercising. The robustness of the model is improved by collecting more data, and further, regularization techniques are used to prevent overfitting, and finally cross-validation is used to select the best model parameters.

[0100] In step S16, a final heart rate output value is obtained through an error back propagation neural network according to the heart rate prediction value, the heart rate category classification, the photoplethysmography signal and the heart rate.

[0101] In one embodiment, the heart rate prediction value, the heart rate category classification, the photoplethysmogram signal and the heart rate are used as neurons of the input layer of the error back propagation neural network; the neural network predicted heart rate is obtained through the error back propagation neural network; when the heart rate category is classified as active heart rate, the neural network predicted heart rate is used as the final heart rate output value.

[0102] In one embodiment, a BP neural network model is constructed, including an input layer, a hidden layer and an output layer, wherein the number of neurons in the input layer is 3, the number of neurons in the output layer is 1, and Sigmoid is selected as the activation function; the data is divided into a training set and a test set in a ratio of 3:1, and the BP neural network is trained; the trained BP neural network model is used to output the final heart rate prediction value, and in this step, the heart rate calculation value is taken as the output value only when the heart rate data is a quiet value.

[0103] In one embodiment, when the heart rate class

[0104] When the heart rate category is classified as a resting heart rate, the heart rate prediction value is used as the final heart rate output value; when the heart rate category is classified as an active heart rate, the heart rate prediction value of the neural network is used as the final heart rate output value. In a resting state, the heart rate is relatively stable, and the heart rate variability parameters can better reflect the state of the heart. Therefore, the heart rate prediction value calculated based on these parameters can provide higher accuracy. Directly using the formula to calculate the heart rate prediction value avoids the complex machine learning model reasoning process and improves the real-time performance and efficiency of the system. In an active state, the heart rate changes greatly and is affected by many factors. The neural network model can capture these complex change patterns and provide more accurate heart rate prediction. The neural network model has been trained with a large amount of data and has strong learning and generalization capabilities, and can provide high-precision heart rate prediction under different activity states. In an active state, the user's heart rate changes greatly and is affected by many factors, such as exercise intensity, emotional fluctuations, etc. In this case, relying solely on the heart rate variability parameters cannot accurately reflect the current heart rate state. Therefore, introducing a neural network model for heart rate prediction can better capture the dynamic changes of heart rate. Furthermore, under the condition of exercise heart rate, when the final heart rate output value is also selected, the predicted heart rate of the neural network model is used as the final output. In the state of exercise, the heart rate changes greatly and frequently, and is affected by many factors, such as exercise intensity, breathing rate, body position changes, etc., with high variability. At the same time, the relationship between heart rate and these factors is often nonlinear, and traditional linear models are difficult to accurately capture this complex relationship. Heart rate monitoring in exercise requires high real-time performance to provide timely feedback on the user's heart condition. Neural networks (especially deep learning models) can process highly nonlinear data and learn complex patterns from them. They can also consider multiple input features (such as ECG signals, PPG signals, accelerometer data, etc.) at the same time, and automatically extract the most relevant feature combination, thereby improving prediction accuracy.

[0105] In summary, the present invention aims to solve the problem of low accuracy of heart rate collection by integrating electrocardiogram (ECG) and photoplethysmography (PPG) technology, introducing machine learning models, identifying activity states of measured values, and realizing heart rate prediction and acquisition in different situations, so as to make the heart rate value more accurate. The system includes three main modules: ECG and PPG signal acquisition module, data processing module, and machine learning model training and implementation module. When faced with situations where the heart rate changes greatly, such as exercise, emergencies, or emotional fluctuations, the heart rate calculated by traditional physiological models often has large errors. In order to solve this problem, this method innovatively introduces machine learning

[0106] It combines physical models with machine learning algorithms to accurately identify activity states, thereby effectively improving the accuracy of heart rate data.

[0107] Specifically, the present invention enhances the comprehensiveness and real-time performance of heart rate data collection by synchronously collecting ECG and PPG signals on smart wearable devices. These two signals reflect the changes in cardiac electrical signals and blood volume, respectively, and their combination can provide richer physiological information. In addition, the introduction of machine learning models, especially BP neural network models, can not only perform in-depth processing and analysis of data, but also improve the accuracy of heart rate calculation. As a powerful nonlinear model, BP neural network can find hidden patterns in complex data, thereby better predicting heart rate.

[0108] In order to further improve the contextual interpretation ability of heart rate data, the present invention also uses the linear discriminant analysis (LDA) algorithm to classify the heart rate variability parameters. The LDA algorithm can classify the user's state into three categories: quiet, active, and exercising according to the different characteristics of the heart rate variability parameters. This classification method helps to understand the changing patterns of the user's heart rate under different activity states, and then provide more personalized health advice.

[0109] By integrating ECG and PPG technologies, the present invention realizes the comprehensiveness and real-time collection of heart rate data; by introducing machine learning models, especially BP neural network models, the accuracy of heart rate calculation is improved; by classifying heart rate variability parameters through LDA algorithm, the contextual interpretation of heart rate data is realized. The combination of these technologies not only solves the inaccuracy problem existing in traditional heart rate monitoring, but also provides users with a more reliable and intelligent heart rate monitoring solution. In addition, the present invention also pays special attention to the parameter analysis of heart rate variability. Through the comprehensive analysis of parameters such as RR interval, respiratory sinus arrhythmia (RSA), root mean square difference of adjacent heartbeat peak interval (RMSSD) and standard deviation of normal heartbeat peak interval (SDNN), the user's heart health status can be more comprehensively evaluated. These parameters not only reflect the immediate state of the heart, but also reveal the regulatory effect of the autonomic nervous system on the heart, thereby providing an important reference basis for medical diagnosis and health management.

[0110] In short, the present invention significantly improves the accuracy and reliability of heart rate monitoring through multimodal signal fusion, application of machine learning models and contextualized data analysis. This method is not only suitable for daily health monitoring, but also plays an important role in clinical environments, providing more refined management and treatment plans for heart patients. In the future, with the continuous advancement of technology and the expansion of application scenarios, this heart rate monitoring method based on the fusion of ECG and PPG is expected to become one of the standards in the field of heart rate monitoring, safeguarding people's health.

[0111] Reference Figure 2 The second embodiment of the present invention provides a system for improving the accuracy of heart rate collection, including:

[0112] A data acquisition module, used for acquiring electrocardiogram signals and photoelectric volume pulse wave signals;

[0113] A heart rate parameter module, used to calculate the pulse rate according to the photoplethysmography signal, and obtain the heart rate variability parameter through heart rate variability analysis;

[0114] A heart rate calculation module, used to calculate the heart rate according to the electrocardiogram signal;

[0115] A heart rate classification module, used to obtain a heart rate category classification according to the heart rate variability parameter by a linear discriminant analysis algorithm;

[0116] A heart rate prediction module, used to obtain a heart rate prediction value according to the heart rate through a heart rate model;

[0117] The heart rate output module is used to obtain a final heart rate output value through an error back propagation neural network according to the heart rate prediction value, the heart rate category classification, the photoplethysmogram signal and the heart rate.

[0118] Preferably, the data acquisition module is used to:

[0119] Acquire electrocardiogram (ECG) and photoplethysmogram (PPE) signals.

[0120] In one embodiment, in one embodiment, an electrocardiogram (ECG) signal is obtained using a smart wearable device such as a smart bracelet, a smart watch, or a professional electrocardiograph. The specific operation is as follows: one or more ECG electrodes are placed on the user's chest, usually near the sternum or left chest, to capture the heart's electrical signals; at the same time, a reference electrode is attached to a position away from the heart, such as an arm or leg, to provide a stable reference point; the ground electrode is placed on the abdomen or other appropriate position to provide circuit grounding. Through these electrodes, the heart's electrical signals can be collected and digitally sampled. In order to ensure that sufficiently detailed ECG information is captured, the sampling frequency is set to 250Hz.

[0121] In one embodiment, a smart wearable device such as a smart bracelet or a smart watch is used to obtain a photoplethysmogram (PPG) signal. These devices are usually equipped with a light-emitting diode sensor and a photodiode. The user needs to wear the smart wearable device on the arm, usually the wrist, to ensure that the device is stable and the measurement results are not affected by the absorption of light by the back of the hand or the bracelet. The light-emitting diode emits a light signal with an intensity of about 60mW, which is irradiated to a fixed position under the skin. Part of the light signal will be absorbed by the blood vessels in the skin, resulting in a reduction in light flux. The photodiode receives the light signal that passes through the skin and converts it into an electrical signal. Subsequently, the signal received by the photodiode is digitally sampled, and the common sampling frequency is 100Hz. This dual-modal signal acquisition method combines the advantages of ECG and PPG, and can provide more comprehensive and accurate heart rate monitoring data. The ECG signal provides detailed information about the heart's electrical signals, can accurately detect the R wave, and thus calculate the heart rate. The PPG signal reflects the changes in blood volume, can continuously monitor heart rate changes over a long period of time, and is suitable for non-invasive daily wear. By fusing these two signals, we can not only improve the accuracy of heart rate monitoring, but also better understand the performance of the heart in different activity states. In practical applications, these signals can be further used for heart rate variability analysis, activity state recognition, and heart rate prediction after preprocessing (such as filtering and smoothing). By calculating and analyzing heart rate variability parameters (such as RSA, RMSSD, and SDNN), we can have a deeper understanding of the user's heart health status.

[0122] It is worth noting that the combination of machine learning models, such as neural networks, can provide more accurate heart rate predictions in complex situations, thereby achieving personalized health management. In short, this implementation not only improves the accuracy and reliability of heart rate monitoring, but also enhances the practicality and user experience of the system, and is suitable for various application scenarios from daily health monitoring to clinical diagnosis.

[0123] Preferably, the heart rate parameter module is used to:

[0124] The pulse rate is calculated according to the photoplethysmographic signal, and the heart rate variability parameters are obtained through heart rate variability analysis, including:

[0125] Performing frequency domain filtering on the photoplethysmogram signal to obtain a filtered photoplethysmogram signal;

[0126] The pulse rate is calculated using the following formula:

[0127]

[0128] Among them, R bpm is the pulse rate, T rp According to the time interval between two adjacent peaks in the filtered photoplethysmogram signal;

[0129] Extracting the heartbeat peak interval of each heartbeat cycle according to the filtered photoplethysmogram signal;

[0130] The heart rate variability parameters include respiratory sinus arrhythmia, the root mean square of the difference between the peak intervals of adjacent heartbeats, and the standard deviation of the normal peak intervals of heartbeats;

[0131] Respiratory sinus arrhythmia was calculated by the following formula:

[0132]

[0133] Among them, RSA is respiratory sinus arrhythmia, F H is the high frequency proportion of the filtered photoplethysmogram signal, F L is the low frequency proportion of the filtered photoplethysmogram signal;

[0134] The root mean square of the difference between the peak intervals of adjacent heartbeats is calculated by the root mean square formula;

[0135] The standard deviation of the normal heartbeat peak interval is calculated using the standard deviation formula.

[0136] It is worth noting that respiratory sinus arrhythmia (RSA) can reflect the impact of breathing on heart rate. During breathing, heart rate often changes with the rhythm of breathing. Specifically, a higher RSA value usually means that the heart is strongly affected by breathing, which is common in exercise; on the contrary, a lower RSA value indicates that the heart is less affected by breathing, which is usually a characteristic of a quiet state. The root mean square difference (RMSSD) of the intervals between adjacent heartbeat peaks is an important indicator for measuring the characteristics of short-term heart rate fluctuations. A high RMSSD value usually indicates a large heart rate fluctuation, which is particularly obvious in exercise; while a low RMSSD value indicates a small heart rate fluctuation, which is common in a quiet state. The standard deviation of the normal heartbeat peak interval (SDNN) reflects the overall degree of heart rate fluctuation. A high SDNN value usually indicates a large heart rate fluctuation, which is more common in a quiet state; while a low SDNN value indicates a small heart rate fluctuation, which is common in an exercise state. Through the comprehensive analysis of these parameters, we can have a more comprehensive understanding of the characteristics of heart rate changes under different activity states, thereby providing an important reference for health monitoring and medical diagnosis. Whether it is a steady heart rate at rest or drastic fluctuations during exercise, these parameters can reveal the dynamic performance of the heart in different situations.

[0137] Preferably, the heart rate calculation module is used to:

[0138] Calculating the heart rate according to the electrocardiogram signal includes:

[0139] Performing frequency domain filtering on the electrocardiogram signal to obtain a filtered electrocardiogram signal;

[0140] The heart rate is calculated using the following formula:

[0141]

[0142] Among them, H r is the heart rate, T r According to the time interval between two adjacent peaks in the filtered electrocardiogram signal.

[0143] Preferably, the heart rate classification module is used to:

[0144] According to the heart rate variability parameters, a heart rate category classification is obtained through a linear discriminant analysis algorithm, including:

[0145] The heart rate categories include: resting heart rate, active heart rate and exercise heart rate;

[0146] When the value of respiratory sinus arrhythmia and the root mean square of the difference between the peak intervals of adjacent heartbeats in the heart rate variability parameter are higher than the preset threshold, and the standard deviation of the normal heartbeat peak interval is less than the preset threshold, it is determined to be an exercise heart rate;

[0147] When the value of respiratory sinus arrhythmia and the root mean square of the difference between adjacent heartbeat peak intervals in the heart rate variability parameter are lower than the preset threshold, and the standard deviation of the normal heartbeat peak interval is greater than the preset threshold, it is determined to be a resting heart rate.

[0148] Preferably, the heart rate prediction module is used to:

[0149] According to the heart rate, a heart rate prediction value is obtained through a heart rate model, including:

[0150] The heart rate model formula is:

[0151] PPG=c1·HR+c2·HR 2 -c3 HR 3 +c4 HR 4 -c5 HR 5

[0152] Where HR is the heart rate, PPG is the predicted heart rate, c1, c2, c3, c4 and c5 are set coefficients;

[0153] The heart rate model is obtained through historical big data training, and the heart rate model can obtain a predicted heart rate value based on an input heart rate.

[0154] Preferably, the heart rate output module is used to:

[0155] According to the heart rate prediction value, the heart rate category classification, the photoplethysmogram signal and the heart rate, a final heart rate output value is obtained through an error back propagation neural network, including:

[0156] Using the heart rate prediction value, the heart rate category classification, the photoplethysmography signal and the heart rate as neurons of the input layer of the error back propagation neural network;

[0157] Obtaining a neural network predicted heart rate through the error back propagation neural network;

[0158] When the heart rate category is classified as active heart rate, the heart rate predicted by the neural network is used as the final heart rate output value.

[0159] Preferably, when the heart rate category is classified as a resting heart rate, the heart rate prediction value is used as the final heart rate output value; when the heart rate category is classified as an active heart rate, the neural network predicted heart rate is used as the final heart rate output value.

[0160] It is worth noting that as an important health indicator, accurate measurement of heart rate is crucial for health management. Although existing smart wearable devices have made significant progress in heart rate monitoring, there are still challenges in the face of individual differences, situational factors and heart rate variability. By introducing personalized calibration, considering situational factors, heart rate variability analysis, improving algorithm models, and data enhancement and preprocessing, the reliability and accuracy of heart rate data can be significantly improved. The development of these technologies not only helps to improve the level of personal health management, but also provides strong support for clinical diagnosis and medical research.

[0161] It should be noted that a system for improving the accuracy of heart rate collection provided in an embodiment of the present invention is used to execute all the process steps of a method for improving the accuracy of heart rate collection in the above embodiment. The working principles and beneficial effects of the two correspond one to one, and thus will not be described in detail.

[0162] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a heart rate prediction program. When the processor executes the computer program, the steps of the above-mentioned method for improving the accuracy of heart rate acquisition are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the data acquisition module.

[0163] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0164] The electronic device may be a desktop computer

[0165] Computers such as computers, notebooks, PDAs, and smart tablets. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, and the like.

[0166] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0167] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0168] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0169] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0170] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for improving the accuracy of heart rate collection, characterized in that: include: Acquire electrocardiogram signals and photoplethysmogram signals; Calculating the pulse rate according to the photoplethysmographic signal, and obtaining the heart rate variability parameter through heart rate variability analysis; Calculating the heart rate according to the electrocardiogram signal; According to the heart rate variability parameters, a heart rate category classification is obtained by using a linear discriminant analysis algorithm; According to the heart rate, a predicted heart rate value is obtained through a heart rate model; According to the heart rate prediction value, the heart rate category classification, the photoplethysmography signal and the heart rate, a final heart rate output value is obtained through an error back propagation neural network.

2. The method for improving the accuracy of heart rate acquisition according to claim 1, characterized in that: The pulse rate is calculated according to the photoplethysmogram signal, and the heart rate variability parameter is obtained by heart rate variability analysis, including: Performing frequency domain filtering on the photoplethysmogram signal to obtain a filtered photoplethysmogram signal; The pulse rate is calculated using the following formula: Among them, R bpm is the pulse rate, T rp According to the time interval between two adjacent peaks in the filtered photoplethysmogram signal; Extracting the heartbeat peak interval of each heartbeat cycle according to the filtered photoplethysmogram signal; The heart rate variability parameters include respiratory sinus arrhythmia, the root mean square of the difference between the peak intervals of adjacent heartbeats, and the standard deviation of the normal peak intervals of heartbeats; Respiratory sinus arrhythmia was calculated by the following formula: Among them, RSA is respiratory sinus arrhythmia, F H is the high frequency proportion of the filtered photoplethysmogram signal, F L is the low frequency proportion of the filtered photoplethysmogram signal; The root mean square of the difference between the peak intervals of adjacent heartbeats is calculated by the root mean square formula; The standard deviation of the normal heartbeat peak interval is calculated using the standard deviation formula.

3. The method for improving the accuracy of heart rate acquisition according to claim 1, characterized in that: The step of obtaining the heart rate category classification based on the heart rate variability parameter by using a linear discriminant analysis algorithm includes: The heart rate categories include: resting heart rate, active heart rate and exercise heart rate; When the value of respiratory sinus arrhythmia and the root mean square of the difference between the peak intervals of adjacent heartbeats in the heart rate variability parameter are higher than the preset threshold, and the standard deviation of the normal heartbeat peak interval is less than the preset threshold, it is determined to be an exercise heart rate; When the value of respiratory sinus arrhythmia and the root mean square of the difference between adjacent heartbeat peak intervals in the heart rate variability parameter are lower than the preset threshold, and the standard deviation of the normal heartbeat peak interval is greater than the preset threshold, it is determined to be a resting heart rate.

4. The method for improving the accuracy of heart rate acquisition according to claim 1, characterized in that: The step of obtaining a predicted heart rate value based on the heart rate through a heart rate model includes: The heart rate model formula is: PPG=c1·HR+c2·HR 2 -c3·HR 3 +c4·HR 4 -c5·HR 5 Wherein, HR is the heart rate, PPG is the predicted heart rate, c1, c2, c3, c4 and c5 are setting coefficients; The heart rate model is obtained through historical big data training, and the heart rate model can obtain a predicted heart rate value based on an input heart rate.

5. The method for improving the accuracy of heart rate acquisition according to claim 1, characterized in that: The method of obtaining a final heart rate output value according to the heart rate prediction value, the heart rate category classification, the photoplethysmogram signal and the heart rate through an error back propagation neural network includes: Using the heart rate prediction value, the heart rate category classification, the photoplethysmography signal and the heart rate as neurons of the input layer of the error back propagation neural network; Obtaining a neural network predicted heart rate through the error back propagation neural network; When the heart rate category is classified as active heart rate, the heart rate predicted by the neural network is used as the final heart rate output value.

6. The method for improving the accuracy of heart rate acquisition according to claim 1, characterized in that: The step of calculating the heart rate according to the electrocardiogram signal comprises: Performing frequency domain filtering on the electrocardiogram signal to obtain a filtered electrocardiogram signal; The heart rate is calculated using the following formula: Among them, H r is the heart rate, T r According to the time interval between two adjacent peaks in the filtered electrocardiogram signal.

7. The method for improving the accuracy of heart rate acquisition according to claim 5, characterized in that: The method further comprises: When the heart rate category is classified as a resting heart rate, the heart rate prediction value is used as the final heart rate output value.

8. A system for improving the accuracy of heart rate acquisition, characterized in that: include: A data acquisition module, used for acquiring electrocardiogram signals and photoelectric volume pulse wave signals; A heart rate parameter module, used to calculate the pulse rate according to the photoplethysmography signal, and obtain the heart rate variability parameter through heart rate variability analysis; A heart rate calculation module, used to calculate the heart rate according to the electrocardiogram signal; A heart rate classification module, used to obtain a heart rate category classification according to the heart rate variability parameter by a linear discriminant analysis algorithm; A heart rate prediction module, used to obtain a heart rate prediction value according to the heart rate through a heart rate model; The heart rate output module is used to obtain a final heart rate output value through an error back propagation neural network according to the heart rate prediction value, the heart rate category classification, the photoplethysmogram signal and the heart rate.

9. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for improving the accuracy of heart rate collection as claimed in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for improving the accuracy of heart rate collection as described in any one of claims 1 to 7.

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