182 results about "Heart beat" patented technology

The invention relates to the technical field of medical monitoring, in particular to an intelligent old people health monitoring system, which comprises a heart rate module for collecting heart rate signals, identifying local peak values in the signals to detect heartbeat, measuring time intervals between continuous peak values to determine heart beat intervals, and calculating differences between adjacent intervals to evaluate variability. According to the method, through synchronous analysis of multi-dimensional data of the comprehensive heart rate, the skin conductance and the facial expression, the comprehensive understanding and evaluation ability of the health state of the old people is enhanced. The complex relationship between the emotion change and the physiological response is accurately captured and analyzed by integrating multi-source data without singly depending on any physiological index. Through the method, the health warning signal can be identified in the subtle change, and early warning can be carried out in time. Besides, environmental factors such as illumination, music and fragrance can be dynamically adjusted according to the mood and health state monitored in real time, so that more personalized and optimized environmental response is provided.

The invention discloses a heart disease prediction system based on an electrocardiosignal and a large model, and relates to the field of electrocardiosignal processing, and the system is characterized in that the electrocardiosignal of a user is collected by using a heart coat, and baseline offset suppression and noise processing operation are carried out on the electrocardiosignal by using a data processing center; a variability sequence is extracted from the processed electrocardiosignal, an electrocardiograph scatter diagram is drawn according to variability, and scatter points in the scatter diagram are numbered and coded in a partitioned mode to obtain the sequence information entropy of the heart rate variability sequence; segmenting the electrocardiosignal based on the sequence information entropy and the wave crest, obtaining a waveform segment of a single cardiac pulse according to a segmentation result, inputting the waveform segment into the large medical prediction model, and outputting the characteristic change of the electrocardiosignal; and comparing the feature change with a pre-stored disease feature standard and outputting a disease prediction result. According to the method, the complex characteristics of the electrocardiosignals and the prediction capacity of the large model are combined, and the diagnosis precision and the early-stage discovery rate of heart diseases can be remarkably improved.

The invention discloses an electrocardiosignal processing method and system, and relates to the field of biomedical signal processing.The method comprises the steps that electrocardiosignals are obtained, heart beat cutting is conducted on the electrocardiosignals, multiple heart beats are obtained, and position information of QRS waves, T waves and ST wave bands of each heart beat is obtained through a Pan-Tompkins algorithm; extracting dynamic characteristics of each heart beat based on the position information; and evaluating the importance of the dynamic features by using a random forest algorithm, screening key features, and carrying out series fusion to obtain comprehensive feature information. According to the method, through multi-dimensional feature extraction and fusion, subtle changes in the complex electrocardiosignals are captured more comprehensively, and therefore the accuracy and efficiency of electrocardiosignal analysis are improved.

The heart electrophysiological signal classification method comprises the steps that electrocardiosignals and magnetocardiogram signals are obtained, filtering and denoising preprocessing is conducted on original ECG signals and original MCG signals respectively, heart beats are divided through R-wave positioning, a two-dimensional spectrogram is obtained after Fourier transform, and the two-dimensional spectrogram is used for classifying the electrocardiosignals and the magnetocardiogram. After feature extraction, respectively establishing a multi-domain cardiac function signal data set; a convolutional recurrent neural network is constructed, synchronous processing of two different forms of heart electrophysiological signals is realized through a double-input model, shallow signal features extracted by the convolutional neural network are input into a time sequence feature extraction module formed by the recurrent neural network, local saliency features and time sequence features of ECG and MCG signals are fused, and a time sequence feature extraction module is constructed. And finally, ECG and MCG signal features for classification are formed. And inputting the fused features into a nonlinear classification layer, performing model training by using Softmax in combination with a cross entropy loss function, and outputting a classification result.

The invention discloses an intelligent arrhythmia classification device based on deep learning. The intelligent arrhythmia classification device comprises a data set construction module, a data preprocessing module, an intelligent arrhythmia classification network construction training module and an analysis module, the data set construction module is used for constructing a heart rhythm sample data set, the data preprocessing module is used for expanding the data set by applying a wavelet denoising method and randomly sampling the heart beat type data set with more data distribution by utilizing an undersampling principle, and the arrhythmia intelligent classification network construction training module is used for constructing an arrhythmia intelligent classification network based on a DenseNet-201 network. And carrying out model training by adopting an Adam optimizer and a learning rate attenuation strategy, and classifying the arrhythmia data set. And the analysis module explains a model classification result by utilizing a plurality of methods such as HiResCAM, LIME and SHAP. According to the method, the electrocardiogram can be rapidly analyzed, and the basis for model type judgment can be visually displayed.

The invention relates to the technical field of electrocardiosignal analysis, and discloses a long-sequence electrocardiosignal disease recognition system based on a Transform architecture. The core defects that in traditional electrocardiogram analysis, waveform integrity is damaged by fixed window segmentation, a lead space topological relation is neglected, and long sequence modeling efficiency is low are overcome, a P-QRS-T waveform structure is completely reserved through the heart beat adaptive segmentation technology, and the fixed window truncation risk is eliminated; the lead anatomical topology and the space-time coding are fused, and multi-lead electrophysiological association is modeled; long sequence efficient processing is realized based on hierarchical sparse Transform, and the recognition sensitivity of complex pathologies such as arrhythmia and myocardial ischemia is improved; in combination with gradient directional regulation and control and a streaming processing mechanism, the clinical real-time requirement is met while the diagnosis accuracy is guaranteed, and finally, reliable, efficient and universal intelligent decision support is provided for early warning of heart diseases through lightweight deployment of an adaptive mobile terminal.

The invention discloses an arrhythmia recognition method and system based on electrocardio waveform dynamic feature reconstruction, and the method comprises the steps: obtaining an electrocardiogram of a user, extracting the electrocardio potential of each cardiac blog in the electrocardiogram, and calculating the deformation rate vector of each cardiac blog according to the electrocardio potential; according to the deformation rate vector, calculating a heart rhythm transition value between the heart blogs as a heart rhythm transition fingerprint, calculating heart beat form difference energy between the heart blogs, and generating a form fluctuation index of each heart blog according to the heart beat form difference energy; and acquiring a change acceleration of the form fluctuation index between heart blogs, mapping the change acceleration, the form fluctuation index and the cardiac rhythm transition value into a discrete symbol, and inputting the discrete symbol into a diagnosis synthesizer to output an arrhythmia identification result.

The invention relates to the technical field of biological radar signal processing, in particular to an FMCW radar-based heart beat interval estimation method, which comprises the following steps of: S1, converting a chest vibration echo phase time sequence obtained by irradiating a chest area of a monitored object by an FMCW radar into an acceleration time sequence by using a second-order time derivative; s2, dynamically mapping the center frequency and the wavelet order in a preset frequency analysis interval, constructing a self-adaptive wavelet dictionary, then executing multi-order wavelet time domain convolution operation on the acceleration time sequence by using the dictionary, and aggregating time-frequency energy distribution to generate a time-frequency energy diagram; and S3, performing a deconvolution operation reconstruction strategy based on an energy-guided multi-stage time-frequency feature screening technology and wavelet function conjugation, generating an approximate time domain signal of heart beat vibration, and completing heart beat information inversion. According to the method, the accuracy and robustness of IBI extraction can be effectively improved under the condition of low signal-to-noise ratio, so that stable monitoring of light and moderate HRV (heart rate variability) is supported.

The invention provides an ECG classification system based on a deep residual shrinkage network and an LSTM. The ECG classification system comprises an acquisition unit used for acquiring an ECG original signal; the preprocessing unit is used for carrying out noise reduction on the ECG original signal to obtain an ECG noise reduction signal; the segmentation unit is used for carrying out heart beat segmentation on the ECG noise reduction signal to obtain a plurality of heart beats; calculating RR interval information according to the R wave of the heart beat; the classification unit is used for creating and training a classifier according to the deep residual shrinkage network and the LSTM; and inputting the ECG noise reduction signals and the RR interval information into a classifier, and classifying the ECG original signals. According to the ECG classification system based on the deep residual shrinkage network and the LSTM, the convolutional neural network, the deep residual network and the long and short term memory artificial neural network are combined to create the classifier, the ECG noise reduction signal and the RR interval information are combined to train the classifier, and the ECG signal classification accuracy is improved.

The invention belongs to the field of non-contact vital sign detection, and particularly relates to a millimeter wave radar arrhythmia detection method based on particle swarm optimization variational mode decomposition and deep learning, and the method specifically comprises the steps: S1, obtaining a human chest micro-motion signal through a millimeter wave radar, performing static clutter filtering, phase extraction, unwrapping and detrending processing on the radar echo signal to obtain a chest displacement signal containing heartbeat and breathing information; s2, aiming at the thoracic cavity displacement signal, constructing a variational mode decomposition model, and carrying out adaptive optimization on a mode number and a penalty factor through a particle swarm optimization algorithm to obtain an optimal decomposition parameter and complete signal decomposition; s3, according to the center frequency and the energy distribution characteristics of each modal component, screening the modal components in the heartbeat frequency range and reconstructing the modal components to obtain heartbeat characteristic signals representing heart mechanical activities; s4, carrying out time sequence segmentation on the heartbeat characteristic signals, extracting local heart beat morphological characteristics by utilizing a convolutional neural network, and carrying out modeling on a long-time rhythm dependency relationship of the heartbeat signals in combination with a time sequence modeling network based on an attention mechanism to obtain heart rhythm depth characteristic representation; and S5, inputting the heart rhythm depth features into a heart rhythm discrimination model, analyzing the heart rhythm state of the detected person, and outputting an arrhythmia detection result. According to the method, adaptive selection of variational mode decomposition parameters is realized by introducing a particle swarm optimization mechanism, heartbeat signals and respiration and motion interference components are effectively separated, modeling is carried out on rhythm characteristics in combination with a deep learning model, and non-contact detection of arrhythmia is realized. The method does not need to wear an electrode or contact a human body, has the advantages of strong anti-interference capability, good adaptability and high detection precision, and has a good application prospect in the fields of heart rhythm health monitoring, disease screening and the like.

The invention provides a regional anesthesia puncture path planning method and system under ultrasonic guidance, and relates to the technical field of ultrasonic image.According to the regional anesthesia puncture path planning method and system, dynamic motion information of a tissue structure is obtained by collecting ultrasonic image data of a target region and conducting motion analysis, and electrocardiosignals are synchronously collected to determine heart beat time phase information; determining a vascular movement mode based on the two, learning a time change rule by using a long-short-term memory network, and establishing an association relationship between a vascular spatial position and a heart beat phase; constructing a motion prediction model to predict the future motion trend of the blood vessel, and fusing the respiratory motion information to correct to obtain the blood vessel motion information; and finally, a safe puncture window is calculated according to the information, a regional anesthesia puncture path is planned, vascular movement prediction and safe puncture window calculation based on vascular movement rule learning and breathing correction in regional anesthesia under ultrasonic guidance can be achieved, and then the puncture path is precisely planned.

The invention discloses a coronary plaque biomechanical property calculation method, and belongs to the field of computational biofluid mechanics in biomedical engineering. According to the method, the influence of cardiac pulsation, coronary blood vessel complex geometric distribution characteristics and coronary plaque composition factors is integrally considered, and the coronary plaque biomechanical characteristics are calculated; wherein the number of the fluid computational domains is two, and the two fluid computational domains are respectively a coronary vessel intracavity blood flow area and a plaque tissue fluid flow area surrounded by a fibrous membrane and a blood vessel; the two structural computational domains are respectively an elastic blood vessel wall and an ultrathin flexible plaque fiber membrane; the plaque fiber membrane generates deformation movement under the pressure of blood in blood vessels on two sides and tissue fluid in the plaque; the motion of the coronary plaque fibrous membrane is described by adopting an independently researched and developed immersion film method, and compared with a traditional coronary plaque biomechanical characteristic calculation method, the method has better information integrity, better accuracy and wider applicability.

The invention discloses a magnetocardiogram P-wave detection method based on wavelet analysis and a local noise self-adaptive threshold, which is characterized in that accurate and robust detection of a P wave is realized by searching on a characteristic scale which can highlight the P wave, a self-adaptive threshold confirmation mechanism based on local noise estimation is introduced, and a self-adaptive threshold confirmation mechanism is established for a time domain search window of each cardiac cycle. According to the method, the internal noise level is dynamically evaluated, and an estimated value capable of accurately reflecting the local noise condition of the current heart beat can be obtained by carrying out statistical analysis on wavelet coefficients in a characteristic scale in an area where P waves are most likely to appear.

The invention discloses a smart watch arrhythmia early warning method and system based on deep learning. The method comprises the steps that firstly, PPG signals are collected through a photoelectric volume pulse wave sensor, and denoising and standardization processing are conducted through an adaptive filtering algorithm; then, the heart beat position is recognized through a wavelet transform peak value detection algorithm, and a heart rate variability feature sequence is obtained; converting the one-dimensional heart rate variability feature sequence into a two-dimensional heart rhythm image by using an improved Gramian angular field transformation algorithm; and training an arrhythmia classification model by adopting a ResNet-50 convolutional neural network to realize automatic identification of different types of arrhythmia. And finally, continuous monitoring and timely alarming are realized through a sliding window technology and an early warning mechanism. The key technical problems of low detection precision, poor real-time performance, high equipment cost, poor user experience and the like in the existing arrhythmia detection technology are effectively solved, and an innovative solution is provided for heart rhythm health monitoring of the intelligent wearable equipment.

To efficiently connect the estimated value of vital information and a user to each other and determine whether the vital information of a target person is to be measured by a medical apparatus.SOLUTION: A video of the face of a user is taken by a built-in camera (S1) and the vital data of the user is estimated by RPPG (S2). For example, it is estimated that the heart beat is 70 / min and the blood pressure is 163 / 98 mmHg. When the estimated vital data is off standard values, a measurement using a medical apparatus is promoted (S3). For example, the message that "the blood pressure is off the standard values. Do you want to make a measurement again by a medical apparatus?" is displayed.SELECTED DRAWING: Figure 1

A method of operating a counterpulsation device (CPD) in a human or animal subject is disclosed, the method including: receiving a heart beat signal indicative of the heart beat of the subject; providing counterpulsation therapy by controlling the pressure supplied to a CPD drive line in pneumatic communication with the CPD to cause the CPD to alternately fill with blood and eject blood with a timing that is determined at least in part based on the heart beat signal; while providing counterpulsation therapy, receiving a CPD drive line pressure signal indicative of the pressure in the CPD drive line; and adjusting the pressure supplied to the drive line based at least in part on the drive line pressure signal.

The present invention discloses a multi-channel ultrasound monitoring system and method based on data analysis, which relates to the technical field of data analysis and signal processing. The method comprises the following steps: S100, acquiring original ultrasound signals from each channel through a wearable flexible ultrasound monitoring array; S200, performing denoising and filtering processing on the original ultrasound signals received from each channel, and extracting key characteristic parameters through a signal processing algorithm; S300, analyzing the extracted signal characteristics, and using a signal classification algorithm to determine whether the current signal comes from a target area; S400, performing cardiac pulsation analysis based on A-ultrasound signals, calculating the dynamic displacement of cardiac tissue through changes in echo time, and generating a time-displacement curve of cardiac pulsation in combination with multiple frames of A-ultrasound signals; S500, adjusting the monitoring strategy through real-time monitoring data. The present invention solves the disadvantage that a single array element easily deviates from the target by switching multiple array elements.

To perform synchronous reconfiguration to the periodic movement of a subject without using an external device.SOLUTION: A nuclear medicine diagnosis device analyzes a respiratory movement from list mode data of scanning in which the respiratory movement and heart beats occur, divides the list mode data for every respiratory phase to create first images, analyzes the heart beats for every respiratory phase, creates second images from data obtained by dividing the list mode data for every respiratory phase and data obtained by the division for every heart beat phase, acquires at least one of a first motion vector between the respiratory phases and a second motion vector between the heart beat phases, and applies the first motion vector to the second images and adds up the images, for every group of the second images different in the respiratory phase from each other and common in the heart beat phase, to create a first single phase image corresponding to a specific phase, or applies the motion vector to the second images and adds up the images, for every group of the second images different in the heart beat phase from each other and common in the respiratory phase, to create a second single phase image corresponding to the specific phase.SELECTED DRAWING: Figure 9

The application discloses an old person free breathing coronary artery scanning image lesion analysis system and relates to the technical field of scanning image lesion analysis. In order to solve the problem that the accurate condition of a patient cannot be obtained according to a scanning image. The application adopts a diameter method, an area method and a contrast agent filling condition to judge the stenosis degree and the hemodynamic change, analyzes the lesion from the morphological and functional double angles, provides comprehensive information for clinical decision-making, identifies the R wave peak value and divides the cardiac phase, matches the respiratory signal and the projection data in time, can accurately capture the characteristics of the heart in different motion states and the respiratory stage, effectively avoids the interference of the artifacts caused by the heart beat and the respiratory motion, makes the reconstructed image clearer and more accurate, sets the CT and the injector parameters in sequence from the scanning type confirmation, each link is closely related and the target is clear, improves the work efficiency, and is convenient for quality control and process management.

The present application relates to the field of respiratory heart rate monitoring, and discloses a non-contact heart rate and respiration recorder, which comprises a host computer, a wireless charging base, a charging cable and a terminal device, wherein the host computer is internally provided with a non-contact sensor module, a processor, a battery and a wireless communication module, and the shell adopts a waterproof sealing structure. In use, the host computer is placed under a pillow, and a heart beat and respiration channel signal is collected through the non-contact sensor, and after filtering, normalization and artifact removal, the signal forms a heart beat observation and a respiration observation under the action of parameters such as a sampling rate, a time window length, a vertical offset and a coupling stiffness, and is subjected to joint optimization under the constraints of spectral matching and cross-modal coherence, and finally outputs a heart rate and a respiration rate. The device does not need to wear an electrode, is stable in detection, and is suitable for long-term home health monitoring and clinical assistance.

To diagnose and output a compatibility degree of passengers from a synchronization rate of the heart beats of arbitrary two passengers from a space of a vehicle room.SOLUTION: A compatibility degree determination device (1) includes: an input unit (110) for acquiring data obtained by simultaneously measuring the heart beats of at least two passengers in a space of a mobile body (2A); and a compatibility degree determination unit (125) for determining a compatibility degree of a first passenger and a second passenger according to a synchronous degree of the combination of the heart beat data.SELECTED DRAWING: Figure 1

A method to process signals acquired with at least one accelerometer and one gyroscope worn by a subject for evaluating a heart rate and / or a respiratory rate. The method includes: receiving the accelerometer and gyroscope signals at a sampling frequency in a given time window; combining each pair of accelerometer and gyroscope signals in the given window to output an orientation vector for each sampling time using a quaternion representation; applying at least one filter to the orientation signal defined by the orientation vectors to obtain a filtered signal; calculating an average breath cycle and / or time interval separating two consecutive heart beats using an algorithm for modeling an average signal, which iteratively determines the average between the filtered signal and the average signal from a preceding iteration; estimating a heart and / or respiratory rate from the calculated average breath cycle and / or time interval separating two consecutive heart beats.

An apparatus includes a photoplethysmography (PPG) sensor configured to measure a PPG signal associated with a user, an accelerometer configured to measure an acceleration signal associated with the user, and a processor operatively coupled to the PPG sensor and the accelerometer. The processor is configured to determine whether the user is engaging in movement based on the acceleration signal, in response to determining that the user is engaging in movement, determine a type of movement of the user based on the acceleration signal, generate a probability distribution associated with a pulse rate of the user based on the type of movement, and determine the pulse rate of the user based on the PPG signal, the acceleration signal, and the probability distribution.

The application provides a magnetocardiogram signal superposition average optimization method based on heart beat classification, and is suitable for the field of magnetocardiogram signal processing and analysis. The method comprises the following steps: obtaining a denoised magnetocardiogram signal; performing R wave detection on the denoised magnetocardiogram signal and performing heart beat segmentation; setting the segmented first heart beat as a heart beat classification, and calculating the correlation of subsequent heart beats with the first heart beat; the heart beat with a correlation greater than a threshold value is considered to belong to the existing classification, and the heart beat with a correlation less than the threshold value is taken as a new heart beat classification; repeating the above process until the correlation calculation of all heart beats is completed; superimposing and averaging all heart beats in the same classification; calculating the correlation between the superimposed and averaged results again, merging the classifications with high correlation, recalculating the superimposed average, and obtaining the result of the heart beat classification superimposed average. The present application improves the existing magnetocardiogram superposition average algorithm and provides a more accurate magnetocardiogram superposition average image.

The present application discloses a blood pump speed pulsation control method, system, and related products. The method: based on the electrical signal change information of the blood pump motor within a historical period, detects the start time of the N0 diastole and the start time of the N0 systole of the current patient's heart beat within the historical period; based on these start times, determines the start time of the N1 diastole and the start time of the N1 systole of the heart after the historical period. The method uses the patient's individual historical electrical signal change information as a basis to determine the future start time of the diastole and the start time of the systole of the current patient's heart, ensuring that the speed adjustment time of the blood pump motor is personalized to conform to the physiological contraction law of the individual heart, and realizing the specific output of the blood pump such as the blood pumping time and the blood pumping volume per time to flexibly follow the blood flow pulsation output of the heart beat, that is, to assist the blood pump in generating a pulsating flow synchronized with the natural heart beat, thereby effectively and reliably maintaining the patient's life safety.

Embodiments of the present application relate to a kind of electrocardiosignal processing method and device, the method includes: receiving first electrocardiosignal;Filtering processing generates second electrocardiosignal;Baseline drift elimination processing is respectively carried out to first, second electrocardiosignal, and third, fourth electrocardiosignal is generated;R point identification processing is carried out to fourth electrocardiosignal, and first R point sequence is generated;Electrocardiosignal segment intercepting processing is carried out to third, fourth electrocardiosignal, and first, second electrocardiosignal segment sequence is generated;Heart beat classification processing is carried out to first electrocardiosignal segment sequence, and first segment type sequence is generated;Interference classification processing is carried out to second electrocardiosignal segment sequence, and second segment type sequence is generated;Segment type fusion processing is carried out to first, second segment type sequence, and third segment type sequence is generated;First R point sequence and third segment type sequence are as electrocardiosignal processing result output.The processing efficiency of electrocardiosignal processing can be improved in the present application and the stability of processing quality is guaranteed.

The present application relates to magnetic resonance examination device technical field, specifically to a kind of cardiac magnetic resonance examination device, including fixed plate and electrocardiogram control patch, fixed plate is equipped with chest coil;Fixed plate is detachably connected with detection plate, detection plate is equipped with breathing training component for exerting chest pressure, breathing training component is electrically connected with control panel;Control panel records and draws fluctuation curve based on the pressure data detected by breathing training component, then obtains the breathing interval of patient based on fluctuation curve, sends breath-holding reminding instruction to voice module according to breathing interval;Detection plate side away from fixed plate is equipped with several electrocardiogram monitoring electrode pieces for detecting the quality of heart beat signal strength, detection plate is equipped with driving mechanism for driving electrocardiogram monitoring electrode piece to move in range;The present application is used to assist patient breathing breath control, realize the accurate matching of patient breathing breath opportunity and imaging sequence, reduce the generation of breathing artifact, guarantee the progress of cardiac magnetic resonance.

The present invention provides a method for analyzing ambulatory electrocardiogram based on a two-stage template of waveform rhythm, which can analyze ambulatory electrocardiogram comprehensively and quickly. At the same time, the method is simple, the process is fixed, and it is easy to master, especially suitable for analyzing a large number of ambulatory electrocardiograms. In the technical solution of this application, the analysis process of ambulatory electrocardiogram is divided into two analysis stages: the analysis process based on waveform template and the analysis process based on rhythm template; all the waveforms of heart beats are batch-reviewed through the waveform superposition diagram corresponding to the clustering template, and the clutter template is browsed one by one in the way of sample diagram; through the generation process of the rhythm template, the missed beat template and supraventricular premature beat template that the analysts mainly focus on are screened out, and combined with other graphic tools, the data in the missed beat template and supraventricular premature beat template are comprehensively reviewed. At the same time, this application also discloses an ambulatory electrocardiogram analysis system based on a two-stage template of waveform rhythm.

A method for synchronizing operation of a heart assist pump device to a patient's cardiac cycle includes obtaining a signal from a motor of a heart assist pump device and filtering the signal to remove noise. The method also includes determining a speed synchronization start point at which time the motor of the heart assist pump device will begin a change in speed of operation based on the filtered signal. The method further includes modulating a speed of the motor of the heart assist pump device to a target speed at the speed synchronization start point, thereby synchronizing the change in speed of operation with a patient's cardiac cycle.

The present application is applicable to the field of electrocardiogram technology, and provides a dynamic electrocardiogram data processing method, device, electronic device and storage medium, wherein the method comprises: preprocessing dynamic electrocardiogram data to obtain a beat set, the beat set comprising multiple beats; generating and displaying a time-RR interval scatter plot according to the RR intervals of the beats in the beat set; in response to a first selection instruction for the time-RR interval scatter plot, selecting a first target beat indicated by the first selection instruction from the beat set; displaying an electrocardiogram segment corresponding to the first target beat; performing a QRS wave search on the electrocardiogram segment according to a parameter threshold to obtain a missing QRS wave; and dividing the electrocardiogram segment according to the missing QRS wave to update the beat set.