849 results about "Ecg signal" patented technology

The invention discloses a multi-label electrocardiogram classification method based on self-supervised pre-training and multi-modal semantic alignment, which belongs to the technical field of artificial intelligence, and comprises the following steps: realizing self-supervised pre-training of unlabeled data through a single-modal contrast enhancement network, generating global and local contrast views by adopting a multi-scale random cutting strategy, and classifying the global and local contrast views in a multi-scale random cutting mode; in combination with a teacher-student network architecture, the potential invariance features of the ECG signals are learned while negative sample dependence is avoided, the problem of annotation data scarcity is effectively relieved, and the feature robustness is improved. A multi-modal fusion mechanism based on label semantic guidance is provided, a time domain signal and a frequency domain time-frequency graph are mapped to a unified semantic space through fine-grained semantic alignment, local feature enhancement and cross-modal complementary information fusion are realized by using a cross attention mechanism, and the problem of semantic difference caused by modal heterogeneity in a traditional method is overcome. A multi-label comparison loss function based on a disease co-occurrence relation is proposed, a category discrimination boundary is dynamically optimized by modeling a label co-occurrence probability, the feature separability of a tail category is improved while the head category discrimination ability is enhanced, and the problem of sample category imbalance in a multi-label scene is remarkably relieved.

The invention discloses an electrocardiogram classification system and method fusing multi-scale self-adaptive attention, and relates to the technical field of electrocardiogram classification.The electrocardiogram classification method comprises the steps that electrocardiogram data are collected through a data collection module, and a structured data set is output; the data preprocessing module is used for carrying out data preprocessing on the structured data set; the window data segmentation module obtains a standardized windowed electrocardiosignal; the feature extraction module takes the standardized windowed electrocardiosignals as input, performs three stages of multi-scale convolution feature extraction, liquid neural network dynamic modeling and self-adaptive attention mechanism enhancement, and outputs diagnosis results of nine types of heart diseases; and the training and optimizing module is used for optimizing the extraction and classification capability of the model on the electrocardiosignal features, so that the technical effects of full-process technical upgrading from data acquisition, preprocessing, feature extraction to model training optimization and high-precision electrocardiogram automatic classification are achieved.

The invention discloses an electrocardiosignal preprocessing system and method based on filtering and deep learning, and relates to the field of electrocardiosignal data processing. The multi-stage adaptive filtering module comprises a baseline drift elimination unit, a power frequency interference suppression unit and a myoelectricity noise removal unit, and all the units are connected in sequence to form pipelined parallel processing; the deep learning fusion module is used for performing deep feature extraction and noise classification on the output signal and feeding back a classification result to the multi-stage adaptive filtering module; the signal quality evaluation module carries out quality evaluation on the electrocardiosignals subjected to multi-stage adaptive filtering and deep learning fusion and judges whether the signal quality is qualified or not; a feature enhancement and standardization module; the technical effects of improving the self-adaptability and robustness, improving the calculation efficiency of heart disease classification, the signal fidelity and the diagnosis reliability, and enhancing the signal quality evaluability and the self-adaptive ability are achieved.

The invention belongs to the field of intelligent rehabilitation medical instruments, and particularly relates to a brain-computer interface-based multi-modal child sensory integration training device, which comprises a brain-computer interface head ring, an intelligent touch floor and AR interactive glasses, the brain-computer interface head ring is connected with a biological signal acquisition module, the intelligent touch floor is connected with a motion trail analysis unit, the AR interactive glasses are connected with a universe scene engine, and the biological signal acquisition module, the motion trail analysis unit and the universe scene engine are connected with a digital twin generator. The digital twin generator is connected with a dynamic mode switching decision tree, and the dynamic mode switching decision tree is connected with a multi-mode feedback actuator; the brain-computer interface head ring is located on the head of the child and collects electroencephalogram, myoelectricity and electrocardiosignals through a biological signal collection module; and the biological signal acquisition module sends a signal to the digital twin generator through wireless transmission. According to the invention, the training efficiency can be improved, the evaluation dimension can be expanded, potential safety hazard early warning can be realized, and the compliance can be enhanced.

The invention relates to the technical field of remote monitoring, in particular to a wireless electrocardiograph monitor based on the Internet of Things and a data sharing method. A lead signal sequence is collected, window variance and adjacent difference slope are evaluated to exceed a threshold value to generate a trigger instruction, a low-noise channel packaging data packet is called to monitor time delay and interrupt to generate a parameter set, and the parameter set is sent to a server; and extracting interrupt over-limit interface detection impedance to trigger port switching to generate a connection state, and counting a link stability period comparison threshold value to restart a channel. By calculating the time window variance and the differential slope mean value of the electrocardiosignal in real time, comparing with a preset threshold value, accurately identifying abnormity, reducing abrupt change detection delay, dynamically screening low-interference channels based on channel noise, optimizing a transmission path in combination with data priority classification and transmission interruption statistics, reducing the influence of environmental fluctuation on the waveform, and improving the detection accuracy of the electrocardiosignal. Physical lead switching is triggered through wire harness impedance detection, a double-link redundancy architecture is constructed, and the risk of single-link failure is avoided.

The invention discloses a 12-lead electrocardiosignal generation method based on a medical text and related equipment. The method comprises the steps that text information is acquired; inputting the text information into a pre-trained TTE model to generate a 12-lead electrocardiogram signal; wherein the TTE model comprises an encoder, a noise predictor and a decoder; in an electrocardiogram generation stage, a noise predictor takes a text semantic condition as input, starts from initial random Gaussian noise zT, gradually recovers a potential feature vector # imgabs0 # pretrained decoder meeting conditional constraints through iterative denoising, reflects a potential feature vector # imgabs1 # to a high-dimensional original signal space, and outputs the potential feature vector # imgabs1 # pretrained decoder to a high-dimensional original signal space. According to the method, medical text description is used as condition input, text semantic constraints are embedded in a potential diffusion model framework, 12-lead simulated electrocardiosignals conforming to specific pathological features are generated, and a feasible alternative scheme is provided for shortage of current labeled ECG data sets.

The invention discloses a millimeter-wave radar fusion system and method for electrocardiograph monitoring false alarm recognition, and the method comprises the steps: carrying out the multi-scale time-frequency feature extraction of a millimeter-wave radar signal and an electrocardiograph signal, obtaining a radar feature sequence and an electrocardiograph feature sequence, carrying out the time alignment of the radar feature sequence and the electrocardiograph feature sequence, and inputting a multi-modal fusion architecture; outputting cross-modal joint embedding features based on the multi-modal fusion architecture; a cross-modal consistency judgment model obtained based on self-supervised contrast learning optimization training is constructed, whether a cross-modal feature inconsistent state exists in the joint embedded features or not is judged, and if yes, it is judged that a potential false alarm event exists; and based on the potential false alarm event, extracting sequence feature fragments before and after alarm triggering in the joint embedded feature, inputting the sequence feature fragments into an anomaly classification network, outputting a final judgment result about whether false alarm is formed, and when the final judgment result is false alarm, generating an alarm adjustment instruction and sending the alarm adjustment instruction to a monitoring terminal.

The invention relates to the field of non-invasive blood pressure monitoring, in particular to a single-channel PPG non-invasive blood pressure monitoring system based on generative ECG enhancement, which comprises a data preprocessing module for preprocessing an ECG signal and a PPG signal to obtain a PPG-ECG signal sample and a PPG signal sample with a blood pressure label; the ECG generation model is used for generating a cross-modal physiological signal from PPG to ECG to obtain a time domain aligned generative ECG signal; according to the blood pressure prediction model, a feature extractor is constructed based on an improved U-Net architecture, a multi-scale convolution module and a cross-modal attention fusion module are embedded, extracted spatial and temporal features are input into a mapping regression device, continuous predicted values of systolic pressure and diastolic pressure are output, and end-to-end blood pressure regression is achieved. According to the method, cross-modal data enhancement is achieved by constructing the time domain aligned generative ECG signals, meanwhile, multi-scale convolution and a cross-modal attention fusion module are integrated in the prediction model, and the precision limitation of single-channel PPG blood pressure prediction is broken through.

The invention belongs to the technical field of wireless sensing and artificial intelligence, and discloses a high-robustness non-contact accurate electrocardiogram monitoring method based on a millimeter wave radar. Firstly, the distance and angle of a potential target are obtained through distance fast Fourier transform and digital beam forming technologies, and static background removal and thoracic cavity position detection are achieved in combination with mean filtering and a two-dimensional constant false alarm rate algorithm. And then a continuous phase is extracted by using a differential cross multiplication method, a two-step heartbeat-related phase extraction scheme is designed, body micro-motion and breathing interference are removed by adopting B-spline fitting and differential operation respectively, and a stable heartbeat-related phase signal is obtained. A heart rate-guided adaptive wavelet decomposition method is designed to obtain multiband features, and time-frequency joint features are extracted through a double-branch attention mechanism and a gating fusion part. Finally, the time-frequency joint features are input into an electrocardiosignal time domain reconstruction module based on a TransUNet architecture, high-quality reconstruction of electrocardiosignals is achieved, and the method has the advantages of being non-contact, continuous and convenient.

The invention belongs to the technical field of medical health monitoring, and relates to a noninvasive blood glucose detection method based on reinforcement learning and multi-modal dynamic weight fusion, and the method comprises the following steps: carrying out adaptive dynamic weight distribution on the weight of an ECG signal and a PPG signal based on a reinforcement learning network, optimizing a signal processing parameter, and determining whether to trigger threshold adjustment; performing weighted feature fusion on the ECG signal and the PPG signal to obtain a fusion feature vector; the fusion feature vector is input into a prediction and early warning double-branch output structure, the prediction and early warning double-branch output structure comprises a regression branch and a classification branch, the regression branch is used for continuously predicting the blood glucose value, and finally a single blood glucose concentration value is output; and the classification branch is used for early warning level judgment, and finally outputting classification alarms for continuous prediction results so as to complete noninvasive blood glucose detection. The method has the beneficial effects that the fluctuation influence of user movement and temperature on noninvasive blood glucose data acquisition is reduced, and the individual difference suitability and long-term stability of noninvasive blood glucose detection are improved.

The invention discloses a depression classification method and system based on multi-modal fusion, and relates to the technical field of multi-modal data processing and intelligent identification. Comprising a data acquisition module used for acquiring an electrocardiosignal, an electroencephalogram signal, a facial expression video and a gastrointestinal environment expiration signal; the pre-processing module is used for performing pre-processing operation on the four modal signals; the data fusion module is used for performing high-order feature extraction and dimensionality reduction on the four modal signals by using different deep learning sub-networks, considering the real-time performance and the mutual relation between different modals, and performing feature fusion on the four modal signals after dimensionality reduction based on an attention soft fusion strategy; and the classification detection module is used for performing classification detection on the comprehensive features by using a deep learning classification model. According to the method, real-time signals of four modes are collected, and efficient and accurate recognition and dynamic monitoring of the depression state are achieved in combination with multi-mode signal preprocessing, multi-domain feature extraction, multi-mode fusion and a deep learning classification model.

The invention relates to the technical field of sleep monitoring, in particular to a portable sleep monitoring method based on edge computing and a sleep instrument.The portable sleep monitoring method comprises the following steps of obtaining breathing data and screening continuous rhythm fragments, extracting electrocardiosignal difference values to recognize a jump state, checking double-signal cycle mutation and synchronously distributing fragments, reading vibration waveform freezing interference paragraphs, and obtaining a sleep monitoring result. And pausing state updating and replacing the output result to obtain a state buffer structure identifier. According to the method, continuous recognition of rhythm fragments is enhanced through periodic sequence labeling, state features are extracted in combination with electrocardio amplitude abrupt change, change fragments are screened through double-signal synchronous abrupt change, vibration waveform rhythm differences are superposed to position interference sections, labels are continuously output by means of a state freezing mode, and state connection in the signal switching process is enhanced; the abrupt change section separation capability and the multi-signal cooperative processing level are improved, the problems of label disorder and state hopping are relieved, and the signal processing stability and stage output consistency in a dynamic monitoring scene are optimized.

The invention relates to the technical field of intelligent medical instruments, and discloses a closed-loop electro-acupuncture therapeutic apparatus based on heart and brain coupling information feedback. The device comprises a forehead electroencephalogram signal acquisition module, a single-lead electrocardio acquisition module, a Bluetooth transmission module, a signal preprocessing module, an ECG and EEG feature extraction module, a dynamic coupling analysis module, an embedded XGBoost classifier and an electroacupuncture control module. According to the system, collected EEG and ECG signals are wirelessly transmitted through Bluetooth, HRV indexes and EEG frequency band power spectral density are extracted after preprocessing, and frequency domain coherence analysis is carried out to obtain heart and brain bidirectional coupling characteristics. The embedded XGBoost classifier outputs optimal electroacupuncture stimulation parameters based on the characteristics, and the electroacupuncture control module generates corresponding bidirectional pulse waves for stimulation. According to the therapeutic apparatus, closed-loop feedback control is achieved, therapeutic parameters can be dynamically optimized, the individuation and precision level is improved, and meanwhile safety is ensured through impedance monitoring and electrical isolation.

The invention provides a health monitoring module and electronic equipment. The health monitoring module comprises a shell, an optical heart rate module, a circuit board and a light transmitting part, the shell is fixed to one side of the circuit board, and the shell and the circuit board jointly define a containing space. The shell comprises a conductive part made of conductive materials, one part of the conductive part is exposed out of the top face of the shell, and the other part of the conductive part is electrically connected with the circuit board. The optical heart rate module is located in the containing space, the optical heart rate module is fixed to the circuit board and electrically connected with the circuit board, the optical heart rate module and the light hole are oppositely arranged, and the optical heart rate module emits and receives light through the light hole and the light transmitting piece. The optical heart rate module can be used for acquiring PPG signals of a user. The conductive part can be used for collecting electric signals of a user. And the ECG signal of the user can be obtained after the electric signal is processed. Devices for measuring ECG signals and PPG signals are integrated in one module, so that the module can realize multiple functions in a small size.

The invention relates to the technical field of medical monitoring, and provides a high-precision sleep electrocardio continuous monitoring system and method.The high-precision sleep electrocardio continuous monitoring system and method.A modular closed-loop framework is adopted, and synchronous acquisition of electrocardio, movement and respiration signals is achieved through a multi-modal sensing and high-precision synchronous acquisition module; the signal quality multi-dimensional traceability diagnosis module performs feature extraction and noise classification on the input signal; the dynamic traceability filtering processing module intelligently calls a corresponding filtering algorithm to perform signal purification according to the noise type; and the intelligent output and self-adaptive resource management module outputs high-quality electrocardiosignals and realizes dynamic optimization of system power consumption. By establishing a complete signal quality evaluation system and an intelligent processing mechanism, the whole process optimization of the sleep electrocardiosignals from collection to output is realized, and the accuracy of monitoring data and the cruising ability of the system are remarkably improved.

Disclosed are a single-arm electrocardiogram (ECG) signal measurement device and a single-arm ECG signal measurement method. The single-arm ECG signal measurement device includes a first sensing electrode, a second sensing electrode, a third sensing electrode, a fourth sensing electrode, a fifth sensing electrode, a sixth sensing electrode, and a computing circuit. The first sensing electrode, the second sensing electrode, the third sensing electrode, the fourth sensing electrode, the fifth sensing electrode, and the sixth sensing electrode receive physiological signals from six different positioning points located on an upper arm of a user. The computing circuit generates ECG signals according to the physiological signals, and generates a 12-lead ECG-like signal according to the ECG signals.

The invention relates to the technical field of data fusion, in particular to a multi-source electrocardiosignal correction method and system based on adaptive fusion, and the method comprises the following steps: constructing a multi-channel input tensor, extracting local features through a weight calculation network, carrying out the adaptive weight fusion and dimension reduction of multiple paths of signals, and carrying out the correction of the multi-source electrocardiosignal. A nonlinear mapping relation is established through a deep reconstruction network, a standard waveform is reconstructed, and network parameters are optimized based on reconstruction error reverse iteration. According to the method, local neighborhood features of multichannel signals are extracted by constructing a weight calculation network, a dynamic channel weight sequence reflecting the real-time contribution degree of a signal source is constructed, the amplitude intensity is adaptively adjusted according to the signal quality, unstable channel noise interference is effectively inhibited, and high-quality signal components are enhanced; a deep reconstruction network is used for carrying out nonlinear feature transformation on a fusion sequence, accurate mapping from non-standard input to standard lead waveforms is established, and weight distribution and optimization of signal reconstruction parameters are achieved in combination with an error back propagation mechanism.

The invention discloses a patch position correction method and system, relates to the technical field of biomedical engineering and digital health, and adopts a hardware-level time synchronization and motion-artifact coupling modeling technology to effectively solve the problems of time delay drift and motion interference of signal acquisition in a dynamic environment and remarkably improve the basic quality of electrocardiosignals. Secondly, by establishing a body surface reference coordinate system and a pose compensation parameter generation algorithm, the micro-displacement state of the patch can be accurately recognized, and visual position adjustment guidance is generated, so that the patch is always kept at the optimal measurement position, and the phenomenon of signal attenuation caused by poor contact is fundamentally reduced; a signal quality multi-dimensional evaluation system adapts to quality discrimination requirements in different motion states through an intelligent weighted fusion mechanism, and combines an adaptive filtering and deep learning enhancement technology, so that the morphological integrity of a QRS waveform is kept, various interference components are effectively inhibited, and the system can still keep excellent electrocardiosignals in a strenuous motion state.

The invention provides an intelligent electrocardiogram abnormity prediction method and system based on videos. The method comprises the steps that S1, video data are collected, face information in the video data is locked, and rPPG signals are extracted; s2, an FANMAMBA network model is constructed, the rPPG signal is input into the FANMAMBA network model, and an ECG signal is generated; and S3, inputting the rPPG signal and the ECG signal into an ECG anomaly detection large model to obtain fusion features of the signals, and further generating an anomaly prediction result. The non-contact monitoring method not only exceeds the traditional contact technology, but also realizes efficient and accurate electrocardiosignal monitoring, and is widely suitable for assisting health monitoring.

The invention provides a non-invasive blood pressure estimation method, device and equipment, a storage medium and a product, and belongs to the technical field of biomedicine, and the method comprises the steps that an ECG signal and an ICG signal are preprocessed; generating physiological feature indexes, wherein the physiological feature indexes comprise conventional indexes and special indexes; determining an incidence relation between the conventional index and the basic peripheral vascular resistance parameter as well as the basic artery compliance parameter; inputting the conventional indexes into a pre-trained machine learning model, and outputting basic peripheral vascular resistance prediction parameters and basic artery compliance prediction parameters; performing dynamic correction according to the special indexes, and generating a target peripheral vascular resistance parameter and a target artery compliance parameter; and calculating and generating a blood pressure estimation result. According to the non-invasive blood pressure estimation method, device and equipment, the storage medium and the product, the accuracy, dynamic nature and stability of non-invasive blood pressure estimation can be improved.

The invention discloses a PICC (Peripherally Inserted Central Catheter) tip precise navigation fixing method and system based on electrocardiogram real-time positioning. High-precision positioning and safe fixing of a catheter tip are realized through a multi-modal data fusion and deep learning technology. The method comprises the following steps: collecting intracavity electrocardiosignals (ECG) in real time through a catheter built-in electrode, and obtaining blood vessel wall contact pressure and temperature data in combination with an optical fiber sensor; an improved wavelet threshold algorithm is adopted to dynamically suppress motion artifacts, and P-wave features are enhanced; the preprocessed ECG signals are input into a spatial-temporal feature fused Transformer model, a time attention layer is used for analyzing P-wave time sequence changes, a space attention layer integrates multi-lead space distribution features, and the positioning precision is optimized in combination with preoperative blood vessel anatomical data; the ECG positioning result and the optical fiber sensing data are fused through Kalman filtering, three-dimensional position information of the tip end of the catheter is generated, and real-time navigation is conducted on an augmented reality interface; and when the P wave amplitude reaches a CAJ threshold value, the contact pressure is safe and no abnormal temperature exists, a shape memory alloy (SMA) fixing ring is triggered to contract, and accurate fixing is achieved. The method solves the problems that a traditional method depends on X rays and is poor in anti-interference performance, has the advantages of being free of radiation, high in real-time performance and adaptive to blood vessel deformation, and is suitable for clinical P catheter implantation.

The disclosed systems and methods provide systems and methods for rhythm recognition and the analysis of ECG signals.

The invention discloses a multi-modal interaction evaluation system and method based on synchronous data acquisition, and relates to the technical field of biomedical engineering and man-machine interaction, and the method comprises the steps: generating a synchronous pulse sequence through a synchronous signal generator according to the same coding time base, and outputting the synchronous pulse sequence to an optical path and an electrical path at the same time; pulse events are recognized in the video and the electrophysiological data respectively, and observation pairs of counting numbers and timestamps are obtained; time drift is continuously estimated and compensated through robust regression in a sliding window, and a unified time axis is generated; furthermore, a dynamic time window of heart beat anchoring is constructed according to an R peak event of the electrocardiosignal, and consistent segmentation and feature extraction of cross-modal data are achieved.

The embodiment of the invention provides a data processing method and device and electronic equipment, and relates to the technical field of data processing.The method comprises the steps that multi-lead electrocardiosignal data are obtained and subjected to standardization processing; dividing the patch into a plurality of non-overlapping patches; converting each patch data into a first embedded vector by using a multi-head attention mechanism; extracting attention results of each first embedded vector at a plurality of attention heads, and splicing to obtain global feature representation; performing embedding processing on the patch data by utilizing a sliding window attention mechanism to obtain a second embedding vector; dividing into a plurality of window vectors containing overlapped parts, and extracting an attention result at a single attention head to obtain local feature representation; calculating a difference value between the global feature representation and the local feature representation; and comparing the difference value with a preset difference threshold value to obtain an abnormal detection result of the electrocardiosignal data. According to the embodiment of the invention, the accuracy of abnormal electrocardiosignal recognition is improved.

Methods and systems for automated electrocardiogram (ECG) analysis using neural networks, enhancing the accuracy of beat-by-beat cardiac monitoring. The system utilizes a Generative Adversarial Network (GAN) and beat classifiers to analyze ECG data and detect conditions various beast properties of an ECG at a discrete level. Additional neural networks may be trained to detect beat based conditions such as premature atrial contractions (PACs) and premature ventricular contractions (PVCs). The GAN generates realistic ECG beats, while classifiers detect abnormalities. Additional transformers may be trained to detect rhythm based conditions such as AFib and Aflutter. Methods and Systems support real-time cardiac health insights and integrates with ECG devices for continuous monitoring, offering a robust solution for improving diagnostic accuracy.

The invention relates to the technical field of biological wave resonance, provides a migration analysis method and system based on biological wave resonance, and aims to solve the problems of low accuracy and poor anti-interference capability of biological wave resonance anomaly detection in the prior art. The method comprises the steps that electrocardiosignals and brain wave signals of a living body and micro-deformation data of the surface of the living body are collected, and the signal data are jointly aligned in a time-frequency domain; dynamically suppressing interference frequency bands of the electrocardiosignal and the brain wave signal which are jointly aligned respectively; fusing the suppressed electrocardiosignal, the brain wave signal and the micro-deformation data after joint alignment to generate a fusion result; determining an offset feature of the fusion result relative to a preset reference state by using convolution operation; and generating a biological wave resonance migration analysis report about whether the biological wave resonance is abnormal or not according to the continuous length and the spatial distribution range of the migration characteristics in the time domain dimension. According to the invention, the accuracy and anti-interference capability of biological wave resonance anomaly detection are improved.

The invention discloses an electrocardiosignal artifact elimination method and system based on dynamic channel weighting, and the method comprises the steps: a data collection and preprocessing stage: collecting original ECG physiological signal data, and building a data set suitable for subsequent model training; a network model construction stage: designing a network architecture with a dynamic channel weighting mechanism for electrocardiosignal motion artifact confrontation elimination; a model training stage: training the model by using the constructed data set; in the practical application stage, ECG signals to be processed are input into the trained model, artifact filtering is achieved through multi-stage feature processing, and purified ECG signals are output. The system comprises a data set construction unit, a model construction unit, a model training unit and an artifact elimination unit. By using the method and the device, artifacts in the electrocardiosignals can be effectively eliminated. The method can be widely applied to the field of signal processing.

The invention relates to the technical field of medical health monitoring, in particular to an epileptic seizure cycle HRV feature mining and early warning system and method.Multi-channel electrocardiosignals are collected through wearable equipment, RR intervals are extracted in a layered mode, a high-dimensional manifold and a dynamic graph are constructed, quantum state attention and chaos pooling are combined, and key nodes and attractor modes are recognized; the method comprises the following steps: extracting epileptic risk dynamic characteristics, generating multi-dimensional risk scores and dynamically calibrating, finally outputting graded early warning and intervention suggestions, realizing intelligent prediction and management of epileptic seizure, revealing inherent geometric characteristics of HRV data through a manifold mapping technology, and compared with a traditional Euclidean space analysis method, the method provided by the invention has the advantages that the efficiency is high; the real distance between different physiological states can be measured more accurately, and the accuracy of feature characterization is improved.

The invention discloses a non-contact ECG signal monitoring method based on a millimeter wave radar, belongs to the field of ECG signal monitoring, realizes high-precision electrocardiogram reconstruction by simultaneously sensing chest vibration caused by heartbeat and carotid artery pulsation from the neck and fusing spatial-temporal characteristics, and solves the problems that a traditional scheme depends on electrodes and an existing radar technology cannot recover complete ECG. According to the method, non-contact ECG signal monitoring is carried out in a daily scene, and good privacy and comfort are achieved.

The invention provides a cardiac autonomic neurodynamics quantitative evaluation method based on a phase ordering signal averaging technology. The cardiac autonomic neurodynamics quantitative evaluation method comprises an ECG signal preprocessing step, a QRS wave detection and processing step, a phase ordering signal averaging step and an autonomic neurodynamics quantitative analysis step. According to the cardiac autonomic neurodynamics quantitative evaluation method based on the phase ordering signal averaging technology, on the basis of inhibiting aperiodic interference and improving index stability in a complex physiological environment, continuous analysis of cardiac sympathetic nerve activity and cardiac parasympathetic nerve activity is carried out; time-varying CSI and CPI indexes are extracted to realize quantification of cardiac autonomic neurodynamics, and a lightweight algorithm framework is designed to adapt to real-time monitoring requirements of portable equipment.