1152 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.

It is disclosed new conducting and stretchable hydrogels and “one-pot” process to making them from natural and eco-friendly components, including gelatin, chitosan, and glycerol. Various conducting materials, such as poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS), thermally reduced graphene (TRG), or MXene are introduced in the hydrogels to enhance their conductivity. The resulting conducting hydrogels exhibit remarkable robustness, do not require crosslinking agent, and possess a unique thermo-reversible property, simplifying the fabrication process and ensuring enhanced long-term stability. Moreover, their fabrication is sustainable, employing environmental-friendly materials and processes, while retaining their skin-friendly characteristics. It is also disclosed hydrogel electrodes that were tested for ECG signal acquisition and outperformed the commercial electrodes. The hydrogel-based electrodes deliver high-quality ECG signals, boasting a superior signal-to-noise ratio (SNR) and remarkable resilience against interference and motion artifacts, compared to their commercial AgCl counterparts.

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 a multi-module collaborative rapid electrocardiogram automatic diagnosis method and system, and belongs to the technical field of automatic diagnosis. The method comprises the following steps: preprocessing an original ECG signal; the preprocessed ECG signals serve as input, and after the preprocessed ECG signals are sequentially processed by a lightweight convolution sub-module, a deformation convolution sub-module and a non-local attention sub-module, multi-level features containing local details and global time sequence dependency are output; fusing the multi-level features, and sending the fused multi-level features into a classifier to complete prediction of heart rhythm types to obtain prediction probabilities of all categories; a sliding window mode is adopted, the ECG signal fragments with the fixed length are sent to the diagnosis model for reasoning, the diagnosis model continuously outputs diagnosis results, and alarm or diagnosis prompt information is output in time according to the diagnosis results. According to the method, by designing a lightweight network structure, the parameter quantity and calculation complexity of a diagnosis model are effectively reduced, and real-time online ECG signal automatic diagnosis is realized on the premise of ensuring high diagnosis accuracy.

The invention provides a depression state assessment method and device based on heart rate variability characteristics and a parallel neural network. Real-time electrocardiosignals are collected in real time through wearable equipment and transmitted to a mobile terminal, the mobile terminal transmits the electrocardiosignals to a cloud server, and work of signal preprocessing, heart rate variability (HRV) feature extraction, feature selection and data enhancement and depth model construction and optimization is carried out on the cloud server. And evaluating the depression state of the subject. The cloud outputs an evaluation result to a display screen of the mobile terminal to be displayed, the depression state of the subject is finally displayed, and the four evaluated depression states are healthy, mild depression, moderate depression and severe depression; in addition, the terminal also supports depression state historical record query and key HRV feature tracing.

The invention discloses an electrocardiosignal prediction method, system and device for an acute heart failure patient and a medium, and the method comprises the steps: obtaining a historical electrocardiosignal of the acute heart failure patient, generating a multi-modal signal comprising a feature enhanced electrocardiosignal, a dynamic feature and a power spectrum density, and constructing an acute heart failure sample data set; obtaining a pre-trained ECG signal prediction model, and training the model by using the acute heart failure sample data set based on transfer learning to obtain an acute heart failure ECG signal prediction model; the method comprises the following steps: acquiring an electrocardiosignal of a patient with acute heart failure in real time and preprocessing the electrocardiosignal to obtain a real-time multi-mode signal, and taking the real-time multi-mode signal as an input to predict an ECG signal within a future preset duration; by extracting a multi-modal signal as model input, the prediction precision and robustness of the ECG signal are remarkably improved; based on transfer learning, efficient and accurate ECG signal prediction is realized in an acute heart failure scene.

The invention belongs to the technical field of intelligent rehabilitation equipment control, and relates to a multi-parameter feedback regulation and control method for postoperative lung function rehabilitation training, which comprises the following steps: acquiring an electrocardiosignal, a respiration signal and a limb movement signal of a patient; identifying a heart rate fluctuation form of the electrocardiosignal to generate an abnormal fluctuation time period, and analyzing a time difference between an inspiration phase and an expiration phase of the respiratory signal to generate a starting point of respiratory rhythm disorder; judging a cardiopulmonary risk level; the exercise intensity of rehabilitation training equipment is automatically adjusted based on the cardiopulmonary risk level, and meanwhile a regulation and control mode is dynamically selected according to the proportion change of the chest fluctuation amplitude and the abdomen fluctuation amplitude; when the heart rate recovery rate is lower than a preset reference and the respiratory rhythm stabilization time exceeds a critical value, gradually improving the exercise intensity; calculating a motion intensity correction coefficient and triggering a motion posture correction instruction; and generating a rating report containing the cardiopulmonary function fitness. According to the invention, the problem of lack of comprehensive quantitative evaluation of rehabilitation efficiency in the prior art is solved.

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 discloses a non-contact electrocardio detection method which comprises the following steps: transmitting a frequency modulation continuous wave signal to the heart part of a human body by using an MIMO millimeter wave radar so as to obtain an echo signal reflected by the human body and pre-process the echo signal to obtain a discrete three-dimensional radar signal; enhancing the discrete three-dimensional radar signal by using a 2D beam forming technology to obtain a 2D signal; extracting a target area signal from the 2D signal, and performing phase extraction on the extracted target area signal to obtain phase data; the method comprises the following steps: constructing a WaveGRU-Net network comprising an MODWT module, a CNN module and a Bi-GRU module; and training the WaveGRU-Net network by using the phase data so as to output a reconstructed electrocardiogram by using the trained WaveGRU-Net network, thereby realizing non-contact electrocardiogram detection. According to the method, accurate extraction of the electrocardiosignal is realized, and meanwhile, the signal reconstruction precision is improved.

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 depression assessment system based on resting heart and brain coupling analysis and an implementation method. The system performs depression scale score prediction through a depression symptom assessment model; the implementation method comprises the steps that synchronous multi-lead electroencephalogram signals and electrocardiosignals with the same time duration are collected for a plurality of users; the collected electroencephalogram signals and electrocardiosignals are preprocessed; feature sequence extraction is carried out on the preprocessed electroencephalogram signals and electrocardiosignals, and traditional heart rate variability features are extracted; calculating causal nonlinear coupling strength between each frequency band feature sequence and the RR interval feature sequence of the electroencephalogram to obtain a heart and brain coupling feature set; based on the heart rate variability feature set and the heart and brain coupling feature set, establishing a depressive symptom evaluation model; according to the depression feature set obtained through calculation, the depression symptom evaluation model outputs the quantitative score of the depression degree. The method can avoid the limitation that a traditional scale depends on subjective evaluation, and can be used for early screening of depression.

The invention discloses a medical device and rehabilitation software information system based on the Internet of Things, and particularly relates to the technical field of communication transmission management, which constructs event-anchored linear alignment on an edge side, takes a real-time channel quality vector as input, and generates a compression strategy through a Pareto multi-arm bandit, so that the compression strategy is compressed, and the communication transmission efficiency is improved. Meanwhile, a dynamic compression offset index is used as a safety gate, so that a self-adaptive compression decision considering distortion and time delay is realized, and the technical problem that the time sequence consistency and the diagnosis integrity of the multi-modal physiological signal are difficult to guarantee simultaneously under the conditions of clock drift and bandwidth limitation is solved; a cross-modal alignment quality monitoring module is arranged and used for recognizing and restraining cross-modal dislocation false correlation in real time, so that it is ensured that rehabilitation evaluation and short-period diagnosis are established on a reliable time sequence corresponding relation, and the dislocation problem existing in electrocardiosignals and action signals is solved.

The invention discloses a rapid identification method applied to defibrillation requirements of an AED device, and belongs to the technical field of medical equipment. According to the method, multiple links including acquisition, preprocessing, feature extraction, mode classification, real-time classification and defibrillation triggering work cooperatively, and advanced signal classification and mode recognition technologies are introduced, so that efficient and accurate electrocardiosignal analysis is realized in the AED device, the response speed and accuracy in the emergency treatment process are remarkably improved, and the emergency treatment efficiency is improved. Multi-dimensional features of electrocardiosignals are extracted and input into a pattern classification model, automatic recognition of arrhythmia is achieved, whether defibrillation operation needs to be carried out or not can be accurately judged, the possibility of misjudgment is reduced, self-optimization can be carried out according to new electrocardiosignal data through a dynamic learning and retraining mechanism, and the accuracy of defibrillation is improved. The adaptive capacity to a complex heart rhythm mode is improved, the first-aid response time is effectively shortened, the safety of a patient is guaranteed, and the first-aid efficiency and the treatment effect are 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 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 discloses an electrocardiogram analysis method and device based on a deep learning model and a medium, and relates to the field of deep learning, and the method comprises the steps: carrying out the preprocessing of an electrocardiogram, and carrying out the noise suppression; inputting the electrocardiosignals into a pre-trained deep learning model, extracting time features and spatial features, and performing cross-modal feature fusion; synchronously executing a plurality of anomaly detection tasks, and synchronously executing a time sequence prediction task; aiming at the abnormal detection result, correcting the abnormal detection result according to the clinical information and the time sequence prediction result; and outputting an analysis result corresponding to the electrocardiogram. A complete closed loop is formed from signal processing to feature extraction, multi-task analysis and result correction, manual intervention links are reduced, electrocardiogram analysis time is remarkably shortened, result consistency is guaranteed, end-to-end automation is achieved, and efficiency is improved.

The invention discloses a multi-label arrhythmia detection method based on multi-scale space-time dynamic graph convolution, and the method comprises the steps: capturing the space-time feature information of an ECG signal through series gating time convolution and dynamic graph convolution; a feature fusion module is provided, the single-scale representation capability is enhanced through statistical feature assisted global-local feature fusion, redundant information is successfully removed through orthogonal gating multi-scale fusion, and a gating mechanism dynamically adjusts the importance of each scale feature in the feature screening process, so that the accuracy of the feature screening is improved. The sending end and the receiving end are added to further filter information, so that the risks of multi-scale feature overfitting and information loss are effectively avoided, the robustness and accuracy of the model are improved, and the situation that the most valuable information is not fully reserved during multi-scale spatial-temporal feature fusion due to the fact that redundant features are difficult to effectively distinguish in a traditional method is avoided. The method not only improves the accuracy and stability of the model, but also has high practicability, and can effectively support automatic diagnosis of arrhythmia.

The invention discloses a dual-channel non-contact electrocardiogram gating system for cardiovascular magnetic resonance imaging, which combines optical and radar technologies, is not in contact with the body of an examinee, and overcomes the defects of more signal interference, weak acquisition, fast attenuation and the like in the magnetic resonance examination process in a dual-channel mode. In a magnetic resonance environment, displacement is measured through the out-of-focus speckle technology, the real-time respiration monitoring error is low, and the consistency of millimeter wave radar micro-motion electrocardio respiration signal real-time monitoring data and the ECG electrocardio signals is high.

The invention relates to the technical field of physiological signal processing, in particular to an electroencephalogram and electrocardio combined feature de-entanglement emotion decoding system which comprises a feature de-entanglement module, a three-way dynamic interaction module, a feature semantic mapping module, an abnormal feature verification module and a feature restoration module. According to the method, the electroencephalogram signals and the electrocardiosignals are subjected to multi-stage decomposition to recognize and distinguish public features and invalid private features, synchronous analysis of the signal dimension and the semantic dimension is achieved, feature items inconsistent with task targets are dynamically detected through statistical analysis of feature semantic deviation, stability and abnormal distribution conditions, and the accuracy of the task targets is improved. The semantic state evolution trend of the multi-modal physiological signals is defined, semantic quality control on the feature level is promoted, feature replacement and modeling updating are achieved, and the expression of the multi-modal physiological signals in an emotion recognition task has sensitivity, accuracy and adaptability; and a physiological feature expression system which is clear in structure, definite in semantics and dynamically adjustable is provided for emotional state recognition.

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 relates to the technical field of electrocardiogram detection, in particular to an electrocardiogram atrial fibrillation prediction method based on multi-modal deep learning, which comprises the following steps: S1, collecting electrocardiogram signal data, S2, performing signal preprocessing and feature fusion, and S3, performing model training and atrial fibrillation prediction. According to the method, multi-modal features are extracted through a complex multi-scale attention mechanism and a feature self-adaptive attention mechanism, a deep learning model based on the combination of a convolutional neural network and a long-short term memory network is combined, multi-dimensional features and time sequence information of electrocardiosignals are fully utilized, and the multi-modal features of the electrocardiosignals are extracted. Therefore, the efficiency, the accuracy and the reliability of the atrial fibrillation prediction method are greatly improved, and the problem that the sensitivity and the specificity cannot meet the requirements when the prior art faces complex and diversified electrocardiosignals is solved.

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 discloses a heart sound and electrocardio acquisition and analysis system, which relates to the technical field of biomedical engineering, and comprises a time-frequency analysis module for acquiring a heart sound and electrocardio original data set through an electrocardio and heart sound lead suction ball and an electrocardio and heart sound simulation complete machine and transmitting the data set to an upper computer to execute wavelet decomposition and short-time Fourier transform, the system comprises a heart sound time-frequency spectrum matrix and an electrocardio time-frequency energy matrix output module, a chaotic feature extraction module, the heart sound time-frequency spectrum matrix and the electrocardio time-frequency energy matrix are subjected to phase-space reconstruction, a high-dimensional dynamic track is formed, an improved wolf algorithm is applied to conduct dynamic index calculation on the high-dimensional dynamic track, and a dynamic parameter set is obtained. According to the method, through the improved wolf algorithm and the constructed heart sound space propagation model, the multi-modal fusion capability and analysis discrimination between the heart sound and the electrocardiosignal are improved, and the intelligent level of heart sound and electrocardiosignal collection and analysis is also improved.

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.

A catheter includes: (a) a shaft for insertion into a heart of a patient, (b) an expandable distal-end assembly, which is coupled to the shaft and is configured to make contact with tissue of the heart, (c) at least first and second electrocardiogram (ECG) electrodes, which are coupled to an outer surface of the expandable distal-end assembly, and when placed in contact with the tissue, are configured to sense ECG signals in the tissue, and (d) a reference electrode, which is positioned within an inner volume of the distal-end assembly, and in an expanded position of the distal-end assembly, the reference electrode: (i) has no physical contact with the tissue, and (ii) is positioned at a first distance from the first ECG electrode and at a second distance from the second ECG electrode, and the difference between the first and second distances is smaller than a predefined threshold.