452 results about "Electrocardiography" patented technology

The invention relates to the field of medical treatment, and discloses a cardiac surgery risk assessment system based on artificial intelligence. The system fuses electrocardiogram, image data and physiological data of wearable equipment, and data quality is ensured through a data preprocessing module; carrying out data dimensionality reduction and nonlinear mapping by using manifold learning and the method, and capturing a relationship between complex physiological signals; the sensor weight is optimized by adopting the maximum entropy principle, and the accuracy of the evaluation result is improved; performing space-time alignment of the multi-modal data based on the Lie group and Lie algebra theories; modeling the fused data by using a machine learning algorithm to generate a risk assessment result; the real-time feedback module dynamically adjusts the evaluation model and system parameters, and the output module displays an evaluation result through a visual interface to assist doctors in accurate decision making. The system improves the accuracy and real-time performance of assessment, and provides scientific and comprehensive risk assessment support for cardiac surgery.

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 relates to the technical field of cardiovascular medicine, in particular to an automatic alarm system for cardiovascular medicine, which comprises a biomarker analysis module, a physiological parameter analysis module, a cardiovascular risk assessment module, a cardiovascular early warning adjustment module and an alarm trigger notification module. According to the invention, by integrating real-time monitoring of blood biomarkers and cardiovascular physiological parameters, the early recognition capability of cardiovascular disease risks is improved, key biomarkers are dynamically measured and compared with historical marker data, and subtle physiological changes can be captured at the early stage of asymptomatic conditions, so that potential cardiovascular events are warned; through correlation analysis of electrocardiogram, blood pressure and pulse waveforms, the system can accurately draw a comprehensive image of cardiovascular functions, so that more targeted monitoring and intervention are implemented, timeliness and accuracy of medical response are ensured, doctors can formulate targeted monitoring schemes according to real-time data, and the monitoring efficiency is improved. Therefore, deterioration or occurrence of cardiovascular events of patients can be effectively prevented.

Systems and methods are provided for monitoring and processing physiological signals (e.g., electrocardiogram signals) to detect and predict for possible dysfunction of an anatomical feature (e.g., cardiac dysfunction) or otherwise predict a likelihood of future cardiac dysfunction of the individual. For example, a system comprises a plurality of sensors, a physiological signal processing system, and a feature analysis system. The sensors are configured to monitor physiological signals from an individual that has undergone a medical procedure on an anatomical feature. The physiological signal processing system is configured to analyze the physiological signals and extract features from the physiological signals which are indicative of a function of the anatomical feature. The feature analysis system is configured to analyze the extracted features and predict a risk of the individual developing a post-procedural dysfunction of the anatomical feature as a result of the medical procedure on the anatomical feature.

The invention relates to a chest pain classification method and system based on multi-modal data fusion and a deep learning model, and belongs to the technical field of intelligent medical auxiliary diagnosis. The method comprises the following steps: firstly, carrying out heart region segmentation and pathological feature enhancement preprocessing on a chest radiograph image, extracting anatomical features by adopting a multi-scale visual model which is optimized and improved aiming at the chest radiograph, meanwhile, carrying out multi-lead time sequence calibration and ST-segment waveform marking on an electrocardiogram signal, and capturing electrophysiological time sequence features through a multi-scale one-dimensional convolutional network; then, a dynamic gating hybrid expert system is constructed, an anatomy expert, an electrophysiology expert, a multi-modal association expert and a critical value expert process specific modal features respectively, and a gating network dynamically calculates expert weights based on real-time vital signs and pathological features; according to the method, by integrating multi-modal data and combining a deep learning technology, rapid and accurate chest pain classification support and report generation can be provided for clinicians, and the method has a wide application prospect.

Embodiments herein provide a system and method for screening and monitoring of cardiac diseases by analyzing acquired physiological signals. Unlike state of art approaches that consider only synchronized ECG and PPG signals for cardiac health analysis and do not consider PCG which is a critical signal for CAD analysis, the system synchronously captures physiological signals such as photo plethysmograph (PPG), phonocardiogram (PCG) and electrocardiogram (ECG) from subject(s) and builds an analytical model in the cloud for analyzing heart conditions from the captured physiological signals. The system and method provides a fusion based approach of combining the captured physiological signals such as PPG, PCG and ECG along with other details such as subject clinical information, demography information and so on. The analytical model is pretrained using ECG. PPG and PCG along with metadata associated with the subject such as demography and clinical information.

The invention relates to a data processing method and device for a multi-parameter health monitoring sensor, and the method comprises the following steps: carrying out the continuous recording of a target user, and obtaining a multi-channel monitoring data flow; carrying out heartbeat profile description on the multi-channel monitoring data stream based on a heartbeat feature reconstructor to obtain heart activity parameters; carrying out heartbeat rule analysis on the target user based on the heart activity parameters to obtain heart working characteristics; performing heart state evaluation on the heart working characteristics to obtain heart operation data; and if the heart running data is not within the preset heart running range, performing dynamic health tracking on the target user based on the heart running data to obtain a heart health report, thereby solving the problem that the traditional heart health monitoring means mainly depends on regular physical examination and static electrocardiogram detection, and the monitoring accuracy is high. Dynamic changes and individual differences of heart activities are difficult to capture, so that potential heart risks cannot be early warned and intervened in time.

A medical monitoring system comprises a wearable garment incorporating multiple physiological sensors for real-time patient monitoring. The garment integrates an embedded processing unit that receives sensor data and transmits it wirelessly to a base station or remote server. A multi-modal physiological signal processing unit analyses the data to generate composite physiological indices, including metabolic rate, work of breathing, and fall risk assessments. The garment includes optional monitoring features such as transcutaneous carbon dioxide measurement, electrocardiography, pulse oximetry, blood pressure monitoring, and accelerometer - gyroscope-based motion tracking. The system provides early-warning alerts for clinical deterioration, enabling proactive therapeutic interventions. A wireless communication module ensures real-time data transmission, supporting remote monitoring and integration with hospital networks. The garment's modular design allows for non-invasive physiological assessment while maintaining patient mobility, making it suitable for critical care, ambulatory monitoring, and telemedicine applications.

The invention provides a sudden cardiac death high-risk group multi-modal data intelligent analysis and prediction method and system, and relates to the technical field of medical health monitoring, and the method comprises the steps: collecting multi-modal data, projecting an electrocardiogram to a three-dimensional coordinate system, extracting potential trajectory features, and calculating a space-time discrete index; performing dynamic response analysis on the blood pressure data, and matching with a space-time discrete index to calculate a cardiovascular coupling coefficient; and performing segmented correction according to the motion state data, monitoring a coupling coefficient deviation condition in real time, calculating a risk degree and generating an early warning. According to the method, heart abnormity can be found in advance, and sudden cardiac death can be effectively prevented.

There is described an artificial intelligence wearable ECG skin patch (400) to detect sudden cardiac arrest. The wearable ECG monitoring patch (400) with AI based predictive analytics and remote based cardiac monitoring (615) system that can detect cardiac arrhythmias automatically in real-time and make a diagnosis with AI models trained with acquired data. The wearable skin has a biocompatible polymer patch (400) which captures the electrical signal through a flexible printed electronic technology based conducting ink and a substrate. The microcontroller controls (201), store and transmit the data packets. The IoT connected signal transmission is capable of recording and transferring the data packets through wireless communication. The AI engine is capable of analysing, evaluating, testing and providing the data packets of sudden cardiac arrest through a peak detector algorithm. The ECG skin patch (400) to detect and measure the sudden cardiac arrest with the R-R interval time series to obtain heart rate variability.

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

The invention discloses an electrocardiogram waveform real-time anomaly detection and early warning evaluation method and system, and the method comprises the following steps: S1, collecting the signal data of an electrocardiogram, and carrying out the preprocessing; s2, inputting the electrocardiogram segment sequence into an improved convolutional neural network, and performing feature extraction and aggregation; s3, inputting the fusion feature sequence into a graph attention neural network, and extracting a relation between waveform local dependence and fragment state transition; s4, inputting the global feature map into an anomaly evaluation network, and outputting an anomaly judgment result and a confidence score sequence; s5, generating a risk level tag sequence in combination with the individual parameters; s6, inputting the risk level label sequence into an early warning decision network, and generating a multi-level response label and an alarm trigger signal; and S7, controlling an early warning interface to execute alarm, and synchronously writing related data. According to the invention, real-time anomaly detection and multi-stage early warning response of the electrocardiogram waveform are realized, and the timeliness and reliability of abnormal heart rhythm recognition are improved.

The invention relates to the technical field of electrocardiogram analysis, in particular to an electrocardiogram analysis method and device.The method comprises the following steps that S1, comprehensive information collection is conducted, specifically, an electrocardiogram, heart rate variability and blood pressure of a patient are collected in real time through a comprehensive collection module, and preliminary data of the patient are obtained; s2, preliminary analysis and prediction: according to the preliminary data, a comprehensive analysis module compares the electrocardiogram data of the healthy person with the electrocardiogram of the patient, and analyzes the image data difference; the comprehensive analysis module preliminarily evaluates the heart health condition of the patient according to the image data difference; predicting a potential cardiac problem of the patient; s3, recording and learning ST-segment abnormal signals: establishing a deep learning module based on a convolutional neural network algorithm, recording and storing the electrocardiogram of the patient collected each time by using the deep learning module, and learning and judging ST-segment abnormal signals in different electrocardiograms; the system is complete in logic and comprehensive in function, and can effectively assist doctors in electrocardiogram analysis.

The invention provides an electrocardiograph electromagnetic compatibility test data diagnosis method based on an intelligent sensor, and relates to the technical field of electrocardiograph electromagnetic compatibility test. The method includes generating structured test data. And calculating a theoretical interference reference threshold. And generating an initial interference judgment atlas arranged according to the interference time sequence. And constructing a dynamic compensation coefficient matrix, and injecting the dynamic compensation coefficient matrix into the initial interference determination map to obtain a dynamic interference determination map. And calculating the characteristic similarity between the real-time test data in each interference time sequence window and the dynamic interference judgment map, and diagnosing the electromagnetic compatibility state of the electrocardiograph. According to the invention, through multi-dimensional data acquisition and interference identification, through a dynamic reference threshold and a compensation mechanism, environment and equipment state changes are adapted, the diagnosis stability of a complex scene is improved, through real-time data comparison and traceability, the equipment state is rapidly judged, the interference source is positioned, the debugging time is shortened, and finally the measurement accuracy of the electrocardiograph is guaranteed.

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 belongs to the technical field of remote health monitoring, medical diagnosis and intelligent health equipment, and discloses a non-contact heartbeat monitoring method and system based on millimeter wave signals. According to the non-contact heartbeat monitoring system, heartbeat monitoring is achieved by transmitting and receiving millimeter wave signals reflected from the chest of the human body through the millimeter wave radar technology. Tiny movement generated by cardiac pulsation can cause tiny changes of the chest of the human body, and the changes are shown through reflected millimeter wave signals. By processing and analyzing the reflected signals in real time, the influence of heartbeat on the signals can be accurately extracted, so that the heartbeat frequency and physiological indexes related to the electrocardiogram can be deduced.

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.

Embodiments of methods and systems for determining electrocardiogram (ECG) depth of electrical activity. Electrical activity from a plurality of electrodes of a catheter is received; and within the electrical activity, an electrical signal originating from a depth within cardiac tissue is identified. In some embodiments, spatial electrode signal analysis of the electrical activity may be performed for each electrode of the plurality of electrodes. In some embodiments, a linear and / or non-linear combination of signal components within the electrical activity may be calculated. In some embodiments, a neural network may be provided with the electrical signals and may determine an estimated distance to the nearest activation for at least one of the plurality of electrodes. A visualization of the identified electrical signals at a specified depth within the cardiac tissue may be provided for display.

A precise cardiac data reconstruction method is provided, which may also be referred to herein as radar cardiography (RCG). RCG can reconstruct cardiac data, such as heart rate and / or electrocardiogram (ECG)-like heartbeat waveform signals wirelessly by using advanced radar signal processing techniques. For example, heartbeat and related characteristics can be monitored by isolating cardiovascular activity from strong respiratory interference in spatial spaces: azimuth and elevation. This results in significant improvements to pulse signal-to-noise-ratio (SNR) compared to conventional approaches, facilitating heart-rate variability (HRV) analysis.

The invention provides a health pre-assessment method and system based on information monitoring, and the method comprises the steps: obtaining ECG data of a user, analyzing a target ECG waveform, extracting heart electrophysiological parameters which comprise a heart rate, a heart rate variability standard deviation and a heart rate rhythm, recognizing a heart rate feature mode of the user, and carrying out the health pre-assessment of the user according to the heart rate feature mode. The periodic change of the electrocardio effect is evaluated, and heart rate characteristic mode information is obtained; based on the heart rate characteristic mode information, multiple physiological parameters of the user are collected in real time, the mutual influence relation between the multiple physiological parameters and heart mode measurement values is analyzed, the heart regulation efficiency of the user is evaluated, and a physiological correlation analysis result is obtained in combination with the correlation coefficient. Through the method and the corresponding system, important risks such as health deterioration and subclinical abnormality can be found in advance, and continuous and decision support is provided for clinical decision and individualized health management.

The invention provides a cardiovascular and cerebrovascular disease risk prediction method and system, and relates to the technical field of data processing.The method comprises the steps that real-time physiological parameters of a target user are obtained, and the real-time physiological parameters comprise the ambulatory blood pressure fluctuation rate, the serum lipoprotein level, the heart rate variability frequency domain index and the sleep apnea hypopnea index; the method comprises the following steps: preprocessing real-time physiological parameters, extracting instantaneous waveform features of blood pressure signals by adopting wavelet transform, and extracting features related to health conditions, including blood pressure level, blood fat level, blood sugar level, electrocardiogram abnormal indexes and cardiac ultrasound abnormal indexes, to form a feature vector set; according to the method, the real-time physiological parameters are acquired, the feature vector set is formed through preprocessing, the krill swarm algorithm is used for optimization, the three-level fusion model is constructed, finally, the cardiovascular and cerebrovascular disease risk degree of the target user is accurately evaluated, early warning information is output, and the accuracy and timeliness of cardiovascular and cerebrovascular disease risk prediction are effectively improved.

The invention discloses a dynamic electrocardiogram waveform signal analysis method based on improved DTW and machine learning, and relates to the technical field of medical monitoring and human factor engineering. Firstly, denoising and baseline correction are carried out on an original signal through fast adaptive filtering and wavelet threshold denoising; then adopting an adaptive Sakoe-Chiba constrained improved DTW algorithm, dynamically planning and generating a minimum cumulative cost path through a limited alignment path bandwidth based on dynamic electrocardiogram signal physiological features, and calculating a normalized DTW distance; and finally, in combination with an XGBoost classifier and an SHAP interpretable framework, carrying out classification training by utilizing a DTW alignment distance and time-frequency domain features, and quantifying feature contribution through a Shapley value to generate a classification result. The method solves the problems of low efficiency of the traditional DTW and black box of the traditional model, has the capabilities of real-time processing and long-time data analysis, and provides efficient and reliable technical support for heart health monitoring.

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 a heart premature beat detection device and method, a ventricular assist system, a storage medium and equipment, and the detection device comprises a first sampling module which is configured to collect operation data of a drive motor in a ventricular assist device, and the operation data at least comprise rotation speed data; the calculation module is configured to determine a current rotating speed maximum value and a historical average maximum value according to the rotating speed data; the judgment module is configured to judge that premature beat occurs when the current rotating speed maximum value is larger than the gear rotating speed and smaller than the historical average maximum value. Compared with a traditional premature beat detection mode, premature beat detection can be carried out in real time in the operation process of the percutaneous ventricular auxiliary device, and traditional external monitoring equipment such as electrocardiogram or color Doppler ultrasound does not need to be relied on any more.

Systems, methods, and devices involve approaches for limiting the number of detected cardiac events for which a physician is alerted. Approaches involve detecting cardiac events based on electrocardiogram (ECG) data and associating the cardiac events with a criticality. ECG data associated with a subset of the cardiac events is transmitted to a remote computing system based, at least in part, on the criticality. An electronic notification is transmitted to a patient care group regarding at least one of the cardiac events in the subset. Approaches further include determining whether an acknowledgement of the electronic notification has been received by the server from the patient care group within a set period of time.

Method for reconstructing ECG signals through imaging for arrhythmia detection and its detection system, wherein the method for reconstructing ECG signals through imaging for arrhythmia detection involves receiving images of the human skin, which contain color changes on the skin surface caused by heartbeats and heart rhythms. The method comprises performing noise reduction on these color changes and analyzing the color variations to extract a remote photoplethysmographic (rPPG) signal that corresponds to the heart rates and heart rhythms. Frequency domain analysis is conducted to identify the frequency features within the rPPG signal, and feature extraction is performed on these frequency characteristics to obtain a photoplethysmographic (PPG) signal. The PPG signal undergoes feature extraction to derive time domain features and frequency domain features, which are then fused to output a reconstructed ECG signal. Finally, the waveform characteristics of the reconstructed ECG signal are interpreted, allowing for the identification of classifications corresponding to arrhythmias based on these waveform features.

A method for removing noise from a signal and an electronic device are provided. According to the method, by virtue of a continuous abrupt change performance of a wave peak having reference significance in an electrocardiography signal, whether a signal point is a point on a signal wave having reference significance can be identified during noise removal, and whether to remove noise from the signal point can be determined. The method is implemented, so that noise can be effectively removed from the electrocardiography signal on the premise that a reference value of the electrocardiography signal is guaranteed.

A cardiac rhythm condition recognition method based on a seismocardiogram signal comprises the steps that 1, the seismocardiogram signal and a gold standard electrocardiogram signal are collected, and filtering and standardization preprocessing are carried out on the seismocardiogram signal and the gold standard electrocardiogram signal; step 2, automatically labeling the heart rhythm condition based on the gold standard electrocardiogram signal: extracting at least one heart rhythm feature through an electrocardiogram analysis algorithm, and labeling the heart rhythm condition of the electrocardiogram signal based on a multi-evidence fusion labeling rule; step 3, mapping the labeling result of the electrocardiogram signal to the synchronous seismocardiogram signal: mapping the electrocardiogram labeling result to a corresponding time window of the synchronous seismocardiogram signal through a time alignment and priority mapping strategy, and endowing each seismocardiogram signal time window with a heart rhythm condition label; 4, constructing a deep learning model fusing a residual convolutional neural network and a long-short-term memory network, and training by using the marked seismocardiogram signal data; and 5, training model deployment and heart rhythm condition real-time identification.

The present disclosure relates to a method of reconstructing an arterial blood pressure (ABP) signal corresponding to the morphological feature of a combination signal of ECG and PPG on the basis of the morphological feature. The method of reconstructing an ABP signal according to an embodiment of the present disclosure includes: generating an ECG-PPG signal by combining ECG and PPG signals corresponding to an ABP signal; generating a plurality of ECG-PPG signals for learning by applying a plurality of noises, which is different in intensity, to the ECG-PPG signal; training a neural network model to receive the ECG-PPG signals for learning and output the ABP signal; and reconstructing an ABP signal of a target user by inputting an ECG-PPG signal of the target user into the neural network model.

A system for real-time prediction of heart disease and to support clinical decisions; the system includes: a patient data acquisition module configured to receive multimodal inputs, including physiological signals selected from electrocardiographic waveforms, blood pressure readings, heart rate readings, and oxygen saturation readings, as well as demographic and lifestyle information, including age, gender, cholesterol levels, smoking habits, and family medical history; a preprocessing engine configured to perform data cleansing, imputation of missing values, categorical coding, and feature scaling, so that the input data is normalized and converted into a format suitable for machine learning; a processing core comprising a multi-core CPU / GPU system-on-chip operationally coupled with a secure storage unit, wherein the processing core is configured to execute a variety of pre-trained machine learning models, including Naive Bayes, Random Forest, Logistic Regression and Decision Tree classifiers; a model evaluation unit configured to calculate validation metrics such as precision, recall, F1 score and area under the curve for each of the models and dynamically select the model with optimal performance to generate real-time predictions for the risk of heart disease; a display interface configured to present predictive results in the form of risk probability values, confidence indices, and actionable recommendations that are mapped to clinical treatment guidelines; and a communication interface configured to transmit emergency alerts based on high-risk predictions to remote caregivers, hospitals, and emergency response systems via wireless communication protocols such as WLAN, Bluetooth, and 4G / 5G cellular connections.