101 results about "Electrocardiogram analysis" patented technology

The invention relates to the technical field of health monitoring, and discloses a patient health monitoring method and system based on dynamic electrocardiogram analysis, and the method comprises the steps: synchronously obtaining multi-lead dynamic electrocardiogram data continuously collected by a patient within a preset duration, and related physiological parameters of exercise intensity, respiratory rate and body position change; performing dynamic self-adaptive preprocessing on the dynamic electrocardiogram data based on the associated physiological parameters to obtain standardized electrocardiosignals; extracting a multi-dimensional characteristic parameter set from the standardized electrocardiosignal, and sampling according to a preset time window to generate a characteristic parameter sequence with a timestamp; and inputting the characteristic parameter sequence into a dynamic optimization analysis model capable of iteratively updating parameters through real-time physiological parameter feedback, and performing graded evaluation on the health state of the patient to generate an evaluation result. The system corresponds to the method. By adopting the method and the system, the accuracy of dynamic electrocardiogram monitoring and the evaluation refinement level meet the dynamic monitoring requirement of cardiovascular health.

The invention provides an electrocardiogram data anomaly recognition method, device and equipment based on deep learning and a storage medium. An original electrocardiogram signal of a patient is collected, and a target electrocardiogram signal is obtained through multi-stage preprocessing. And identifying an abnormal signal in the target electrocardiogram signal by using a deep fusion learning model, and generating a preliminary identification result. And then, the preliminary recognition result is combined with a pre-trained machine learning model, mixed model processing is performed through a stack generalization fusion method, a Bayesian network is constructed based on the detailed information of the patient, a reasoning result with context sensing is obtained, and a target recognition result is finally generated. And generating an abnormal report according to the target identification result, and storing and displaying the abnormal report to the user in a classified manner according to a predefined standard. According to the method, high precision and personalized diagnosis of anomaly detection are ensured, and the reliability and efficiency of electrocardiogram analysis are improved.

Artificial intelligence enabled disease profiling is described. An electrocardiogram analysis module is configured to derive disease vectors for a plurality of diseases using electrocardiogram training data from both disease-negative and disease-positive individuals. A standardized input is generated, via a data preprocessor of the electrocardiogram analysis module, from an electrocardiogram recorded from an individual. The standardized input is encoded, by a deep learning autoencoder of the electrocardiogram analysis module, into an embedding, the embedding being a lower-dimensional latent space representation of features extracted from the standardized input. At least one disease risk score for the individual is generated, by a statistical modeling algorithm of the electrocardiogram analysis module, for the plurality of diseases based on the embedding and the disease vectors.

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 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.

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 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 a multi-task electrocardiogram analysis method and system based on deep learning and a medium, and the method comprises the following steps: S1, receiving to-be-analyzed electrocardiogram data sent by a client, and preprocessing the to-be-analyzed electrocardiogram data to obtain preprocessed to-be-analyzed electrocardiogram data; s2, performing arrhythmia recognition processing on the preprocessed to-be-analyzed electrocardiogram data by using the trained multi-task electrocardiogram detection model to obtain a recognition result; and S3, transmitting an identification result to the terminal equipment. According to the method, a bimodal fusion module fusion method based on attention mechanism guidance is adopted, information fusion among different tasks is achieved, a saliency map of diagnosis information is highlighted, the problem that electrocardiosignal diagnosis is poor in interpretability is effectively solved, meanwhile, an auxiliary task based on feature point positioning is adopted, and the diagnosis accuracy is improved. The problems in the aspects of stability and robustness of the model are solved, and the effect of reducing the sensitivity to noise and data distribution change is achieved.

The invention discloses a paper electrocardiogram voltage value reconstruction method and system based on a dynamic diffusion threshold value, and belongs to the technical field of electrocardiogram analysis. Comprising the following steps: acquiring a standard paper electrocardiogram and a to-be-processed paper electrocardiogram, and extracting paper electrocardiogram parameters; based on a preset threshold value, carrying out iterative optimization to obtain an optimal threshold value so as to carry out binarization processing on the to-be-processed paper electrocardiogram to obtain a binarized paper electrocardiogram; the positioning of the electrocardiogram waveform lead position is realized; the electrocardio waveform information of the lead image area is converted into a digital signal; converting the obtained digital signal into a voltage signal; and resampling the voltage signal to obtain a digitalized reconstructed electrocardiosignal. Compared with the prior art, the method has the advantages that the image processing threshold value can be dynamically adjusted according to the local features of the paper electrocardiogram, so that the edge of the electrocardiogram waveform in the digitization process is clearer, and it is ensured that the digitization data can accurately reflect the original information of the paper electrocardiogram.

In one embodiment of the present disclosure, a method for evaluating the quality of multiple electrocardiogram data and diagnosing diseases based only on good quality signals is disclosed. Specifically, according to the present disclosure, a computing device evaluates the quality of multiple electrocardiogram data obtained from multiple leads, and based on the evaluation of the quality of the multiple electrocardiogram data, excludes the electrocardiogram data obtained from at least one lead among the multiple leads, and performs electrocardiogram analysis based on the remaining multiple electrocardiogram data using a pre-trained electrocardiogram analysis model.

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

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.

A system for interactive ECG monitoring is described. The system includes a data repository storing pre-processed ECG data. The pre-processed ECG data is associated with historical data, real-time data, or both derived from a plurality of ECG recorders. The pre-processed ECG data includes ECG measurements extracted or derived from raw ECG signals and annotations of cardiac events. Further, the system includes a multi-agent query processor to receive and process an input message related to health of a subject, retrieve relevant data elements from the pre-processed ECG data, raw ECG signals, or both based on the processed input message, compute metrics corresponding to the input message based on the retrieved data elements, and generate a response to the input message using an LLM or at least one agent to integrate retrieved data elements and computed metrics. The response is presented on a user interface to a healthcare provider.

The present invention relates to the technical field of dynamic electrocardiogram analysis, specifically a dynamic electrocardiogram data processing method and system, comprising the following steps: segmenting the acquired dynamic electrocardiogram data, adjusting the signal strength of each segment, calculating the energy distribution of each segment, and judging its stability, and generating stability-adjusted electrocardiogram data segments. The present invention, by extracting bioinformatics features based on deep learning for electrocardiogram signals, can identify complex patterns and features in electrocardiogram signals in the early detection of abnormal heart rhythms, thereby improving the accuracy and sensitivity of abnormal heart rhythm identification. It provides strong support for the accurate classification of abnormal heart rhythms. The fractal dimension theory is used to conduct an in-depth analysis of dynamic electrocardiogram data, revealing the nonlinear characteristics and self-similarity of electrocardiogram signals. The theoretical depth of electrocardiogram analysis is enhanced.

The invention discloses a wearable real-time nursing monitoring device for physiological signs. The wearable real-time nursing monitoring device comprises an electrocardio garment body, a sensor integration module, a micro-processing module, an electrode slice and a heating module, the electrocardio garment body is made of a flexible fabric material, has a shape fitting the curve of a human body and is used for being worn on the trunk of the human body; the sensor integration module is embedded in a fabric of the electrocardiograph garment body, and the sensor integration module comprises an electrode sensor, a heart rate sensor, a respiration sensor, a temperature sensor, a motion sensor, an optical sensor and an electrocardiogram analysis module; the intelligent electrocardiograph garment has the advantages that the electrocardiograph sensor, the heart rate sensor, the breathing sensor, the temperature sensor, the motion sensor and the optical sensor are embedded into the electrocardiograph garment in a fabric integration mode, multiple key vital signs can be collected synchronously for a long time in a non-sensitive mode in the natural living state of a user, and the flexible electrodes are matched with the special electrocardiograph analysis module, so that the user experience is improved. And the continuous and dynamic electrocardiogram monitoring capability is realized in a wearable clothing form.

Electrocardiogram-based deep learning for cardiac prediction is described. An electrocardiogram analysis module may include a data preprocessor configured to normalize an electrocardiogram to generate a standardized input for an electrocardiogram-based cardiac prediction. The electrocardiogram analysis module may further include a deep learning model including a neural network and at least one dense layer, the deep learning model trained to identify features associated with a cardiac condition that reflect underlying cardiac changes that result from or predispose development of the cardiac condition, and generate a cardiac prediction based on the identified features.

This assistance system comprises an electrocardiograph, an electrocardiogram analysis apparatus, and an assistance apparatus. The electrocardiograph is configured to acquire an electrocardiogram of a subject. The electrocardiogram analysis apparatus is configured to receive the electrocardiogram, divide the electrocardiogram into a plurality of sections, and extract sections other than a specific section from the plurality of sections as candidate sections. The assistance apparatus is configured to communicate with and connect to the electrocardiogram analysis apparatus. The assistance apparatus comprises a computing device and a display apparatus that is controlled by the computing device. The functions of the computing device are described in detail in a specification.

The support system includes an electrocardiograph, an electrocardiogram analyzer, and a support device. The electrocardiograph is configured to acquire an electrocardiogram of a subject. The electrocardiogram analyzer is configured to receive the electrocardiogram, to divide the electrocardiogram into a plurality of sections, and to extract the sections other than specified sections from the plurality of sections as candidate sections. The support device is configured to be communicatively connected to the electrocardiogram analyzer. The support device includes a computing device and a display device controlled by the computing device. The functions of the computing device are described in the specification in detail.

The present invention provides a method for batch analysis of missing QRS waves in dynamic electrocardiogram, which can assist clinicians in batch finding the missing QRS waves and simultaneously batch modifying and compensating for the missing QRS waves, greatly improving the efficiency of electrocardiogram analysis. By constructing a superimposed QRS waveform diagram and a bar chart of the number of QRS wave markers, and comparing the total number of heartbeats M at the QRS peak waveform in the superimposed QRS waveform diagram with the number of QRS wave markers N, all regions where QRS markers are missing can be found in batch, without the need for clinical staff to manually search item by item, greatly improving the efficiency of electrocardiogram analysis. At the same time, the present invention also discloses a system for batch analysis of missing QRS waves in dynamic electrocardiogram.

According to one embodiment of the present disclosure, there are disclosed a method, program and apparatus for providing a user interface for electrocardiogram analysis. The method may comprise: obtaining bio-data of an electrocardiogram reading target; and displaying a first control graphic configured to switch a visual representation and state according to the result of the electrocardiogram analysis performed based on the obtained bio-data in the first area of the user interface. Furthermore, the first control graphic may be constructed for each type of disease or electrocardiogram feature that can be read from an electrocardiogram.

This design is a graphical user interface for electrocardiogram analysis, characterized by the combination of the shape and form of the display screen on which the graphical user interface for electrocardiogram analysis is displayed. The dotted lines in this design do not form part of the design sought to be registered. Figure 5.2 is a reference view and does not form part of the design sought to be registered. 5.1) Front view; 5.2) Reference view

The invention relates to an ICU patient delirium risk early screening system based on AI electrocardiogram analysis. The system comprises a neural signal extraction module, a component mode determination module, a delirium type identification module, a risk trajectory prediction module and a risk alarm generation module, wherein the neural signal extraction module extracts neuromodulation related signal fragments based on an electrocardiogram signal sequence and a preset neuromodulation specificity index; a component mode determination module extracts time sequence features from the fragments and determines an abnormal component distribution mode; the delirium type identification module is used for matching potential delirium types through the feature database; the risk trajectory prediction module predicts a risk trajectory in combination with a long-short-term memory network; a risk alert generation module evaluates a risk level and generates an alert. With the adoption of the system, early and dynamic screening of delirium risks can be realized, the screening accuracy and timeliness are improved through multi-module collaborative analysis, and accurate monitoring support is provided for ICU (Intensive Care Unit) patients.

The invention relates to the technical field of electrocardiogram analysis, and discloses an unsupervised partial adversarial adaptive method and system for different label spaces, and the method comprises the steps: constructing a deep learning diagnosis model, a feature extractor, a feature classifier and a full-connection feature processor; constructing an entropy minimization loss function according to the source domain classification loss; a selection factor is obtained through calculation, and the weight of source domain data in diagnosis model training is adjusted according to the selection factor; and constructing a final loss function in the diagnosis model training process, and optimizing the feature extractor, the feature classifier and the full-connection feature processor by minimizing the final loss function. Through the above method, the unsupervised partial adversarial adaptive system for the different label spaces is realized, adversarial training is performed on the model by introducing the EL loss function, the selection factor and the final loss function, the cross-domain migration performance of the deep learning model can be effectively improved, and the diagnosis performance of the model in the different label spaces is ensured.

This design is an analysis area selection screen for a graphical user interface for electrocardiogram analysis, characterized by the combination of shape and form of the display screen on which the graphical user interface for electrocardiogram analysis is displayed. The dotted lines in this design do not form part of the design sought to be registered. Figure 4.2 is a reference view and does not form part of the design sought to be registered. 4.1) Front view; 4.2) Reference view

According to one embodiment of the present disclosure, there are disclosed a method, program and apparatus for providing a user interface for electrocardiogram analysis. The method may comprise: obtaining bio-data of an electrocardiogram reading target; and displaying a first control graphic configured to switch a visual representation and state according to the result of the electrocardiogram analysis performed based on the obtained bio-data in the first area of the user interface. Furthermore, the first control graphic may be constructed for each type of disease or electrocardiogram feature that can be read from an electrocardiogram.

When statistical values ​​calculated from a partial measurement section of electrocardiogram data are presented to an analyst, it is possible to prevent abnormalities in electrocardiogram data from being overlooked. [Solution] The electrocardiogram analysis device 3 has a first calculation unit 335 that calculates the difference between at least two feature points related to the position of a predetermined wave in the electrocardiogram data; a second calculation unit 336 that calculates a first statistical value of one or more differences in a specific section in the electrocardiogram data and a second statistical value of the difference different from the first statistical value; an identification unit 334 that identifies a second section in the electrocardiogram data for which the second statistical value satisfies a predetermined judgment condition, provided that the second statistical value in the first section in the electrocardiogram data does not satisfy a predetermined judgment condition, and that is different from the first section that is within a predetermined range based on the first section; and an output unit 339 that associates the first statistical value calculated for at least one of the first and second sections and outputs the calculated first statistical value for that section.

This design is a graphical user interface for electrocardiogram analysis, characterized by the combination of the shape and form of the display screen on which the graphical user interface for electrocardiogram analysis is displayed. The dotted lines in this design do not form part of the design sought to be registered. Figure 8.2 is a reference view and does not form part of the design sought to be registered. 8.1) Front view; 8.2) Reference view

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

To provide an electrocardiogram analysis system capable of determining the need for an electric shock to a patient undergoing cardiopulmonary resuscitation (CPR) with a higher accuracy. An electrocardiogram analysis system includes electrocardiogram (ECG) signal acquiring means 11, ECG signal sampling means 12, ECG spectrogram transforming means 13, impedance signal acquiring means 21, impedance signal sampling means 22, impedance spectrogram transforming means 23, a convolutional neural network (CNN) 4 including an input layer 4I, an output layer 4O, sample data accumulation means 4L, and sample data input means 4T, and electric shock indication reporting means 5. The CNN is a priori provided with sample data including sample ECG spectrograms and sample impedance spectrograms obtained from a large number of subjects, and sample response data on the need for an electric shock, and is optimized by self-learning the sample data.

To better support the rescuer's movements during cardiopulmonary resuscitation. [Solution] In the AED1, the detection unit 134 detects the patient's body movement during the CPR period after electrocardiogram analysis and discharge processing. The output control unit 131 outputs instructions to the rescuer regarding chest compressions based on whether or not the patient's body movement is present during the CPR period. The output control unit 131 also outputs a continuation instruction D1 to instruct the continuation of chest compressions, and outputs the continuation instruction D1 again when a first time has elapsed since the previous output of the continuation instruction D1. Furthermore, if the patient's body movement is not detected for a period of time shorter than the first time, the output control unit 131 outputs a start instruction D2 to instruct the start of chest compressions with priority over the continuation instruction D1, and outputs the start instruction D2 again when the second time has elapsed.