50 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.

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 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.

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.

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.

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

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

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.

According to an embodiment of the present disclosure, disclosed is a method for evaluating qualities of electrocardiogram data and diagnosing a disease only with a signal having an excellent quality. Specifically, according to the present disclosure, a computing device evaluates qualities of electrocardiogram data obtained from a plurality of leads, excludes electrocardiogram data obtained from at least one lead among the plurality of leads based on the evaluation of the qualities of the electrocardiogram data, and performs electrocardiogram analysis based on remaining electrocardiogram data by using a pre-learned electrocardiogram analysis model.

The application provides a rare disease occurrence probability prediction method and device and an electrocardiogram analysis system. The method comprises the following steps: acquiring a plurality of to-be-evaluated electrocardiogram data and corresponding to-be-evaluated medical record text data; inputting the to-be-evaluated electrocardiogram data into an electrocardiogram pre-training model to obtain a target electrocardiogram feature vector; inputting the medical record text data corresponding to the to-be-evaluated electrocardiogram data into a medical text pre-training model to obtain a target text feature vector; inputting the to-be-evaluated electrocardiogram data into a preset risk detection model to obtain an electrocardiogram rare disease occurrence probability coefficient; presetting a rare disease feature dictionary; determining the occurrence probability of each rare disease according to the similarity between the target electrocardiogram feature vector and the reference text feature vector of each rare disease, the similarity between the target text feature vector and the reference text feature vector of each rare disease, and the electrocardiogram rare disease occurrence probability coefficient. The occurrence probability of the rare disease can be predicted without training a large number of electrocardiogram samples of the rare disease.

An electrocardiogram analysis apparatus includes a machine learning part that has a machine learning model realized by machine learning that uses training electrocardiogram data of a patient with paroxysmal arrhythmia during a non-paroxysmal period during which no episode of paroxysmal arrhythmia occurs; an input processing part that inputs electrocardiogram data of a person to be analyzed, which is a subject of analysis, into the machine learning model; and an output control part that outputs, to an information terminal, abnormality information which is to be output from the machine learning model and is about whether the person to be analyzed has paroxysmal arrhythmia.

Electrophysiological activity can be mapped using an electroanatomical mapping system. Using electrophysiological data from a clique of at least four non-coplanar electrodes, the mapping system derives a three-dimensional vectorcardiogram for the clique; analyzes a shape of the vectorcardiogram; identifies first and second omnipolar electrograms for the clique; defines an activation direction for the clique; and computes a conduction velocity magnitude for the clique, thereby determining a cardiac activation vector at the cardiac location. The cardiac location can be classified as pathological when the orientations of the first and second omnipolar electrograms differ by more than a threshold amount and / or when the shape of the three-dimensional vectorcardiogram satisfies at least one of a non-planarity criterion and a directional criterion. Various graphical representations of the foregoing analyses are contemplated.

An automated external defibrillator for performing electrocardiogram analysis and discharge processing on a subject. The automated external defibrillator includes a detection unit configured to detect a body motion of the subject, in a cardiopulmonary resuscitation period after the electrocardiogram analysis and the discharge processing, and an output controller configured to output, to a rescuer, an instruction regarding chest compression on the subject, based on presence or absence of the body motion of the subject, in the cardiopulmonary resuscitation period. The output controller is configured to output a continuation instruction for instructing continuation of chest compression, and in a case where a first time has elapsed from the output of a previous continuation instruction, output the continuation instruction again.

Electrocardiogram-based deep learning for hypertension prediction is described. An electrocardiogram analysis module may include a data preprocessor configured to normalize an electrocardiogram to generate a standardized input for electrocardiogram-based hypertension prediction. The electrocardiogram analysis module may further include a deep learning model including a neural network trained to identify features associated with hypertension from the standardized input and at least one dense layer trained to generate a hypertension risk prediction based on the identified features. The hypertension risk prediction may comprise a probability score indicating a likelihood of hypertension.

Electrophysiological activity can be mapped using an electroanatomical mapping system. Using electrophysiological data from a clique of at least four non-coplanar electrodes, the mapping system derives a three-dimensional vectorcardiogram for the clique; analyzes a shape of the vectorcardiogram; identifies first and second omnipolar electrograms for the clique; defines an activation direction for the clique; and computes a conduction velocity magnitude for the clique, thereby determining a cardiac activation vector at the cardiac location. The cardiac location can be classified as pathological when the orientations of the first and second omnipolar electrograms differ by more than a threshold amount and / or when the shape of the three-dimensional vectorcardiogram satisfies at least one of a non-planarity criterion and a directional criterion. Various graphical representations of the foregoing analyses are contemplated.

This design is an initial screen of a graphical user interface for electrocardiogram analysis, characterized by the combination of shape and form of a 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 1.2 is a reference view and does not form part of the design sought to be registered. 1.1) Front view; 1.2) Reference view

The invention discloses a dynamic electrocardiogram analysis method and system combined with noninvasive cardiac output monitoring, and relates to the technical field of medical monitoring, and the method comprises the following steps: continuous electrocardiogram monitoring: a subject wears a dynamic electrocardiogram recording module, and electrocardiogram signals with set duration are continuously collected and stored; non-invasive cardiac output monitoring is triggered through rhythm event characteristics or manual operation, in the continuous electrocardiogram monitoring process, a non-invasive cardiac output monitoring module is started as needed, and hemodynamic parameters in the corresponding time period are collected and stored; cloud analysis and conjoint analysis are conducted, a cloud service module conducts correlation analysis on the electrocardiosignals and the hemodynamic parameters, if the electrocardiosignals and the hemodynamic parameters are abnormal, it is judged that a high-risk composite event exists, and the electrocardiosignals and the corresponding hemodynamic parameters of the high-risk composite event in the corresponding time period or the manual triggering moment are obtained and presented through a conjoint analysis module. And effective combination of electrocardiogram monitoring and hemodynamic parameter monitoring is realized.

This design is an analysis results screen for a graphical user interface for electrocardiogram analysis, and is characterized by a 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 constitute part of the design for which registration is sought. Figure 6.2 is a reference drawing and does not constitute part of the design for which registration is sought. 6.1) Front view; 6.2) Reference drawing

The application discloses an ECG high-frequency feature analysis method for myocardial ischemia and complex heart rate abnormalities, and belongs to the technical field of medical information processing and artificial intelligence, comprising: obtaining an ECG signal and performing preliminary classification based on a support vector machine; adopting a random forest model of a fusion model irrelevant meta-learning and a domain self-adaptive mechanism to dynamically compensate individual physiological differences; generating an individualized dynamic diagnosis threshold sequence through reinforcement learning; mining potential abnormal misjudgment modes by using a variational autoencoder and a generative adversarial network; fusing features and clarifying abnormal state boundaries by means of a graph neural network; and finally realizing gradient quantitative evaluation of the severity of myocardial ischemia through a multi-task learning model; the application effectively solves the problems of poor individual adaptability, fixed threshold and insufficient recognition of complex abnormal patterns of traditional methods, and significantly improves the accuracy, stability and clinical practicability of electrocardiogram analysis in the diagnosis of myocardial ischemia and complex heart rate abnormalities.

The application provides a portable extracorporeal counterpulsation treatment instrument for a family, which comprises a pulse signal detection module, an air bag wearing state detection module and an air bag contact state detection module, which respectively collect the pulse signal of a target object, the real-time wrapping pose state information of the air bag module on the body part of the target object and the contact state information between the air bag module and the body part of the target object, so as to control the first working state and the second working state of the air pump module for pumping and extracting air to the air bag module, the pulse signal of the target object is directly collected as the reference for controlling the action of the air pump, so that a large-volume electrocardiogram detection device and a complex electrocardiogram analysis circuit are not needed, the air pumping and extracting operation of the air bag can be realized by using a small-volume air pump, the volume of the extracorporeal counterpulsation treatment instrument is reduced, the complexity of the internal circuit structure is reduced, and the portable degree of the extracorporeal counterpulsation treatment instrument is improved.

The embodiment of the invention provides an electrocardiogram analysis method and system based on general representation learning, which are used for processing data differences among different patients to improve the generalization ability of an algorithm, and the method comprises the following steps: obtaining original electrocardiogram data of a target patient; wherein the target patient is a patient needing electrocardiogram data diagnosis at present; performing data preprocessing on the original electrocardiogram data to obtain electrocardiogram data; performing classification diagnosis on the electrocardiogram data through a predetermined electrocardiogram classification model to obtain an electrocardiogram diagnosis result of the target patient; wherein the electrocardiogram classification model comprises a category alignment module and a category level integration module; the category alignment module is used for extracting effective general characteristics and multiple categories of the electrocardiogram data; and the category level integration module is used for fusing a plurality of categories. By adopting the scheme, the generalization ability of the electrocardiogram classification algorithm can be greatly improved, and the algorithm can effectively enable clinical auxiliary diagnosis.

This design is a message 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 2.2 is a reference view and does not form part of the design sought to be registered. 2.1) Front view; 2.2) Reference view