252 results about "Electrocardiography" patented technology

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.

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 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.

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.

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 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.

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 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 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.

The invention discloses a multi-person electrocardiogram reconstruction method based on a millimeter-wave radar, which is applied to the technical field of millimeter-wave radar detection, and aims at solving the problem that in the prior art, only simple heart rate estimation of a plurality of targets is considered, but the estimation of heartbeat signals is not involved. The method comprises the following steps: firstly, converting a radar phonocardiogram into a frequency domain signal; extracting a top-k frequency component and converting the top-k frequency component into a time domain signal; then, multi-head cross attention and multi-head self-attention mechanisms are used for carrying out self-correlation reconstruction on the signals; the obtained output is then input into a frequency Gaussian weighting module, and then a reconstructed ECG signal is output. Compared with traditional heartbeat information analysis of multi-target signals, more accurate electrocardiogram reconstruction information can be provided.

A method for cardiac diagnostic analysis is provided. The system includes a processor and memory coupled to the processing unit, receiving data from standard 12-lead ECGs from an external device; at least one computer-readable storage medium containing instructions, including program code that causes the execution of procedures according to the programs encoded on a disk or other media capable of using advanced algorithms such as deep learning networks, so that they can be processed during any given procedure without substantially altering the existing clinical workflow.

The invention discloses an electrocardio data processing and coronary heart disease risk prediction method, device and system and a medium. The method comprises the steps of obtaining exercise electrocardio data output through at least one electrocardiogram lead; analyzing the exercise electrocardiogram data to obtain a heart rate sequence corresponding to each electrocardiogram lead; extracting a QRS wave group corresponding to each heart beat from the exercise electrocardio data; dividing a plurality of heart rate intervals according to the obtained heart rate sequence; according to the heart rate interval to which the heart rate corresponding to each QRS wave group belongs, classifying the QRS wave group into a group corresponding to the corresponding heart rate interval; determining an average QRS wave group corresponding to each heart rate interval according to all the QRS wave groups in each group; extracting the high-frequency energy of the average QRS wave group corresponding to each heart rate interval; and determining an energy sequence according to the high-frequency energy corresponding to each heart rate interval. According to the scheme, high-frequency components and dynamic load information which are more sensitive to ischemia are utilized, so that the detection rate and the identification capability of coronary heart diseases, particularly early and mild lesions, can be remarkably improved.

A cardiac monitoring system that automatically determines whether one or more electrodes are placed at wrong positions on a person's body includes an ECG monitor. The electrodes are connected to the ECG monitor wirelessly or by wire. Each electrode includes a position code that corresponds to a body location where the electrode is to be placed. The signals that are transmitted from each electrode include the position code assigned to the electrode. The profile of the signals received from the electrodes are compared with expected profile of the signals from the electrodes that are placed at each specific position on the body. The position code in the transmitted data of any electrode that is determined to be placed at a wrong position is automatically replaced with the position code corresponding to the electrodes' actual position. The leads are then determined based on the electrode signals and the electrodes' position codes.

Methods, systems, and devices that detect arrhythmic episodes and perform arrhythmia discrimination are described. Such a system includes a leadless pacemaker (LP) that senses a near-field electrogram (NF-EGM), and a non-vascular implantable cardioverter defibrillator (NV-ICD) that senses a far-field electrogram (FF-EGM). The LP determines cardiac activity information based on the NF-EGM and optionally also based on paced cardiac events caused by the LP. The LP monitors for specific pacemaker condition(s), sends i2i message(s) including the cardiac activity information to the NV-ICD when at least one of the specific pacemaker condition(s) is detected by the LP, and does not send i2i message(s) including the cardiac activity information to the NV-ICD when the LP detects none of the specific pacemaker condition(s) After the NV-ICD receives the i2i message(s) transmitted by the LP, the NV-ICD can detect an arrhythmic episode and / or perform arrhythmia discrimination based on the cardiac activity information included therein.

A wearable medical device is provided for monitoring the cardiac health of a patient, for example, for indications of cardiac anomalies, where the device includes ECG sensors in electrical contact with the patient's body, therapy electrodes for providing electrical therapy to the patient's heart, and a control unit having at least one touch control with force sensor disposed on its housing for contacting with a finger. Signals from the touch control may be analyzed to identify force application below a first force threshold and at or above a second force threshold below the first force threshold, and, responsive to detecting such application of force, user input may be registered. User inputs to the at least one touch control may be used to delay therapy by the therapy electrodes.

An ambulatory monitor with integrated defibrillator is provided. Four walls are each affixed on one side of a bottom surface. A dividing layer is affixed to each of the four walls creating an electrode enclosure and a circuit enclosure configured to hold circuitry. Access to the circuitry is restricted by the bottom surface and dividing layer. A pair of pads is stored in the electrode enclosure, and each of the pads is applied to a user, by the user, for collecting ECG data of the user. Each lead of a pair is affixed on one end to one of the pads and on an other end to the circuitry. An accelerometer is affixed on one of the leads or one of the pads. A cover is configured to fit over the electrode enclosure on a side opposite the circuit enclosure.

The invention discloses an emotion classification method and system for heart electromagnetic signals of heart rate variability based on topology analysis, and relates to the technical field of biomedical signal processing and emotion calculation, and the emotion classification method comprises the following steps: preprocessing magnetocardiogram signals or electrocardiogram signals; positioning R waves, extracting an RR interval sequence, and extracting statistics of topological feature points of the persistent graph as topological features; and performing fusion through the time sequence branch, the topological branch and the fusion layer to generate a fusion vector, and classifying the emotional state based on the fusion vector. According to the method, the signal quality and the anti-interference capability are remarkably improved, topological data analysis (TDA) is innovatively introduced, quantitative characterization of a heart rate variability nonlinear dynamic structure is achieved, and the capturing capability of the model to a key physiological mode is enhanced; the problem of overfitting under small samples is effectively relieved, the accuracy, robustness and cross-individual generalization ability of emotion classification are remarkably improved, and a new method is provided for emotion calculation.

A wearable device (100) for monitoring cardiac function during apnoeic episode. Wearable device including ballistocardiography (BCG) sensor (102A) to measure mechanical forces generated by heart and respiratory movement; electrocardiography (ECG) sensor (102B) to measure electrical activity of heart; and processor (102C) operatively connected to BCG sensor and ECG sensor to generate BCG data from measured mechanical forces and ECG data from measured electrical activity, respectively. Processor configured to: filter respiratory signals and heart signals from BCG data; detect apnoeic episode based on low respiratory signals or absence of respiratory signals for predetermined time period in filtered BCG data; determine compensatory mechanical output of heart using at least one of filtered BCG data or ECG data for apnoeic episode, and predict risk of cardiac condition based on compensatory mechanical output during apnoeic episode. Also a system (300) and method thereof.

A dual-mode measurement body fat scale disclosed by the present invention comprises a scale body and a handle, the scale body is provided with a first power supply module, a first main control module, a foot electrode module and a weighing module, the handle is provided with a second power supply module, a second main control module and a hand electrode module, the foot electrode module comprises four foot electrodes, and the weighing module comprises a first main control module and a second main control module. The hand electrode module comprises four hand electrodes and further comprises an ECG measuring module and a channel switching module. The channel switching module is connected with the hand electrode module and has a first state and a second state; the function reuse of the hand electrode module is realized through the channel switching module, and when the channel switching module is in a first state, the hand electrode is connected with the first main control module and is matched with the foot electrode to form a complete body fat measuring loop; when the module is switched to the second state, the hand electrode is connected with the ECG measurement module to form an ECG measurement loop, electrocardiogram data of a user can be conveniently collected, and the cardiovascular health monitoring requirement is met.

A system and method for determining and modifying the electromagnetic activity of a patient's heart includes a sensor array, a controller, software, and a stimulator array. The sensor array includes one or more sensors, such as an ECG electrode, that detect the electromagnetic activity of a patient's heart. The sensor array is in data communication with the controller. The controller runs software that outputs in human-format sensor data provided by the sensor array. The software receives data from the controller, analyzes the data, presents the data to the user, and sends the data to the stimulator array via a data communication link. The stimulator array includes one or more electromagnets and at least one or more light-emitting diodes. The stimulator array emits an electromagnetic (and / or light) field to modify the electromagnetic activity of a patient's heart based on the data it receives from the controller. The system can be portable.

A wearable defibrillator for use in dry and non-dry locations includes a harness, ECG sensing electrodes, therapy electrodes configured to deliver defibrillating shocks, and a user interface pod configured to be attached to the harness. The user interface pod includes a speaker configured to provide an audible alarm and one or more buttons configured to be pressed by the patient to indicate the patient is conscious. The wearable defibrillator also includes a waterproof or water-resistant control unit configured to determine whether the patient is experiencing a cardiac arrhythmia, issue the audible alarm, and deliver the defibrillating shocks. The wearable defibrillator further includes a connection pod configured to electrically couple the therapy electrodes to the control unit. The user interface pod is configured to be electrically coupled to the control unit via a waterproof removable connector, which forms a water-tight seal with the control unit when connected to the control unit.

The invention relates to the technical field of medical instruments, in particular to a heart health state assessment method, a processing device, a system and a computer readable storage medium. Motion electrocardiogram data, resting electrocardiogram data and conventional electrocardiogram data output by electrocardiogram leads are obtained and analyzed to determine the possibility of myocardial ischemia, coronary artery lesion, myocardial bridge and coronary microcirculation disturbance, and the heart health state is qualitatively evaluated according to the determination result. And under the condition that the possibility of myocardial ischemia and / or coronary artery lesion exists is determined, quantitative evaluation is carried out on myocardial ischemia and / or coronary artery lesion, and finally the heart health state is evaluated according to the qualitative evaluation result and the quantitative evaluation result. According to the technical scheme provided by the embodiment of the invention, the heart health state can be more accurately qualitatively and quantitatively analyzed by combining the exercise electrocardio data, the resting electrocardio data and the conventional ECG data.

Wearable electrocardiogram (ECG) systems configured to record ECG from ears (auricular ECG systems), fingers (ear-finger or finger-finger ECG systems) or toes, comprising a processing unit configured to analyze the ECG data to detect serious cardiac arrhythmias or impending serious cardiac arrhythmia. Automatic anti-arrhythmia systems comprise one of the ECG systems and a transcutaneous auricular vagus nerve stimulation (taVNS) unit. When serious cardiac arrythmias are detected, the anti-arrhythmia systems may automatically send pre-determined electric stimuli to a vagus nerve to alleviate or stop the cardiac arrhythmias. The ECG electrodes and the stimulating electrode of the taVNS unit are located on a surface of a housing structure made of flexible elastic and adaptable material such that these electrodes will be naturally and snugly contacting the wearer's skin. These wearable ECG and anti-arrhythmia systems are suitable for easy self-installation, self-removal and daily ambulatory use.

A method, device, system and medium for electrocardio data processing and coronary heart disease risk prediction, motion electrocardio data output through at least one electrocardio lead is acquired; the motion electrocardio data is analyzed to obtain a heart rate sequence corresponding to each electrocardio lead; each QRS complex corresponding to each heartbeat is extracted from the motion electrocardio data; a plurality of heart rate intervals are divided according to the obtained heart rate sequence; each QRS complex is classified into a group corresponding to a heart rate interval according to the heart rate interval to which the heart rate corresponding to the QRS complex belongs; the average QRS complex corresponding to each heart rate interval is determined according to all QRS complexes in each group; the high-frequency energy of the average QRS complex corresponding to each heart rate interval is extracted; and an energy sequence is determined according to the high-frequency energy corresponding to each heart rate interval. The present scheme uses high-frequency components and dynamic load information which are more sensitive to ischemia, and can significantly improve the detection rate and discrimination ability for coronary heart disease, especially early and mild lesions.