An electrocardiogram information dynamic monitoring method and dynamic monitoring system. The method includes a dynamic monitoring device receiving monitoring reference data input by a user or issued by a server; the data collection on a tested object so as to obtain electrocardiogram data of the tested object; the characteristic identification on the electrocardiogram data so as to obtain characteristic signals of the electrocardiogram data, implementing cardiac activity classification on the electrocardiogram data according to the characteristic signals, obtaining cardiac activity classification information according to electrocardiogram basic rule reference data, and generating electrocardiogram event data, wherein the electrocardiogram event data comprises device ID information of the dynamic monitoring device; the dynamic monitoring device determining corresponding electrocardiogram event information according to the electrocardiogram event data, and determining whether the electrocardiogram event information is electrocardiogram abnormality event information; and outputting alarm information when the electrocardiogram event information is electrocardiogram abnormality event information.

Disclosed are a method and apparatus for visualizing an electrocardiogram using deep learning.

The present embodiment provides a method and apparatus for visualizing an electrocardiogram, the method and apparatus which analyze an electrocardiogram using a deep learning algorithm for accurate arrhythmia determination as a real-time operation algorithm for monitoring a bedridden patient in order to solve the manpower shortage of medical staff, and then visually output it in real time so that a visual help may be provided for medical staff.

Disclosed are a method and apparatus for visualizing an electrocardiogram using deep learning.

The present embodiment provides a method and apparatus for visualizing an electrocardiogram, the method and apparatus which analyze an electrocardiogram using a deep learning algorithm for accurate arrhythmia determination as a real-time operation algorithm for monitoring a bedridden patient in order to solve the manpower shortage of medical staff, and then visually output it in real time so that a visual help may be provided for medical staff.

Systems and methods for electrocardiographic waveform analysis, data presentation and actionable alert generation are described. Electrocardiographic waveform data can be received from a wearable device associated with a patient. A mathematical analysis of at least a portion of the electrocardiographic waveform data can be performed to provide cardiac analytics. In instances where (1) a pathologically prolonged QT interval and (2) an R on T premature ventricular contraction and/or a ventricular tachycardia are detected from the cardiac analytics of the at least a portion of the electrocardiographic waveform data, an actionable alert can be generated and displayed with a visualization of the cardiac analytics.

Systems and methods for electrocardiographic waveform analysis, data presentation and actionable alert generation are described. Electrocardiographic waveform data can be received from a wearable device associated with a patient. A mathematical analysis of at least a portion of the electrocardiographic waveform data can be performed to provide cardiac analytics. In instances where (1) a pathologically prolonged QT interval and (2) an R on T premature ventricular contraction and/or a ventricular tachycardia are detected from the cardiac analytics of the at least a portion of the electrocardiographic waveform data, an actionable alert can be generated and displayed with a visualization of the cardiac analytics.

Medical apparatus includes a probe configured for insertion into a chamber of a heart of a patient and including one or more intracardiac electrodes. Interface circuitry acquires intracardiac electrogram signals from the intracardiac electrodes and electrocardiogram (ECG) signals from body-surface electrodes that are fixed to a body surface of the patient. A processor detects, in each of the heartbeats in the sequence, a P wave in the acquired ECG signals and identifies one or more of the heartbeats in the sequence as ectopic beats responsively to a morphology of the detected P wave in the heartbeats. The processor extracts electrophysiological parameters from the intracardiac electrogram signals acquired during the sequence of the heartbeats and generates a map of the extracted electrophysiological parameters while excluding from the map the intracardiac electrogram signals that were received during the ectopic beats.

An apparatus yields signals that are equivalent to ECG signals and allow determination of a heartbeat interval or heart rate from bio-vibration signals including vibrations derived from heartbeats. An ECG meter acquires ECG signals of a sample, and a piezoelectric sensor acquires bio-vibration signals of the sample simultaneously. The bio-vibration signals include beating vibration signals derived from heartbeats. A learning unit of a prediction modeling apparatus establishes a prediction model by machine learning in which ECG signals are used as teaching data, and model input signals obtained by performing a specified processing on the bio-vibration signals are input. The learning unit delivers the prediction model to a prediction unit of a signal processing apparatus. The prediction model predicts and outputs pECG signals upon input of model input signals obtained by performing a specified processing on bio-vibration signals acquired from a subject under prediction with a piezoelectric sensor.

A method for adapting, per cardiac cycle, the parameters governing interpolation of varying and non-interpolation of fixed fractions of each individual cardiac cycle is provided. A time series of data values associated with a cardiac cycle is received, and the time series is scaled to a reference cardiac cycle, wherein the scaling includes applying a model to the time series to generate a scaled time series of data values associated with the first cardiac cycle. The model is trained using the scaled time series.

A system for establishing a model for cardiac ventricular hypertrophy (VH) screening includes a storage and a processor. The storage stores multiple pieces of subject data respectively associated with multiple subjects. Each of the pieces of subject data contains a basic physiological parameter group, an electrocardiographic parameter group, and an actual VH condition that corresponds to a left or right ventricle of the subject associated with the piece of subject data. The processor is electrically connected to the storage, splits the pieces of subject data into a training set and a test set, and establishes the model for VH screening based on the pieces of subject data in the training set by using machine learning techniques.

A system and a method for identifying a patient with a threshold number of distinct ECG abnormalities. The system and the method include an ECG monitoring device; a server; a database; a network; a memory containing machine readable medium comprising a machine executable code having stored thereon instructions for identifying patients with a threshold number of distinct ECG abnormalities; and a processor coupled to the memory, the processor configured to execute the machine executable code to cause the processor to: receive an ECG data output from the ECG monitoring device; process the ECG data output to identify abnormalities in the ECG data; and analyze the abnormalities in the ECG data in order to output an indication of whether the patient has depressed LVEF, wherein the ECG monitoring device, the server, the database, the memory, and the processor are coupled to the network via communication links.