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.

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.

Described herein are methods, devices, and systems that use electrogram (EGM) or electrocardiogram (ECG) data for sleep apnea detection. An apparatus and method detect potential apnea events (an apnea or hypopnea event) using a signal indicative of cardiac electrical activity of a patient's heart, such as an EGM or ECG. Variations in one or more morphological or temporal features of the signal over several cardiac cycles are determined and used to detect a potential apnea event in a measurement period. Checks can then be made for a number of factors which could result in a false detection of an apnea event and if such factors are not present, an apnea event is recorded. Described herein are also methods, devices, and systems for classifying a patient as being asleep or awake, which can be used to selectively enable and disable sleep apnea detection monitoring, as well as in other manners.

A device for detecting acute coronary syndrome (ACS) events, arrythmias, heart rate abnormalities, medication problems such as non-compliance or ineffective amount or type of medication, and demand/supply related cardiac ischemia is disclosed. The device may have both implanted and external components and can communicate with other user devices such as smartphones and smartwatches for monitoring and alerting in response to detected medically relevant events or states of a patient. The processor is configured to provide event detection based upon various criteria including what is found to be statistically abnormal for a patient or what has been defined by a doctor to be abnormal. A patient's cardiovascular condition can be tracked over time using histogram, trend, and summary information related to heart rate and/or cardiac features such as those measured from the S-T segment of heartbeats. Heartbeats that are elevated but which are below what is defined as high, are used to provide medically relevant detections.

A device for detecting acute coronary syndrome (ACS) events, arrythmias, heart rate abnormalities, medication problems such as non-compliance or ineffective amount or type of medication, and demand/supply related cardiac ischemia is disclosed. The device may have both implanted and external components and can communicate with other user devices such as smartphones and smartwatches for monitoring and alerting in response to detected medically relevant events or states of a patient. The processor is configured to provide event detection based upon various criteria including what is found to be statistically abnormal for a patient or what has been defined by a doctor to be abnormal. A patient's cardiovascular condition can be tracked over time using histogram, trend, and summary information related to heart rate and/or cardiac features such as those measured from the S-T segment of heartbeats. Heartbeats that are elevated but which are below what is defined as high, are used to provide medically relevant detections.

The object of the invention is a method for determining electrical activity of cardiac muscle, characterised in that the resultant electric potential (V.sub.wyp) forming the QRS complex in the electrocardiogram obtained during the ECG test is decomposed into partial potentials corresponding to the depolarization of specific areas (i) of the left ventricular muscle (MS).

The object of the invention is a method for determining electrical activity of cardiac muscle, characterised in that the resultant electric potential (V.sub.wyp) forming the QRS complex in the electrocardiogram obtained during the ECG test is decomposed into partial potentials corresponding to the depolarization of specific areas (i) of the left ventricular muscle (MS).

An electrocardiogram information processing method and workstation system. The method includes receiving electrocardiogram data output by multiple devices; performs data analysis on the electrocardiogram data, and generating report data and stores same; receiving a report data query by a user, queries corresponding report data according to a user ID of the user, and generating report result list for display and output; receiving a selection by the user, and obtaining selected report data according to the selection; receiving a report data consultation request input by the user; obtaining a user ID of an associated user corresponding to the user ID according to the report data consultation request, and sending the report data to a user equivalent of the associated user according to the user ID of the associated user; and receiving a consultation result feedback data sent by the user equivalent of the associated user.

An artificial intelligence self-learning-based static electrocardiography analysis method and apparatus, the method comprising data preprocessing, heartbeat detection, heartbeat classification based on a depth learning method, heartbeat verification, heartbeat waveform feature detection, measurement and analysis of electrocardiography events, and finally automatic output of reporting data, realizing an automated static electrocardiograph analysis method having a complete and rapid flow. The static electrocardiography analysis method can also record modification information of an automatic analysis result, collect modified data, and feed same back to the depth learning model to continue training, thereby continuously making improvements and improving the accuracy of the automatic analysis method.

The present disclosure encompasses an “artifact score” derived from the signal characteristics of an acquired 12-lead ECG, (2) a “patient context score” derived from key elements of the patient's history, presentation, and pre-hospital emergency care, and (3) techniques for integrating these scores into an emergency medical care system.