Artificial intelligence real-time analysis method and system for electrocardiogram monitoring

Abstract

The embodiment of the invention relates to an artificial intelligence real-time analysis method and system for electrocardiogram monitoring. The method includes the steps of using electrocardiogram monitoring equipment for conducting electrocardiogram monitoring on a monitored user to obtain real-time electrocardiogram data and determining abnormal data of the real-time electrocardiogram data in real time on the basis of an online artificial intelligence (AI) electrocardiogram analysis model to generate a prewarning data segment; adopting a first cloud server for carrying out identification and judgment processing on the prewarning data segment according to an AI electrocardiogram evaluation model; adding a false alarm filtering mark to the prewarning data segment when this prewarning is determined to be a false alarm, and storing the false alarm filtering mark; carrying out abnormal data judgment on the prewarning data segment when prewarning is determined to be a true alarm, and transmitting an alarm record to a corresponding receiving device according to a transmission priority of alarm data records determined by a judgment level identifier; using the electrocardiogram monitoring equipment for uploading all the real-time electrocardiogram data to a second cloud server, generating dynamic electrocardiogram data of the monitored user, carrying out analysis according to an offline AI electrocardiogram analysis model and outputting report data.

Description

Technical field [0001] The present invention relates to the technical field of artificial intelligence data analysis, in particular to an artificial intelligence real-time analysis method and system for ECG monitoring. Background technique [0002] As we all know, electrocardiogram is the simplest, quickest and economical clinical examination method for various cardiovascular diseases, and it is the cornerstone of cardiovascular disease examination. Among them, the dynamic electrocardiography (DCG) technology was first applied by Holter in 1957 to the research of monitoring cardiac electrophysiological activity, so it is also called the Holter electrocardiograph, and it has become one of the important analysis methods for non-invasive examination in the clinical cardiovascular field. . Holter can continuously record the whole process of ECG activity for more than 24 hours, including ECG information under different conditions such as rest, activity, meal, work, study and sleep. ...

AI Technical Summary

Problems solved by technology

[0004] However, due to the complexity and variability of the ECG signal itself, the existing remote dynamic ECG monitoring technology has certain limitations, and it cannot truly analyze automatically, quickly and accurately, and there will still be a large number of missed reports and false positives. and these may lead to the inability to be effectively monitored when the monitored person has an abnormal physical condition

Therefore, the defects of the existing technology lead to the limitation of the application effect of remote dynamic ECG monitoring.

Images