Artificial intelligence-based method for automatic recognition and classification of electrocardiogram heart beats

Abstract

The embodiment of the present invention relates to an artificial intelligence-based method for automatically identifying and classifying electrocardiogram heartbeats, including: processing the received original electrocardiogram digital signals to obtain heartbeat time-series data and lead heartbeat data; according to the heartbeat time-series data, Cut the heartbeat data of the lead to generate the heartbeat analysis data of the lead; combine the data of the heartbeat analysis data of the lead to obtain a one-dimensional heartbeat analysis array; perform data dimension expansion according to the one-dimensional heartbeat analysis array Convert to obtain four-dimensional tensor data; input the four-dimensional tensor data to the trained LepuEcgCatNet heartbeat classification model to obtain heartbeat classification information. This method improves the defect that the traditional method only relies on the independent analysis of a single lead to conduct the summary statistics of the results, which is relatively easy to obtain classification errors, and greatly improves the accuracy of the electrocardiogram heart beat classification.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence-assisted data analysis and processing, in particular to an artificial intelligence-based automatic recognition and classification method for electrocardiogram heartbeats. Background technique [0002] Cardiovascular disease is one of the main diseases that threaten human health, and the use of effective means to detect cardiovascular disease is an important topic of concern worldwide. Electrocardiogram (ECG) is the main method of diagnosing cardiovascular diseases in modern medicine. Using ECG to diagnose various cardiovascular diseases is essentially the process of extracting ECG characteristic data to classify ECG. In the process of reading and analyzing the electrocardiogram, expert doctors need to compare the previous changes of the signals of each lead (except for single-lead data) in time sequence, the correlation (spatial relationship) and variation between the leads, and th...

AI Technical Summary

Problems solved by technology

However, although most of the electrocardiogram analysis software on the market can automatically analyze the data, due to the complexity and variability of the electrocardiogram signal itself, the accuracy of the current automatic analysis software is far from enough to meet the requirements of clinical analysis.

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