Method and system of electrocardiosignal classification

Abstract

The invention relates to a method and system of electrocardiosignal classification. The method comprises the steps of obtaining electrocardiographic test data, processing the electrocardiographic test data to obtain a waveform graph with the predetermined size, and combined with the oscillogram with the predetermined size and an established heart disease classification type model, determining a classification result corresponding to the electrocardiographic test data, wherein the heart disease classification type model is a model capable of reflecting original waveform characteristics and the classification result. By utilizing the method of electrocardiosignal classification, the accuracy rate of the classification result can be increased, and the purpose that the heart rate variability of more types is classified can be achieved at the same time.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a method and system for classifying electrocardiographic signals. Background technique [0002] Electrocardiograph (ECG) is a kind of data measured by electronic equipment, which is used to describe the whole process of cardiac activity, including the curve of potential changes in the process of excitation generation, excitation conduction and excitation recovery, mainly including cardiac Atrial depolarization, atrial repolarization, ventricular depolarization, and ventricular repolarization. [0003] Dynamic electrocardiogram, also known as Holter. It is the most important non-invasive ECG monitoring technology in the contemporary era. It can record 24-hour ECG data, display the total number of heartbeats, maximum heart rate, minimum heart rate, average heart rate and hourly heart rate during the monitoring period within 24 hours; display supraventricular, ventricular ...

AI Technical Summary

Problems solved by technology

[0004] At present, the research on the automatic detection and recognition of ECG basically exists in the theoretical field and the exploratory field, but the effect in the actual diagnosis of cardiovascular diseases is not ideal.

This is mainly due to the fact that previous researchers, when studying the automatic detection and identification of ECG, ignored the doctors' attention to the morphological features of ECG when diagnosing cardiovascular diseases.

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