A Classification Method for ECG Signals Based on Convolutional Neural Network

Abstract

Cardiovascular disease is a chronic disease with high incidence, high disability and high mortality, which poses a great threat to the life and health of people all over the world. At present, the incidence and mortality of cardiovascular disease are increasing year by year worldwide, so the prevention and treatment of cardiovascular disease has become a top priority. In recent years, with the development of computer technology in the field of auxiliary diagnosis and treatment, the research on automatic classification of Electrocardiogram (ECG) signals has ushered in new opportunities. In this study, ECG signals are taken as the research object, to analyze the auxiliary diagnosis needs of users such as patients and pathologists. This study mainly uses ECG data from MIT-BIH database, combined with relevant preprocessing knowledge and deep learning classification model, to achieve ECG reading, denoising, segmentation, classification and so on. It can effectively improve the efficiency of diagnosis. It has certain reference value for assisting users to diagnose arrhythmia.