医学信息2025，Vol.38Issue(21)：1-7,7.DOI:10.3969/j.issn.1006-1959.2025.21.001
基于机器学习的临床心电图自动分类模型设计
Design of Clinical Electrocardiogram Automatic Classification Model Based on Machine Learning
摘要
Abstract
Electrocardiogram(ECG)is a non-invasive one-dimensional medical signal collected on the surface of the human body,which can reflect the changes in electrophysiological signals of cardiac muscle cells during the beating process of the human heart,thus reflecting the health status of the human heart.This article will study the automatic classification technology of electrocardiogram based on Python+TensorFlow2,using the MIT-BIH electrocardiogram database as the research data source,and following the AAMI EC57 standard to classify heartbeats into 5 categories.The constructed electrocardiogram classification model can be used to identify different categories of heart diseases by recognizing 5 types of arrhythmias,in order to reduce the clinical diagnosis cost of arrhythmias and assist doctors in analyzing electrocardiograms.关键词
机器学习/卷积神经网络/心电信号/特征提取Key words
Machine learning/Convolutional neural network/ECG signal/Feature extraction分类
信息技术与安全科学引用本文复制引用
林加论,李晓玲,夏金阳,张锦..基于机器学习的临床心电图自动分类模型设计[J].医学信息,2025,38(21):1-7,7.基金项目
海南省自然科学基金面上项目(编号:622MS067) （编号:622MS067）
