基于深度学习的心电图心律失常分类方法

ECG arrhythmia classification method based on deep learning

近些年来因心血管疾病导致的人类死亡人数不断增加,心律失常是心血管疾病发病前的常见症状。为了提高心电图对心律失常分类的效率和准确率,使医生能对心律失常及时地作出诊断和治疗,提出一种基于二维卷积神经网络模型的心律失常分类方法。该方法使用美国麻省理工学院提供的研究心律失常的MIT-BIH数据库来生成实验数据集对网络进行训练和测试,在心律失常分类测试中分类准确率达到了98.6%,实现了对心电图信号心律失常的高精度自动分类。

In recent years,the number of human deaths caused by cardiovascular diseases has been increasing,and arrhythmia is a common symptom before the onset of cardiovascular diseases.In order to improve the efficiency and accuracy of classifying arrhythmia through electrocardiogram,so that doctors can diagnose and treat the arrhythmia in a timely manner,a method is proposed for arrhythmia classification based on a two-dimensional convolutional neural network model,using MIT-BIH database for arrhythmia research provided by the Massachusetts Institute of Technology to generate experimental data sets to train and test the network.In the arrhythmia classification test,it achieved a classification accuracy of 98.6%,and realized high-precision automatic detection of arrhythmia from ECG signals classification.