Normal Versus Abnormal ECG Classification by the Aid of Deep Learning

Abstract

With the development of telemedicine systems, collected ECG records are accumulated on a large scale. Aiming to lessen domain experts’ workload, we propose a new method based on lead convolutional neural network (LCNN) and rule inference for classification of normal and abnormal ECG records with short duration. First, two different LCNN models are obtained through different filtering methods and different training methods, and then the multipoint-prediction technology and the Bayesian fusion method are successively applied to them. As beneficial complements, four newly developed disease rules are also involved. Finally, we utilize the bias-average method to output the predictive value. On the Chinese Cardiovascular Disease Database with more than 150,000 ECG records, our proposed method yields an accuracy of 86.22% and 0.9322 AUC (Area under ROC curve), comparable to the state-of-the-art results for this subject