关键词: 心电图（ECG）, 心拍分类, 卷积神经网络（CNN）, MIT-BIH数据库, 共性卷积神经网络, 个性卷积神经网络

Abstract: Heart disease is a serious threat to human health. Electrocardiogram（ECG） classification of heart beat plays an important role in clinical diagnosis and automatic diagnosis of heart disease. Although the existing ECG heart beat features generated by deep learning are superior to the heart beat features generated by traditional methods, there are serious data imbalance problems among various types of ECG, resulting in the poor performance of existing heart beat features generated based on deep learning methods. In order to solve this problem, based on the Convolutional Neural Network（CNN）, this paper first constructs a common CNN model which can effectively express the common information of all kinds of heart beat on the basis of the equal data of all kinds of heart beat. Then based on the model of minimizing intra-class distances and maximizing inter-class distances, a category CNN model that can effectively reflect the propensity information of the corresponding heart beat categories is constructed on each type of heart beat data. Finally, the output of each heart beat category CNN model is integrated for identification and classification. The experimental results on the MIT-BIH database show that this method achieves 100% of the indicators of the classification of heart beat, solves the problem of automatic identification and classification of ECG four types of heart beats in the MIT-BIH database.

Key words: Electrocardiogram（ECG）, classification of heart beat, Convolutional Neural Network（CNN）, MIT-BIH database, common Convolutional Neural Network（CNN）, category Convolutional Neural Network（CNN）