A multi-scale convolutional neural network for heartbeat classification

Abstract

Electrocardiogram (ECG), as an important method for diagnosing cardiovascular diseases, can record the heart activity over a period of time. However, most of the current studies on ECG classification focus on the single scale information and ignore the complementary information between different scales. Therefore, this paper proposed an end-to-end multi-scale fusion convolutional neural network (CNN) for heartbeat classification. In this method, multiple convolution kernels of different reception domains are used to extract unique features of different scales, and the extracted multiple scale features are fused, which could effectively capture disease patterns and suppress noise interference. At the same time, attention module is used to select features to improve model performance. Improve efficiency with residual module. Finally, we obtained 34, 983 heartbeats from the Physikalisch-Technische Bundesanstalt (PTB) dataset to validate the model performance. The overall Fl-score is 99. 69%, and the Fl-score of each single class is more than 99. 35%, which is better than the existing algorithms. It can be described as a reference for future research.