Automated ECG classification based on 1D deep learning network

Abstract

The standard 12-lead electrocardiogram (ECG) records the heart's electrical activity from electrodes on the skin, and is widely used in screening and diagnosis of the cardiac conditions due to its low price and non-invasive characteristics. Manual examination of ECGs requires professional medical skills, and is strenuous and time consuming. Recently, deep learning methodologies have been successfully applied in the analysis of medical images. In this paper, we present an automated system for the identification of normal and abnormal ECG signals. A multi-channel multi-scale deep neural network (DNN) model is proposed, which is an end-to-end structure to classify the ECG signals without any feature extraction. Convolutional layers are used to extract primary features, and long short-term memory (LSTM) and attention are incorporated to improve the performance of the DNN model. The system was developed with a 12-lead ECG dataset provided by the Kaohsiung Medical University Hospital (KMUH). Experimental results show that the proposed system can yield high recognition rates in classifying normal and abnormal ECG signals.

Keywords: 12-Lead electrocardiogram; Cardiac abnormality; Convolutional layer; Long short-term memory; Self-constructing clustering.