A 1D Convolutional Neural Network for Heartbeat Classification from Single Lead ECG
Li Xiaolin, Barry Cardiff, Deepu John
Abstract
The advent of low-cost wearable Internet of Things (IoT) sensors has made it possible to continuously acquire physiological signals such as electrocardiogram (ECG) for long durations. Techniques for automated analysis is essential for deriving intelligence from such a large quantity of data. This paper presents a 1-dimenslonal convolutional neural network (CNN) for heartbeat classification from ECG signals obtained from an ambulatory device. The proposed technique can classify heartbeats into 5 classes as specified in AAMI standard and was tested using the Physionet MIT-BIH Arrhythmia database. To address the imbalance of classes in the dataset we used the SMOTE algorithm to augment the dataset. The network was trained using the augmented data and achieved an accuracy of 98.12%, sensitivity of 98.07%, and a specificity of 98.29%.