ECG Heartbeat Classification Using Convolutional Neural Networks
Xuexiang Xu, Hongxing Liu
Abstract
Electrocardiogram (ECG) data recorded by Holter monitors are extremely hard to analyze manually. Therefore, it is necessary to automatically analyze and categorize each heartbeat using a computer-aid method. Because convolutional neural networks (CNNs) can classify ECG signals automatically without trivial manual feature extractions, they have received extensive attention. However, it is anticipated that improving the existing CNN classifiers might provide better overall accuracy, sensitivity, positive predictivity, etc. In this study, we proposed a CNN based ECG heartbeat classification method. Based on the MIT-BIH arrhythmia database, our proposed method achieved a sensitivity of 99.2% and positive predictivity of 99.4% in VEB detection; a sensitivity of 97.5% and positive predictivity of 99.1% in SVEB detection; and an overall accuracy of 99.43%. Our proposed system can be directly implemented on wearable devices to monitor long-term ECG data.