An Automated ECG Beat Classification System Using Convolutional Neural Networks

Classification of Electrocardiogram (ECG) plays an important role in clinical diagnosis of cardiac diseases. In this paper, we introduce an ECG beat classification system using convolutional neural networks (CNNs). The proposed model integrates two main parts, feature extraction and classification, of ECG pattern recognition system. This model automatically learns a suitable feature representation from raw ECG data and thus negates the need of hand-crafted features. By using a small and patient-specific training data, the proposed classification system efficiently classified ECG beats into five different classes recommended by Association for Advancement of Medical Instrumentation (AAMI). ECG signal from 44 recordings of the MIT-BIH database are used to evaluate the classification performance and the results demonstrate that the proposed approach achieves a significant classification accuracy and superior computational efficiency than most of the state-of-the-art methods for ECG signal classification.