BROWSE
Detail
Conference Paper
An Automated ECG Beat Classification System Using Convolutional Neural Networks
Cited 206 time in 
Share 
Share
- Authors
- Muhammad Zubair, Jinsul Kim, Changwoo Yoon
- Issue Date
- 2016-09
- Citation
- International Conference on IT Convergence and Security (ICITCS) 2016
- Language
- English
- Type
- Conference Paper
- Abstract
- 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.
- KSP Keywords
- Cardiac Diseases, Classification Performance, Classification system, Clinical diagnosis, Computational Efficiency, Convolution neural network(CNN), ECG beat classification, ECG signal, Feature Extraction and Classification, Feature representation, MIT-BIH database