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학술대회 An Automated ECG Beat Classification System Using Convolutional Neural Networks
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주바이르, 김진술, 윤장우
International Conference on IT Convergence and Security (ICITCS) 2016
16HH1800, 인체활동 통합관리지원을 위한 다중 웨어러블 SW융합모듈 및 유연 SW 응용플랫폼 기술개발 , 송기봉
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 제안 키워드
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