Journal
APPLIED SOFT COMPUTING
Volume 86, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2019.105778
Keywords
Electrocardiogram (ECG) signals; MIT-BIH dataset; Extreme learning machine; Classification
Categories
Funding
- Scientific Research Fund of Hunan Provincial Education Department of China [17A007]
- Teaching Reform and Research Project of Hunan Province of China
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Electrocardiogram (ECG) can reflect the state of human heart and is widely used in clinical cardiac examination. However, the electrocardiogram signal is very weak, the anti-interference ability is poor, easy to be affected by the noise. Doctors face difficulties in diagnosing arrhythmias. Therefore, automatic recognition and classification of ECG signals is an important and indispensable task. Since the beginning of the 21 st century, deep learning has developed rapidly and has shown the most advanced performance in various fields. This paper presents a method of combining (Convolutional neural network) CNN and ELM (extreme learning machine). The accuracy rate is 97.50%. Compared with the state-of-the-art methods, this method improves the accuracy of ECG automatic classification and has good generalization ability. (C) 2019 Elsevier B.V. All rights reserved.
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