4.7 Article

Electrocardiogram soft computing using hybrid deep learning CNN-ELM

期刊

APPLIED SOFT COMPUTING
卷 86, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.asoc.2019.105778

关键词

Electrocardiogram (ECG) signals; MIT-BIH dataset; Extreme learning machine; Classification

资金

  1. Scientific Research Fund of Hunan Provincial Education Department of China [17A007]
  2. Teaching Reform and Research Project of Hunan Province of China

向作者/读者索取更多资源

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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据