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作者:

Yao, Zhenjie (Yao, Zhenjie.) | Zhu, Zhiyong (Zhu, Zhiyong.) | Chen, Yixin (Chen, Yixin.)

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EI Scopus

摘要:

Atrial Fibrillation (AF) is the most common chronic arrhythmia. Effective detection of the AF would avoid serious consequences like stroke. Conventional AF detection methods need heuristic or hand-craft feature extraction. In this paper, A deep neural network named multi-scale convolutional neural networks (MCNN) based AF detector is proposed. Instant heart rate sequence is extracted from ECG signal, then an end-to-end MCNN detects AF with the instant heart rate sequence as input and detection result as output. The algorithm was tested on both public and private datasets. On the public dataset, with the sensitivity achieved being 0.9822, the corresponding specificity is 0.9811, and the overall accuracy is 0.9818. The area under an ROC curve is as high as 0.9962, compared to the AUC of the best conventional method is 0.9947. Comparison shows that the MCNN based AF detector give superior accuracy than conventional methods. Test on private dataset also shows significant improvement. © 2017 International Society of Information Fusion (ISIF).

关键词:

Convolution Convolutional neural networks Deep neural networks Diseases Feature extraction Heart Heuristic methods Information fusion Statistical tests

作者机构:

  • [ 1 ] [Yao, Zhenjie]Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China
  • [ 2 ] [Zhu, Zhiyong]Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China
  • [ 3 ] [Chen, Yixin]Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China
  • [ 4 ] [Chen, Yixin]Department of Computer Science and Engineering, Washington University in St. Louis, United States

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年份: 2017

语种: 英文

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WoS核心集被引频次: 0

SCOPUS被引频次: 44

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