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WTRPNet: An Explainable Graph Feature Convolutional Neural Network for Epileptic EEG Classification

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Published:30 December 2021Publication History
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Abstract

As one of the important tools of epilepsy diagnosis, the electroencephalogram (EEG) is noninvasive and presents no traumatic injury to patients. It contains a lot of physiological and pathological information that is easy to obtain. The automatic classification of epileptic EEG is important in the diagnosis and therapeutic efficacy of epileptics. In this article, an explainable graph feature convolutional neural network named WTRPNet is proposed for epileptic EEG classification. Since WTRPNet is constructed by a recurrence plot in the wavelet domain, it can fully obtain the graph feature of the EEG signal, which is established by an explainable graph features extracted layer called WTRP block. The proposed method shows superior performance over state-of-the-art methods. Experimental results show that our algorithm has achieved an accuracy of 99.67% in classification of focal and nonfocal epileptic EEG, which proves the effectiveness of the classification and detection of epileptic EEG.

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    • Published in

      cover image ACM Transactions on Multimedia Computing, Communications, and Applications
      ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 17, Issue 3s
      October 2021
      324 pages
      ISSN:1551-6857
      EISSN:1551-6865
      DOI:10.1145/3492435
      Issue’s Table of Contents

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      Publication History

      • Published: 30 December 2021
      • Accepted: 1 April 2021
      • Revised: 1 March 2021
      • Received: 1 January 2021
      Published in tomm Volume 17, Issue 3s

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