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

Published: 30 December 2021 Publication History

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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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 30 December 2021
    Accepted: 01 April 2021
    Revised: 01 March 2021
    Received: 01 January 2021
    Published in TOMM Volume 17, Issue 3s

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    Author Tags

    1. EEG classification
    2. CNN
    3. wavelet transform
    4. recurrence plot

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    Funding Sources

    • National Natural Science Foundation of China
    • Natural Science Foundation of Hebei Province
    • Science Research Project of Hebei Province
    • Open Foundation of Guangdong Key Laboratory of Digital Signal and Image Processing Technology
    • High-Performance Computing Center of Hebei University

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    • (2025)Enhancing electroencephalogram signal quality in epileptic patients using bidirectional stochastic long short-term memory networkNeural Computing and Applications10.1007/s00521-025-10977-1Online publication date: 4-Feb-2025
    • (2024)A novel and efficient multi-scale feature extraction method for EEG classificationAIMS Mathematics10.3934/math.20248059:6(16605-16622)Online publication date: 2024
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