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ORIGINAL RESEARCH article

Front. Neurosci.
Sec. Neuroscience Methods and Techniques
Volume 18 - 2024 | doi: 10.3389/fnins.2024.1539580
This article is part of the Research Topic Neural Dynamics for Brain-inspired Control and Computing: Advances and Applications View all articles

Application of Deconvolutional Networks for Feature Interpretability in Epilepsy Detection

Provisionally accepted
Sihao Shao Sihao Shao 1Yu Zhou Yu Zhou 2*Ruiheng Wu Ruiheng Wu 3Aiping Yang Aiping Yang 4Qiang Li Qiang Li 1*
  • 1 School of Microelectronics, Tianjin University, Tianjin, China
  • 2 School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, Henan Province, China
  • 3 Department of Electronic and Electrical Engineering, College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge, England, United Kingdom
  • 4 School of Electrical and Information Engineering, Tianjin University, Tianjin, China

The final, formatted version of the article will be published soon.

    Scalp electroencephalography (EEG) is commonly used to assist in epilepsy detection.Even automated detection algorithms are already available to assist clinicians in reviewing EEG data, many algorithms used for seizure detection in epilepsy fail to account for the contributions of different channels. The Fully Convolutional Network (FCN) can provide the model's interpretability but has not been applied in seizure detection. To address these challenges, a novel convolutional neural network (CNN) model, combining SE (Squeeze-and-Excitation) modules, was proposed on top of the FCN. The epilepsy detection performance for patient-independent was evaluated on the CHB-MIT dataset. Then, the SE module was removed from the model and integrated the model with Inception, ResNet, and CBAM modules separately. The method showed superior advancement, stability, and reliability compared to the other three methods. The method demonstrated a G-Mean of 82.7% for sensitivity (SEN) and specificity (SPE) on the CHB-MIT dataset. In addition, The contributions of each channel to the seizure detection task have also been quantified, which led us to find that the FZ, CZ, PZ, FT9, FT10, and T8 brain regions have a more pronounced impact on epileptic seizures. This article presents a novel algorithm for epilepsy detection that accurately identifies seizures in different patients and enhances the model's interpretability.

    Keywords: Seizure detection, EEG, Deconvolution network, Interpretability analysis, deep learning Application of Deconvolutional Networks for Feature Interpretability in Epilepsy Detection

    Received: 04 Dec 2024; Accepted: 31 Dec 2024.

    Copyright: © 2024 Shao, Zhou, Wu, Yang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Yu Zhou, School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, 471003, Henan Province, China
    Qiang Li, School of Microelectronics, Tianjin University, Tianjin, China

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