Machine-learning-based diagnostics of EEG pathology.

NeuroImage
Lukas A W GemeinTonio Ball

Abstract

Machine learning (ML) methods have the potential to automate clinical EEG analysis. They can be categorized into feature-based (with handcrafted features), and end-to-end approaches (with learned features). Previous studies on EEG pathology decoding have typically analyzed a limited number of features, decoders, or both. For a I) more elaborate feature-based EEG analysis, and II) in-depth comparisons of both approaches, here we first develop a comprehensive feature-based framework, and then compare this framework to state-of-the-art end-to-end methods. To this aim, we apply the proposed feature-based framework and deep neural networks including an EEG-optimized temporal convolutional network (TCN) to the task of pathological versus non-pathological EEG classification. For a robust comparison, we chose the Temple University Hospital (TUH) Abnormal EEG Corpus (v2.0.0), which contains approximately 3000 EEG recordings. The results demonstrate that the proposed feature-based decoding framework can achieve accuracies on the same level as state-of-the-art deep neural networks. We find accuracies across both approaches in an astonishingly narrow range from 81 to 86%. Moreover, visualizations and analyses indicated that both approaches...Continue Reading

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Citations

Nov 13, 2020·Journal of Neural Engineering·Hubert BanvilleAlexandre Gramfort
May 5, 2021·Computers in Biology and Medicine·Rishabh BajpaiA Amalin Prince
Sep 4, 2021·Yearbook of Medical Informatics·William HsuUNKNOWN Section Editors of the IMIA Yearbook Section on Sensors, Signals, and Imaging Informatics
Oct 16, 2021·GigaScience·Kamalaker DadiDenis Engemann

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