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Localization of Epileptic Foci by Using Convolutional Neural Network Based on iEEG

Abstract : Epileptic focus localization is a critical factor for successful surgical therapy of resection of epileptogenic tissues. The key challenging problem of focus localization lies in the accurate classification of focal and non-focal intracranial electroencephalogram (iEEG). In this paper, we introduce a new method based on short time Fourier transform (STFT) and convolutional neural networks (CNN) to improve the classification accuracy. More specifically, STFT is employed to obtain the time-frequency spectrograms of iEEG signals, from which CNN is applied to extract features and perform classification. The time-frequency spectrograms are normalized with Z-score normalization before putting into this network. Experimental results show that our method is able to differentiate the focal from non-focal iEEG signals with an average classification accuracy of 91.8%.
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Contributor : Hal Ifip <>
Submitted on : Thursday, October 24, 2019 - 12:51:38 PM
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Linfeng Sui, Xuyang Zhao, Qibin Zhao, Toshihisa Tanaka, Jianting Cao. Localization of Epileptic Foci by Using Convolutional Neural Network Based on iEEG. 15th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), May 2019, Hersonissos, Greece. pp.331-339, ⟨10.1007/978-3-030-19823-7_27⟩. ⟨hal-02331333⟩



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