Data-driven electrophysiological feature based on deep learning to detect epileptic seizures. (30th September 2021)

• Record Type: Journal Article
• Authors: Yamamoto, Shota; Yanagisawa, Takufumi; Fukuma, Ryohei; Oshino, Satoru; Tani, Naoki; Khoo, Hui Ming; Edakawa, Kohtaroh; Kobayashi, Maki; Tanaka, Masataka; Fujita, Yuya; Kishima, Haruhiko
• Is Part Of: Journal of neural engineering. Volume 18:Number 5(2021)
• Publication Date: 2021-09-30
• Subjects: data-driven epileptogenicity index -- Epi-Net -- modified integrated gradients -- epilepsy; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/ac23bf
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective . To identify a new electrophysiological feature characterising the epileptic seizures, which is commonly observed in different types of epilepsy. Methods . We recorded the intracranial electroencephalogram (iEEG) of 21 patients (12 women and 9 men) with multiple types of refractory epilepsy. The raw iEEG signals of the early phase of epileptic seizures and interictal states were classified by a convolutional neural network (Epi-Net). For comparison, the same signals were classified by a support vector machine (SVM) using the spectral power and phase–amplitude coupling. The features learned by Epi-Net were derived by a modified integrated gradients method. We considered the product of powers multiplied by the relative contribution of each frequency amplitude as a data-driven epileptogenicity index (d-EI). We compared the d-EI and other conventional features in terms of accuracy to detect the epileptic seizures. Finally, we compared the d-EI among the electrodes to evaluate its relationship with the resected area and the Engel classification. Results . Epi-Net successfully identified the epileptic seizures, with an area under the receiver operating characteristic curve of 0.944 ± 0.067, which was significantly larger than that of the SVM (0.808 ± 0.253, n = 21; p = 0.025). The learned iEEG signals were characterised by increased powers of 17–92 Hz and >180 Hz in addition to decreased powers of other frequencies. The proposed d-EI detected them with better accuracy than the other iEEG features. Moreover, the surgical resection of areas with a larger increase in d-EI was observed for all nine patients with Engel class ⩽1, but not for the 4 of 12 patients with Engel class >1, demonstrating the significant association with seizure outcomes. Significance. We derived an iEEG feature from the trained Epi-Net, which identified the epileptic seizures with improved accuracy and might contribute to identification of the epileptogenic zone.