Data-driven electrophysiological feature based on deep learning to detect epileptic seizures
Year: 2021
Type: article
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.
Source: Journal of Neural Engineering
Institutions: Osaka University Hospital, The University of Osaka
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $2,850
Funders: Japan Agency for Medical Research and Development, National Institute of Biomedical Innovation, Council for Science, Technology and Innovation, National Institutes of Biomedical Innovation, Health and Nutrition, Japan Society for the Promotion of Science, Japan Science and Technology Agency, Core Research for Evolutional Science and Technology, Precursory Research for Embryonic Science and Technology, Exploratory Research for Advanced Technology, Moonshot Research and Development Program
Awards: G1298592547, Enhancement of rehabilitation for spinal cord injury, G1877752588, Extraction of new electrophysiological feature for the prediction of epileptic seizures, G6091383776, Visualizing neural represeantation of mental imagery, Identifying the electrical fingerprint of epileptic network, G831266468, G899222191, G950405879
OpenAlex ID: W3198453802