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Spatiotemporal analysis of interictal EEG for automated seizure detection and classification

Joshi, RK and Varun Kumar, M and Agrawal, M and Rao, A and Mohan, L and Jayachandra, M and Pandya, HJ (2023) Spatiotemporal analysis of interictal EEG for automated seizure detection and classification. In: Biomedical Signal Processing and Control, 79 .

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Official URL: https://doi.org/10.1016/j.bspc.2022.104086


Objective: Seizure type classification is important as therapy differs for different epilepsy subtypes. Currently, skilled neurologists classify seizures based on visual analysis. However, manual EEG inspection is time-consuming, laborious, subjective, and prone to misclassification due to artifacts and EEG variability. This work aims to address these limitations. Methods: In this work, a quick, robust, and accurate spatiotemporal analytical algorithm is developed to classify epileptic seizures. The EEG data set is sampled at 125 Hz using a Nicolet EEG system. Robust preprocessing, feature extraction, and optimal classifiers captured IEDs (Interictal Epileptiform Discharges), including spikes, sharps, slow waves, and Spike-Wave Discharges (SWD). Results: The developed classifier results are validated against clinical impressions provided by experienced epileptologists. The algorithm automatically classifies the EEG data into four types: normal, focal, generalized, and absence, with 93.18 accuracy (n = 88). Conclusion: The results suggest a novel way to screen epileptic subjects without false positives (accuracy: 94.32 , n = 88) and tentatively identify the seizure type. Blind validation further confirms the generalizability of the classifier (accuracy: 90.90 , n = 11). Significance: The developed algorithm reduces the workload of neurologists for epilepsy screening and identifies seizure onset zone, temporal spread, and overall scalp distribution of epileptic activities. © 2022 Elsevier Ltd

Item Type: Journal Article
Publication: Biomedical Signal Processing and Control
Publisher: Elsevier Ltd
Additional Information: The copyright for this article belongs to Elsevier Ltd.
Keywords: Electroencephalography, EEG spatiotemporal analyse; Epilepsy screening; Epileptiform discharges; Interictal epileptiform discharge; Misclassifications; Seizure type classification; Seizure-detection; Spatiotemporal analysis; Type classifications; Visual analysis, Neurology
Department/Centre: Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology)
Division of Interdisciplinary Sciences > Centre for Biosystems Science and Engineering
Date Deposited: 07 Sep 2022 16:29
Last Modified: 07 Sep 2022 16:29
URI: https://eprints.iisc.ac.in/id/eprint/76425

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