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Title: Automated adult epilepsy diagnostic tool based on interictal scalp electroencephalogram characteristics: a six-center study
Authors: Thomas, John
Thangavel, Prasanth
Peh, Wei Yan
Jing, Jin
Yuvaraj, Rajamanickam
Cash, Sydney S.
Chaudhari, Rima
Karia, Sagar
Rathakrishnan, Rahul
Saini, Vinay
Shah, Nilesh
Srivastava, Rohit
Tan, Yee-Leng
Westover, Brandon
Dauwels, Justin
Keywords: Science::Medicine
Issue Date: 2021
Source: Thomas, J., Thangavel, P., Peh, W. Y., Jing, J., Yuvaraj, R., Cash, S. S., Chaudhari, R., Karia, S., Rathakrishnan, R., Saini, V., Shah, N., Srivastava, R., Tan, Y., Westover, B. & Dauwels, J. (2021). Automated adult epilepsy diagnostic tool based on interictal scalp electroencephalogram characteristics: a six-center study. International Journal of Neural Systems, 31(5), 2050074-.
Project: RG16/19
Journal: International Journal of Neural Systems
Abstract: The diagnosis of epilepsy often relies on a reading of routine scalp electroencephalograms (EEGs). Since seizures are highly unlikely to be detected in a routine scalp EEG, the primary diagnosis depends heavily on the visual evaluation of Interictal Epileptiform Discharges (IEDs). This process is tedious, expert-centered, and delays the treatment plan. Consequently, the development of an automated, fast, and reliable epileptic EEG diagnostic system is essential. In this study, we propose a system to classify EEG as epileptic or normal based on multiple modalities extracted from the interictal EEG. The ensemble system consists of three components: a Convolutional Neural Network (CNN)-based IED detector, a Template Matching (TM)-based IED detector, and a spectral feature-based classifier. We evaluate the system on datasets from six centers from the USA, Singapore, and India. The system yields a mean Leave-One-Institution-Out (LOIO) cross-validation (CV) area under curve (AUC) of 0.826 (balanced accuracy (BAC) of 76.1%) and Leave-One-Subject-Out (LOSO) CV AUC of 0.812 (BAC of 74.8%). The LOIO results are found to be similar to the interrater agreement (IRA) reported in the literature for epileptic EEG classification. Moreover, as the proposed system can process routine EEGs in a few seconds, it may aid the clinicians in diagnosing epilepsy efficiently.
ISSN: 0129-0657
DOI: 10.1142/S0129065720500744
Schools: Interdisciplinary Graduate School (IGS) 
Rights: © 2021 World Scientific Publishing Company. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:IGS Journal Articles

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