Improving automated diagnosis of epilepsy from EEGs beyond IEDs

Improving automated diagnosis of epilepsy from EEGs beyond IEDs

Thangavel, Prasanth (Nanyang Technological University)
Thomas, John (McGill University, Montreal Neurological Institute and Hospital)
Sinha, Nishant (University of Pennsylvania)
Peh, Wei Yan (Nanyang Technological University)
Yuvaraj, Rajamanickam (National Institute of Education)
Cash, Sydney S. (Harvard Medical School)
Chaudhari, Rima (Fortis Hospital Mulund, Mumbai)
Karia, Sagar (Lokmanya Tilak Municipal General Hospital, Mumbai)
Dauwels, J.H.G. (TU Delft Signal Processing Systems; Nanyang Technological University)

2022

Objective. Clinical diagnosis of epilepsy relies partially on identifying interictal epileptiform discharges (IEDs) in scalp electroencephalograms (EEGs). This process is expert-biased, tedious, and can delay the diagnosis procedure. Beyond automatically detecting IEDs, there are far fewer studies on automated methods to differentiate epileptic EEGs (potentially without IEDs) from normal EEGs. In addition, the diagnosis of epilepsy based on a single EEG tends to be low. Consequently, there is a strong need for automated systems for EEG interpretation. Traditionally, epilepsy diagnosis relies heavily on IEDs. However, since not all epileptic EEGs exhibit IEDs, it is essential to explore IED-independent EEG measures for epilepsy diagnosis. The main objective is to develop an automated system for detecting epileptic EEGs, both with or without IEDs. In order to detect epileptic EEGs without IEDs, it is crucial to include EEG features in the algorithm that are not directly related to IEDs. Approach. In this study, we explore the background characteristics of interictal EEG for automated and more reliable diagnosis of epilepsy. Specifically, we investigate features based on univariate temporal measures (UTMs), spectral, wavelet, Stockwell, connectivity, and graph metrics of EEGs, besides patient-related information (age and vigilance state). The evaluation is performed on a sizeable cohort of routine scalp EEGs (685 epileptic EEGs and 1229 normal EEGs) from five centers across Singapore, USA, and India. Main results. In comparison with the current literature, we obtained an improved Leave-One-Subject-Out (LOSO) cross-validation (CV) area under the curve (AUC) of 0.871 (Balanced Accuracy (BAC) of 80.9%) with a combination of three features (IED rate, and Daubechies and Morlet wavelets) for the classification of EEGs with IEDs vs. normal EEGs. The IED-independent feature UTM achieved a LOSO CV AUC of 0.809 (BAC of 74.4%). The inclusion of IED-independent features also helps to improve the EEG-level classification of epileptic EEGs with and without IEDs vs. normal EEGs, achieving an AUC of 0.822 (BAC of 77.6%) compared to 0.688 (BAC of 59.6%) for classification only based on the IED rate. Specifically, the addition of IED-independent features improved the BAC by 21% in detecting epileptic EEGs that do not contain IEDs. Significance. These results pave the way towards automated detection of epilepsy. We are one of the first to analyze epileptic EEGs without IEDs, thereby opening up an underexplored option in epilepsy diagnosis.

deep learning
EEG classification
electroencephalogram (EEG)
epilepsy diagnosis
interictal epileptiform discharges (IEDs)
multi-center study
wavelets

http://resolver.tudelft.nl/uuid:f2544f06-f78f-495d-922a-57f2c5d088b2

https://doi.org/10.1088/1741-2552/ac9c93

2023-07-01

1741-2560

Journal of Neural Engineering, 19 (6)

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2022 Prasanth Thangavel, John Thomas, Nishant Sinha, Wei Yan Peh, Rajamanickam Yuvaraj, Sydney S. Cash, Rima Chaudhari, Sagar Karia, J.H.G. Dauwels, More Authors