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Lookup NU author(s): Dr Chloe HinchliffeORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Objective: Functional seizures (FS) look like epileptic seizures but are characterized by a lack of epileptic activity in the brain. Approximately one in five referrals to epilepsy clinics are diagnosed with this condition. FS are diagnosed by recording a seizure using video-electroencephalography (EEG), from which an expert inspects the semiology and the EEG. However, this method can be expensive and inaccessible and can present significant patient burden. No single biomarker has been found to diagnose FS. However, the current limitations in FS diagnosis could be improved with machine learning to classify signal features extracted from EEG, thus providing a potentially very useful aid to clinicians. Methods: The current study has investigated the use of seizure-free EEG signals with machine learning to identify subjects with FS from those with epilepsy. The dataset included interictal and preictal EEG recordings from 48 subjects with FS (mean age=34.76±10.55 years, 14 males) and 29 subjects with epilepsy (mean age=38.95±13.93 years, 18 males) from which various statistical, temporal, and spectral features from the five EEG frequency bands were extracted then analyzed with threshold accuracy, five machine learning classifiers, and two feature importance approaches. Results: The highest classification accuracy reported from thresholding was 60.67%. However, the temporal features were the best performing, with the highest balanced accuracy reported by the machine learning models: 95.71% with all frequency bands combined and a support vector machine classifier. Significance: Machine learning was much more effective than using individual features and could be a powerful aid in FS diagnosis. Furthermore, combining the frequency bands improved the accuracy of the classifiers in most cases, and the lowest performing EEG bands were consistently delta and gamma.
Author(s): Hinchliffe CHL, Yogarajah M, Elkommos S, Tang H, Abasolo D
Publication type: Article
Publication status: Published
Journal: Epilepsia
Year: 2024
Pages: epub ahead of print
Online publication date: 10/09/2024
Acceptance date: 20/08/2024
Date deposited: 10/09/2024
ISSN (print): 0013-9580
ISSN (electronic): 1528-1167
Publisher: Wiley
URL: https://onlinelibrary.wiley.com/doi/full/10.1111/epi.18110
DOI: 10.1111/epi.18110
Data Access Statement: The data used in this study are derived from routine clinical acquisitions and are not publicly available
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