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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).
Psychogenic non-epileptic seizures (PNES) may resemble epileptic seizures but are not caused by epileptic activity. However, the analysis of electroencephalogram (EEG) signals with entropy algorithms could help identify patterns that differentiate PNES and epilepsy. Furthermore, the use of machine learning could reduce the current diagnosis costs by automating classification. The current study extracted the approximate sample, spectral, singular value decomposition, and Renyi entropies from interictal EEGs and electrocardiograms (ECG)s of 48 PNES and 29 epilepsy subjects in the broad, delta, theta, alpha, beta, and gamma frequency bands. Each feature-band pair was classified by a support vector machine (SVM), k-nearest neighbour (kNN), random forest (RF), and gradient boosting machine (GBM). In most cases, the broad band returned higher accuracy, gamma returned the lowest, and combining the six bands together improved classifier performance. The Renyi entropy was the best feature and returned high accuracy in every band. The highest balanced accuracy, 95.03%, was obtained by the kNN with Renyi entropy and combining all bands except broad. This analysis showed that entropy measures can differentiate between interictal PNES and epilepsy with high accuracy, and improved performances indicate that combining bands is an effective improvement for diagnosing PNES from EEGs and ECGs.
Author(s): Hinchliffe C, Yoarajah M, Elkommos S, Tang H, Abasolo D
Publication type: Article
Publication status: Published
Journal: Entropy
Year: 2022
Volume: 24
Issue: 10
Online publication date: 23/09/2022
Acceptance date: 17/09/2022
Date deposited: 21/02/2024
ISSN (electronic): 1099-4300
Publisher: MDPI
URL: https://doi.org/10.3390/e24101348
DOI: 10.3390/e24101348
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