Shokouh Alaei, Hesam;
Kouchaki, Samaneh;
Yogarajah, Mahinda;
Abasolo, Daniel;
(2025)
EEG Complexity Analysis of Psychogenic Non-Epileptic and Epileptic Seizures Using Entropy and Machine Learning.
Entropy
, 27
(10)
, Article 1044. 10.3390/e27101044.
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EEG Complexity Analysis of Psychogenic Non-Epileptic and Epileptic Seizures Using Entropy and Machine Learning.pdf - Accepted Version Download (4MB) | Preview |
Abstract
Psychogenic non-epileptic seizures (PNES) are often misdiagnosed as epileptic seizures (ES), leading to inappropriate treatment and delayed psychological care. To address this challenge, we analysed electroencephalogram (EEG) data from 74 patients (46 PNES, 28 ES) using one-minute preictal and interictal recordings per subject. Nine entropy measures (Sample, Fuzzy, Permutation, Dispersion, Conditional, Phase, Spectral, Rényi, and Wavelet entropy) were evaluated individually to classify PNES from ES using k-nearest neighbours, Naïve Bayes, linear discriminant analysis, logistic regression, support vector machine, random forest, multilayer perceptron, and XGBoost within a leave-one-subject-out cross-validation framework. In addition, a dynamic state, defined as the entropy difference between interictal and preictal periods, was examined. Sample, Fuzzy, Conditional, and Dispersion entropy were higher in PNES than in ES during interictal recordings (not significant), but significantly lower in the preictal (p < 0.05) and dynamic states (p < 0.01). Spatial mapping and permutation-based importance analyses highlighted O1, O2, T5, F7, and Pz as key discriminative channels. Classification performance peaked in the dynamic state, with Fuzzy entropy and support vector machine achieving the best results (balanced accuracy = 72.4%, F1 score = 77.8%, sensitivity = 74.5%, specificity = 70.4%). These results demonstrate the potential of entropy features for differentiating PNES from ES.
| Type: | Article |
|---|---|
| Title: | EEG Complexity Analysis of Psychogenic Non-Epileptic and Epileptic Seizures Using Entropy and Machine Learning |
| Location: | Switzerland |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3390/e27101044 |
| Publisher version: | https://doi.org/10.3390/e27101044 |
| Language: | English |
| Additional information: | © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). |
| Keywords: | psychogenic non-epileptic seizures; epileptic seizures; entropy; preictal and interictal analysis; machine learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10220279 |
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