EEG Complexity Analysis of Psychogenic Non-Epileptic and Epileptic Seizures Using Entropy and Machine Learning.

Hesam Shokouh Alaei, Samaneh Kouchaki, Mahinda Yogarajah, Daniel Abasolo

Abstract read

In one paragraph

Article in Entropy (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Functional/dissociative seizures as a neuroenergetic deficit syndrome: a brain economy failure hypothesis.Frontiers in neurology
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Hesam Shokouh AlaeiCentre for Biomedical Engineering, School of Engineering, University of Surrey, Guildford GU2 7XH, UK.ORCID 0000-0002-7920-7850

Samaneh KouchakiCentre for Vision, Speech and Signal Processing (CVSSP), University of Surrey, Guildford GU2 7XH, UK.ORCID 0000-0002-5870-4030

Mahinda YogarajahDepartment of Clinical and Experimental Epilepsy, Institute of Neurology, University College London, London WC1N 3BG, UK.

Daniel AbasoloCentre for Biomedical Engineering, School of Engineering, University of Surrey, Guildford GU2 7XH, UK.ORCID 0000-0002-4268-2885

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 (

Indexed as

entropyepileptic seizuresmachine learningpreictal and interictal analysispsychogenic non-epileptic seizures

Identifiers

PMID41149002

DOI10.3390/e27101044

PMCPMC12564079

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.