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A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls

Varone, Giuseppe; Boulila, Wadii; Lo Giudice, Michele; Benjdira, Bilel; Mammone, Nadia; Ieracitano, Cosimo; Dashtipour, Kia; Neri, Sabrina; Gasparini, Sara; Morabito, Francesco Carlo; Hussain, Amir; Aguglia, Umberto

Authors

Giuseppe Varone

Wadii Boulila

Michele Lo Giudice

Bilel Benjdira

Nadia Mammone

Cosimo Ieracitano

Sabrina Neri

Sara Gasparini

Francesco Carlo Morabito

Umberto Aguglia



Abstract

Until now, clinicians are not able to evaluate the Psychogenic Non-Epileptic Seizures (PNES) from the rest-electroencephalography (EEG) readout. No EEG marker can help differentiate PNES cases from healthy subjects. In this paper, we have investigated the power spectrum density (PSD), in resting-state EEGs, to evaluate the abnormalities in PNES affected brains. Additionally, we have used functional connectivity tools, such as phase lag index (PLI), and graph-derived metrics to better observe the integration of distributed information of regular and synchronized multi-scale communication within and across inter-regional brain areas. We proved the utility of our method after enrolling a cohort study of 20 age- and gender-matched PNES and 19 healthy control (HC) subjects. In this work, three classification models, namely support vector machine (SVM), linear discriminant analysis (LDA), and Multilayer perceptron (MLP), have been employed to model the relationship between the functional connectivity features (rest-HC versus rest-PNES). The best performance for the discrimination of participants was obtained using the MLP classifier, reporting a precision of 85.73%, a recall of 86.57%, an F1-score of 78.98%, and, finally, an accuracy of 91.02%. In conclusion, our results hypothesized two main aspects. The first is an intrinsic organization of functional brain networks that reflects a dysfunctional level of integration across brain regions, which can provide new insights into the pathophysiological mechanisms of PNES. The second is that functional connectivity features and MLP could be a promising method to classify rest-EEG data of PNES form healthy controls subjects.

Journal Article Type Article
Acceptance Date Dec 17, 2021
Online Publication Date Dec 25, 2021
Publication Date 2022-01
Deposit Date Feb 1, 2022
Publicly Available Date Feb 1, 2022
Journal Sensors
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 22
Issue 1
Article Number 129
DOI https://doi.org/10.3390/s22010129
Keywords psychogenic non-epileptic seizures; power spectral density; phase lag index; rest-machine learning-based diagnosis; EEG-based machine learning techniques for PNES
Public URL http://researchrepository.napier.ac.uk/Output/2839577

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