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

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

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-sta...

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Bibliographic Details
Main Authors: Giuseppe Varone, Wadii Boulila, Michele Lo Giudice, Bilel Benjdira, Nadia Mammone, Cosimo Ieracitano, Kia Dashtipour, Sabrina Neri, Sara Gasparini, Francesco Carlo Morabito, Amir Hussain, Umberto Aguglia
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Sensors
Subjects:
psychogenic non-epileptic seizures
power spectral density
phase lag index
rest-machine learning-based diagnosis
EEG-based machine learning techniques for PNES
Online Access:https://www.mdpi.com/1424-8220/22/1/129

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https://www.mdpi.com/1424-8220/22/1/129

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