Resting-State EEG Classification for PNES Diagnosis

Psychogenic Non-Epileptic Seizure (PNES) represents a neurological disorder often diagnosed and pharmacologically treated as epilepsy. PNES subjects show the same symptoms as epileptic patients but do not have an EEG characterized by ictal patterns during psychogenic seizures. Diagnosis requires an EEG video, but this methodology is very time-consuming and dispensable in both time and cost. Our paper aims to define a novel methodology to support the clinical diagnosis of PNES by analyzing electroencephalographic (EEG) signals obtained in resting conditions. In this case, it is unnecessary to induce seizures in the subjects. A software pipeline was implemented based on robust feature extraction methods used in quantitative EEG analysis in the clinical setting, integrating them with machine learning classifiers. Unlike other similar works, the methodology was tested on a large dataset consisting of 225 EEGs (75 healthy, 75 PNES and 75 subjects with epilepsy), showing that it has a classification accuracy greater than 85%.