#Magna graecia university software

Psychogenic non-epileptic seizures-definition, etiology, treatment and prognostic issues: a critical review Seizure 2009 18 8 543 553 10.1016/j.seizure.2009.06.006 Google Scholar 2.Bishop, C.M., Nasrabadi, N.M.: Pattern Recognition and Machine Learning, vol.Baslet G Roiko A Prensky E Heterogeneity in psychogenic nonepileptic seizures: understanding the role of psychiatric and neurological factors Epilepsy Behav. 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%.

#Magna graecia university software

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. In this case, it is unnecessary to induce seizures in the subjects. Our paper aims to define a novel methodology to support the clinical diagnosis of PNES by analyzing electroencephalographic (EEG) signals obtained in resting conditions. Diagnosis requires an EEG video, but this methodology is very time-consuming and dispensable in both time and cost. PNES subjects show the same symptoms as epileptic patients but do not have an EEG characterized by ictal patterns during psychogenic seizures.

Psychogenic Non-Epileptic Seizure (PNES) represents a neurological disorder often diagnosed and pharmacologically treated as epilepsy.