Psychogenic non-epileptic seizures (PNES) resemble epileptic seizures, but they do not show the characteristic electrical discharges associated with epileptic seizures. Long term video monitoring combined with EEG recording is the gold standard in clinical practice, but this methodology is quite expensive and time-consuming. This paper presents a software pipeline to discriminate short-term interictal EEG from PNES and epileptic patients. The pipeline supports EEG signals pre-processing, features selection and classification. A first case study concerning the classification of 75 EEG (healthy, PNES and epileptic subjects) is under evaluation.

A software pipeline for pre-processing and mining EEG signals: Application in neurology

Zucco C.;Calabrese B.;Sturniolo M.;Gambardella A.;Cannataro M.
2021-01-01

Abstract

Psychogenic non-epileptic seizures (PNES) resemble epileptic seizures, but they do not show the characteristic electrical discharges associated with epileptic seizures. Long term video monitoring combined with EEG recording is the gold standard in clinical practice, but this methodology is quite expensive and time-consuming. This paper presents a software pipeline to discriminate short-term interictal EEG from PNES and epileptic patients. The pipeline supports EEG signals pre-processing, features selection and classification. A first case study concerning the classification of 75 EEG (healthy, PNES and epileptic subjects) is under evaluation.
2021
EEG
Machine learning
Pre-processing
Psychogenic non-epileptic seizures
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12317/74723
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