Please use this identifier to cite or link to this item: https://observatorio.fm.usp.br/handle/OPI/47467
Title: Differentiation between Epileptic and Psychogenic Nonepileptic Seizures in Electroencephalogram Using Wavelets and Support-Vector Machines
Authors: SANTOS, Kaue Reis dosSOUSA, Miguel Angelo de Abreu deSANTOS, Sara Dereste dosPIRES, RicardoTHOME-SOUZA, Sigride
Citation: APPLIED ARTIFICIAL INTELLIGENCE, v.36, n.1, 2022
Abstract: The number of people suffering from epilepsy in the world exceeds 50 million and around 20% of this group refers to patients who have psychogenic nonepileptic seizures (PNES). Unlike epilepsy, PNES requires psychological treatment. When not correctly diagnosed, these patients can be submitted to a treatment based on antiepileptic drugs besides specific procedures for epilepsy. In this work, we propose the identification of patients with PNES from those with epilepsy using electroencephalogram (EEG) signals. Discrete Wavelet Transform (DWT) decomposition and a Support-Vector Machine (SVM) classifier were employed. Common types of wavelet families and SVM kernels were combined and compared. The results obtained for accuracy, sensitivity, and specificity are equal to 100% for the set of configuration parameters composed of windows encompassing whole seizures, wavelet Coiflet 1, and SVM kernel sigmoid or RBF. The proposed method is efficient and feasible to be applied to new patients admitted in a hospital center, even without having their previous EEG signals already collected. The main advantages of the proposed work are not requiring the use of accelerometer nor electromyographic signals, not being patient specific and outperforming other works results.
Appears in Collections:

Artigos e Materiais de Revistas Científicas - HC/IPq
Instituto de Psiquiatria - HC/IPq


Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.