Cargando…

Convolutional Neural Network Classification of Rest EEG Signals among People with Epilepsy, Psychogenic Non Epileptic Seizures and Control Subjects

Identifying subjects with epileptic seizures or psychogenic non-epileptic seizures from healthy subjects via interictal EEG analysis can be a very challenging issue. Indeed, at visual inspection, EEG can be normal in both cases. This paper proposes an automatic diagnosis approach based on deep learn...

Descripción completa

Detalles Bibliográficos
Autores principales: Lo Giudice, Michele, Ferlazzo, Edoardo, Mammone, Nadia, Gasparini, Sara, Cianci, Vittoria, Pascarella, Angelo, Mammì, Anna, Mandic, Danilo, Morabito, Francesco Carlo, Aguglia, Umberto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9738351/
https://www.ncbi.nlm.nih.gov/pubmed/36497808
http://dx.doi.org/10.3390/ijerph192315733
_version_ 1784847519532974080
author Lo Giudice, Michele
Ferlazzo, Edoardo
Mammone, Nadia
Gasparini, Sara
Cianci, Vittoria
Pascarella, Angelo
Mammì, Anna
Mandic, Danilo
Morabito, Francesco Carlo
Aguglia, Umberto
author_facet Lo Giudice, Michele
Ferlazzo, Edoardo
Mammone, Nadia
Gasparini, Sara
Cianci, Vittoria
Pascarella, Angelo
Mammì, Anna
Mandic, Danilo
Morabito, Francesco Carlo
Aguglia, Umberto
author_sort Lo Giudice, Michele
collection PubMed
description Identifying subjects with epileptic seizures or psychogenic non-epileptic seizures from healthy subjects via interictal EEG analysis can be a very challenging issue. Indeed, at visual inspection, EEG can be normal in both cases. This paper proposes an automatic diagnosis approach based on deep learning to differentiate three classes: subjects with epileptic seizures (ES), subjects with non-epileptic psychogenic seizures (PNES) and control subjects (CS), analyzed by non-invasive low-density interictal scalp EEG recordings. The EEGs of 42 patients with new-onset ES, 42 patients with PNES video recorded and 19 patients with CS all with normal interictal EEG on visual inspection, were analyzed in the study; none of them was taking psychotropic drugs before registration. The processing pipeline applies empirical mode decomposition (EMD) to 5s EEG segments of 19 channels in order to extract enhanced features learned automatically from the customized convolutional neural network (CNN). The resulting CNN has been shown to perform well during classification, with an accuracy of 85.7%; these results encourage the use of deep processing systems to assist clinicians in difficult clinical settings.
format Online
Article
Text
id pubmed-9738351
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-97383512022-12-11 Convolutional Neural Network Classification of Rest EEG Signals among People with Epilepsy, Psychogenic Non Epileptic Seizures and Control Subjects Lo Giudice, Michele Ferlazzo, Edoardo Mammone, Nadia Gasparini, Sara Cianci, Vittoria Pascarella, Angelo Mammì, Anna Mandic, Danilo Morabito, Francesco Carlo Aguglia, Umberto Int J Environ Res Public Health Article Identifying subjects with epileptic seizures or psychogenic non-epileptic seizures from healthy subjects via interictal EEG analysis can be a very challenging issue. Indeed, at visual inspection, EEG can be normal in both cases. This paper proposes an automatic diagnosis approach based on deep learning to differentiate three classes: subjects with epileptic seizures (ES), subjects with non-epileptic psychogenic seizures (PNES) and control subjects (CS), analyzed by non-invasive low-density interictal scalp EEG recordings. The EEGs of 42 patients with new-onset ES, 42 patients with PNES video recorded and 19 patients with CS all with normal interictal EEG on visual inspection, were analyzed in the study; none of them was taking psychotropic drugs before registration. The processing pipeline applies empirical mode decomposition (EMD) to 5s EEG segments of 19 channels in order to extract enhanced features learned automatically from the customized convolutional neural network (CNN). The resulting CNN has been shown to perform well during classification, with an accuracy of 85.7%; these results encourage the use of deep processing systems to assist clinicians in difficult clinical settings. MDPI 2022-11-26 /pmc/articles/PMC9738351/ /pubmed/36497808 http://dx.doi.org/10.3390/ijerph192315733 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lo Giudice, Michele
Ferlazzo, Edoardo
Mammone, Nadia
Gasparini, Sara
Cianci, Vittoria
Pascarella, Angelo
Mammì, Anna
Mandic, Danilo
Morabito, Francesco Carlo
Aguglia, Umberto
Convolutional Neural Network Classification of Rest EEG Signals among People with Epilepsy, Psychogenic Non Epileptic Seizures and Control Subjects
title Convolutional Neural Network Classification of Rest EEG Signals among People with Epilepsy, Psychogenic Non Epileptic Seizures and Control Subjects
title_full Convolutional Neural Network Classification of Rest EEG Signals among People with Epilepsy, Psychogenic Non Epileptic Seizures and Control Subjects
title_fullStr Convolutional Neural Network Classification of Rest EEG Signals among People with Epilepsy, Psychogenic Non Epileptic Seizures and Control Subjects
title_full_unstemmed Convolutional Neural Network Classification of Rest EEG Signals among People with Epilepsy, Psychogenic Non Epileptic Seizures and Control Subjects
title_short Convolutional Neural Network Classification of Rest EEG Signals among People with Epilepsy, Psychogenic Non Epileptic Seizures and Control Subjects
title_sort convolutional neural network classification of rest eeg signals among people with epilepsy, psychogenic non epileptic seizures and control subjects
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9738351/
https://www.ncbi.nlm.nih.gov/pubmed/36497808
http://dx.doi.org/10.3390/ijerph192315733
work_keys_str_mv AT logiudicemichele convolutionalneuralnetworkclassificationofresteegsignalsamongpeoplewithepilepsypsychogenicnonepilepticseizuresandcontrolsubjects
AT ferlazzoedoardo convolutionalneuralnetworkclassificationofresteegsignalsamongpeoplewithepilepsypsychogenicnonepilepticseizuresandcontrolsubjects
AT mammonenadia convolutionalneuralnetworkclassificationofresteegsignalsamongpeoplewithepilepsypsychogenicnonepilepticseizuresandcontrolsubjects
AT gasparinisara convolutionalneuralnetworkclassificationofresteegsignalsamongpeoplewithepilepsypsychogenicnonepilepticseizuresandcontrolsubjects
AT ciancivittoria convolutionalneuralnetworkclassificationofresteegsignalsamongpeoplewithepilepsypsychogenicnonepilepticseizuresandcontrolsubjects
AT pascarellaangelo convolutionalneuralnetworkclassificationofresteegsignalsamongpeoplewithepilepsypsychogenicnonepilepticseizuresandcontrolsubjects
AT mammianna convolutionalneuralnetworkclassificationofresteegsignalsamongpeoplewithepilepsypsychogenicnonepilepticseizuresandcontrolsubjects
AT mandicdanilo convolutionalneuralnetworkclassificationofresteegsignalsamongpeoplewithepilepsypsychogenicnonepilepticseizuresandcontrolsubjects
AT morabitofrancescocarlo convolutionalneuralnetworkclassificationofresteegsignalsamongpeoplewithepilepsypsychogenicnonepilepticseizuresandcontrolsubjects
AT agugliaumberto convolutionalneuralnetworkclassificationofresteegsignalsamongpeoplewithepilepsypsychogenicnonepilepticseizuresandcontrolsubjects