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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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 |
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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 |
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