Cargando…

Diagnosis of Epilepsy with Functional Connectivity in EEG after a Suspected First Seizure

Epilepsy is regarded as a structural and functional network disorder, affecting around 50 million people worldwide. A correct disease diagnosis can lead to quicker medical action, preventing adverse effects. This paper reports the design of a classifier for epilepsy diagnosis in patients after a fir...

Descripción completa

Detalles Bibliográficos
Autores principales: Matos, João, Peralta, Guilherme, Heyse, Jolan, Menetre, Eric, Seeck, Margitta, van Mierlo, Pieter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9687589/
https://www.ncbi.nlm.nih.gov/pubmed/36421091
http://dx.doi.org/10.3390/bioengineering9110690
_version_ 1784836044070322176
author Matos, João
Peralta, Guilherme
Heyse, Jolan
Menetre, Eric
Seeck, Margitta
van Mierlo, Pieter
author_facet Matos, João
Peralta, Guilherme
Heyse, Jolan
Menetre, Eric
Seeck, Margitta
van Mierlo, Pieter
author_sort Matos, João
collection PubMed
description Epilepsy is regarded as a structural and functional network disorder, affecting around 50 million people worldwide. A correct disease diagnosis can lead to quicker medical action, preventing adverse effects. This paper reports the design of a classifier for epilepsy diagnosis in patients after a first ictal episode, using electroencephalogram (EEG) recordings. The dataset consists of resting-state EEG from 629 patients, of which 504 were retained for the study. The patient’s cohort exists out of 291 patients with epilepsy and 213 patients with other pathologies. The data were split into two sets: 80% training set and 20% test set. The extracted features from EEG included functional connectivity measures, graph measures, band powers and brain asymmetry ratios. Feature reduction was performed, and the models were trained using Machine Learning (ML) techniques. The models’ evaluation was performed with the area under the receiver operating characteristic curve (AUC). When focusing specifically on focal lesional epileptic patients, better results were obtained. This classification task was optimized using a 5-fold cross-validation, where SVM using PCA for feature reduction achieved an AUC of 0.730 ± 0.030. In the test set, the same model achieved 0.649 of AUC. The verified decrease is justified by the considerable diversity of pathologies in the cohort. An analysis of the selected features across tested models shows that functional connectivity and its graph measures have the most considerable predictive power, along with full-spectrum frequency-based features. To conclude, the proposed algorithms, with some refinement, can be of added value for doctors diagnosing epilepsy from EEG recordings after a suspected first seizure.
format Online
Article
Text
id pubmed-9687589
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-96875892022-11-25 Diagnosis of Epilepsy with Functional Connectivity in EEG after a Suspected First Seizure Matos, João Peralta, Guilherme Heyse, Jolan Menetre, Eric Seeck, Margitta van Mierlo, Pieter Bioengineering (Basel) Article Epilepsy is regarded as a structural and functional network disorder, affecting around 50 million people worldwide. A correct disease diagnosis can lead to quicker medical action, preventing adverse effects. This paper reports the design of a classifier for epilepsy diagnosis in patients after a first ictal episode, using electroencephalogram (EEG) recordings. The dataset consists of resting-state EEG from 629 patients, of which 504 were retained for the study. The patient’s cohort exists out of 291 patients with epilepsy and 213 patients with other pathologies. The data were split into two sets: 80% training set and 20% test set. The extracted features from EEG included functional connectivity measures, graph measures, band powers and brain asymmetry ratios. Feature reduction was performed, and the models were trained using Machine Learning (ML) techniques. The models’ evaluation was performed with the area under the receiver operating characteristic curve (AUC). When focusing specifically on focal lesional epileptic patients, better results were obtained. This classification task was optimized using a 5-fold cross-validation, where SVM using PCA for feature reduction achieved an AUC of 0.730 ± 0.030. In the test set, the same model achieved 0.649 of AUC. The verified decrease is justified by the considerable diversity of pathologies in the cohort. An analysis of the selected features across tested models shows that functional connectivity and its graph measures have the most considerable predictive power, along with full-spectrum frequency-based features. To conclude, the proposed algorithms, with some refinement, can be of added value for doctors diagnosing epilepsy from EEG recordings after a suspected first seizure. MDPI 2022-11-14 /pmc/articles/PMC9687589/ /pubmed/36421091 http://dx.doi.org/10.3390/bioengineering9110690 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
Matos, João
Peralta, Guilherme
Heyse, Jolan
Menetre, Eric
Seeck, Margitta
van Mierlo, Pieter
Diagnosis of Epilepsy with Functional Connectivity in EEG after a Suspected First Seizure
title Diagnosis of Epilepsy with Functional Connectivity in EEG after a Suspected First Seizure
title_full Diagnosis of Epilepsy with Functional Connectivity in EEG after a Suspected First Seizure
title_fullStr Diagnosis of Epilepsy with Functional Connectivity in EEG after a Suspected First Seizure
title_full_unstemmed Diagnosis of Epilepsy with Functional Connectivity in EEG after a Suspected First Seizure
title_short Diagnosis of Epilepsy with Functional Connectivity in EEG after a Suspected First Seizure
title_sort diagnosis of epilepsy with functional connectivity in eeg after a suspected first seizure
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9687589/
https://www.ncbi.nlm.nih.gov/pubmed/36421091
http://dx.doi.org/10.3390/bioengineering9110690
work_keys_str_mv AT matosjoao diagnosisofepilepsywithfunctionalconnectivityineegafterasuspectedfirstseizure
AT peraltaguilherme diagnosisofepilepsywithfunctionalconnectivityineegafterasuspectedfirstseizure
AT heysejolan diagnosisofepilepsywithfunctionalconnectivityineegafterasuspectedfirstseizure
AT menetreeric diagnosisofepilepsywithfunctionalconnectivityineegafterasuspectedfirstseizure
AT seeckmargitta diagnosisofepilepsywithfunctionalconnectivityineegafterasuspectedfirstseizure
AT vanmierlopieter diagnosisofepilepsywithfunctionalconnectivityineegafterasuspectedfirstseizure