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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...
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/PMC9687589/ https://www.ncbi.nlm.nih.gov/pubmed/36421091 http://dx.doi.org/10.3390/bioengineering9110690 |
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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 |
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