A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction

Abstract Seizure prediction may improve the quality of life of patients suffering from drug-resistant epilepsy, which accounts for about 30% of the total epileptic patients. The pre-ictal period determination, characterized by a transitional stage between normal brain activity and seizure, is a crit...

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Autores principales:	Mauro. F. Pinto, Adriana Leal, Fábio Lopes, António Dourado, Pedro Martins, César A. Teixeira
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b09fbc6a2ded4e698b2cfc42fa8d5983
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spelling	oai:doaj.org-article:b09fbc6a2ded4e698b2cfc42fa8d59832021-12-02T12:09:51ZA personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction10.1038/s41598-021-82828-72045-2322https://doaj.org/article/b09fbc6a2ded4e698b2cfc42fa8d59832021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82828-7https://doaj.org/toc/2045-2322Abstract Seizure prediction may improve the quality of life of patients suffering from drug-resistant epilepsy, which accounts for about 30% of the total epileptic patients. The pre-ictal period determination, characterized by a transitional stage between normal brain activity and seizure, is a critical step. Past approaches failed to attain real-world applicability due to lack of generalization capacity. More recently, deep learning techniques may outperform traditional classifiers and handle time dependencies. However, despite the existing efforts for providing interpretable insights, clinicians may not be willing to make high-stake decisions based on them. Furthermore, a disadvantageous aspect of the more usual seizure prediction pipeline is its modularity and significant independence between stages. An alternative could be the construction of a search algorithm that, while considering pipeline stages’ synergy, fine-tunes the selection of a reduced set of features that are widely used in the literature and computationally efficient. With extracranial recordings from 19 patients suffering from temporal-lobe seizures, we developed a patient-specific evolutionary optimization strategy, aiming to generate the optimal set of features for seizure prediction with a logistic regression classifier, which was tested prospectively in a total of 49 seizures and 710 h of continuous recording and performed above chance for 32% of patients, using a surrogate predictor. These results demonstrate the hypothesis of pre-ictal period identification without the loss of interpretability, which may help understanding brain dynamics leading to seizures and improve prediction algorithms.Mauro. F. PintoAdriana LealFábio LopesAntónio DouradoPedro MartinsCésar A. TeixeiraNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mauro. F. Pinto Adriana Leal Fábio Lopes António Dourado Pedro Martins César A. Teixeira A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction
description	Abstract Seizure prediction may improve the quality of life of patients suffering from drug-resistant epilepsy, which accounts for about 30% of the total epileptic patients. The pre-ictal period determination, characterized by a transitional stage between normal brain activity and seizure, is a critical step. Past approaches failed to attain real-world applicability due to lack of generalization capacity. More recently, deep learning techniques may outperform traditional classifiers and handle time dependencies. However, despite the existing efforts for providing interpretable insights, clinicians may not be willing to make high-stake decisions based on them. Furthermore, a disadvantageous aspect of the more usual seizure prediction pipeline is its modularity and significant independence between stages. An alternative could be the construction of a search algorithm that, while considering pipeline stages’ synergy, fine-tunes the selection of a reduced set of features that are widely used in the literature and computationally efficient. With extracranial recordings from 19 patients suffering from temporal-lobe seizures, we developed a patient-specific evolutionary optimization strategy, aiming to generate the optimal set of features for seizure prediction with a logistic regression classifier, which was tested prospectively in a total of 49 seizures and 710 h of continuous recording and performed above chance for 32% of patients, using a surrogate predictor. These results demonstrate the hypothesis of pre-ictal period identification without the loss of interpretability, which may help understanding brain dynamics leading to seizures and improve prediction algorithms.
format	article
author	Mauro. F. Pinto Adriana Leal Fábio Lopes António Dourado Pedro Martins César A. Teixeira
author_facet	Mauro. F. Pinto Adriana Leal Fábio Lopes António Dourado Pedro Martins César A. Teixeira
author_sort	Mauro. F. Pinto
title	A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction
title_short	A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction
title_full	A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction
title_fullStr	A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction
title_full_unstemmed	A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction
title_sort	personalized and evolutionary algorithm for interpretable eeg epilepsy seizure prediction
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b09fbc6a2ded4e698b2cfc42fa8d5983
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A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction

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