Interpretable EEG seizure prediction using a multiobjective evolutionary algorithm
Abstract: Seizure prediction might be the solution to tackle the apparent unpredictability of seizures in patients with drug-resistant epilepsy, which comprise about a third of all patients with epilepsy. Designing seizure prediction models involves defining the pre-ictal period, a transition stage between inter-ictal brain activity and the seizure discharge. This period is typically a fixed interval, with some recent studies reporting the evaluation of different patient-specific pre-ictal intervals. Recently, researchers have aimed to determine the pre-ictal period, a transition stage between regular brain activity and a seizure. Authors have been using deep learning models given the ability of such models to automatically perform pre-processing, feature extraction, classification, and handling temporal and spatial dependencies. As these approaches create black-box models, clinicians may not have sufficient trust to use them in high-stake decisions. By considering these problems, we developed an evolutionary seizure prediction model that identifies the best set of features while automatically searching for the pre-ictal period and accounting for patient comfort. This methodology provides patient-specific interpretable insights, which might contribute to a better understanding of seizure generation processes and explain the algorithm’s decisions. We tested our methodology on 238 seizures and 3687 h of continuous data, recorded on scalp recordings from 93 patients with several types of focal and generalised epilepsies. We compared the results with a seizure surrogate predictor and obtained a performance above chance for 32% patients. We also compared our results with a control method based on the standard machine learning pipeline (pre-processing, feature extraction, classifier training, and post-processing), where the control marginally outperformed our approach by validating 35% of the patients. In total, 54 patients performed above chance for at least one method: our methodology or the control one. Of these 54 patients, 21 (38%) were solely validated by our methodology, while 24 (44%) were only validated by the control method. These findings may evidence the need for different methodologies concerning different patients
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Anmerkungen
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ISSN: 2045-2322
- Ereignis
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Veröffentlichung
- (wo)
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Freiburg
- (wer)
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Universität
- (wann)
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2024
- Urheber
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Pinto, Mauro F.
Coelho, Tiago
Leal, Adriana
Lopes, Fabio
Dourado, Antonio
Martins, Pedro
Teixeira, César Alexandre
- DOI
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10.1038/s41598-022-08322-w
- URN
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urn:nbn:de:bsz:25-freidok-2449698
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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25.03.2025, 13:51 MEZ
Datenpartner
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Beteiligte
- Pinto, Mauro F.
- Coelho, Tiago
- Leal, Adriana
- Lopes, Fabio
- Dourado, Antonio
- Martins, Pedro
- Teixeira, César Alexandre
- Universität
Entstanden
- 2024