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Title: Interpretable EEG seizure prediction using a multiobjective evolutionary algorithm
Authors: Pinto, Mauro F. 
Coelho, Tiago 
Leal, Adriana 
Lopes, Fábio 
Dourado, António 
Martins, Pedro 
Teixeira, César A. 
Issue Date: 2022
Publisher: Nature Research
Project: CISUC - UID/CEC/00326/2020 
Ph.D. grant SFRH/ BD/139757/2018 
Serial title, monograph or event: Scientific Reports
Volume: 12
Issue: 1
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 interictal 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.
ISSN: 2045-2322
DOI: 10.1038/s41598-022-08322-w
Rights: openAccess
Appears in Collections:I&D CISUC - Artigos em Revistas Internacionais

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