Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/105444
Title: A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction
Authors: Pinto, Mauro F. 
Leal, Adriana 
Lopes, Fábio 
Dourado, António 
Martins, Pedro 
Teixeira, César A. 
Issue Date: 9-Feb-2021
Publisher: Springer Nature
Project: CISUC—UID/CEC/00326/2020 
PhD Grant SFRH/BD/139757/2018 
Serial title, monograph or event: Scientific Reports
Volume: 11
Issue: 1
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.
URI: https://hdl.handle.net/10316/105444
ISSN: 2045-2322
DOI: 10.1038/s41598-021-82828-7
Rights: openAccess
Appears in Collections:I&D CISUC - Artigos em Revistas Internacionais

Show full item record

SCOPUSTM   
Citations

25
checked on Dec 25, 2023

WEB OF SCIENCETM
Citations

25
checked on Dec 2, 2023

Page view(s)

105
checked on Oct 2, 2024

Download(s)

44
checked on Oct 2, 2024

Google ScholarTM

Check

Altmetric

Altmetric


This item is licensed under a Creative Commons License Creative Commons