Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/111195
Title: Removing artefacts and periodically retraining improve performance of neural network-based seizure prediction models
Authors: Lopes, Fábio 
Leal, Adriana 
Pinto, Mauro F. 
Dourado, António 
Schulze-Bonhage, Andreas
Dümpelmann, Matthias
Teixeira, César A. D. 
Issue Date: 11-Apr-2023
Publisher: Springer Nature
Project: FCT - CISUC - UID/CEC/00326/2020 with funds from the European Social Fund, through the Regional Operational Program Centro 2020; 
project RECoD - PTDC/EEI-EEE/5788/2020 financed with national funds (PIDDAC) via the Portuguese State Budget. 
Ph.D. grant 2020.04537.BD 
metadata.degois.publication.title: Scientific Reports
metadata.degois.publication.volume: 13
metadata.degois.publication.issue: 1
Abstract: The development of seizure prediction models is often based on long-term scalp electroencephalograms (EEGs) since they capture brain electrical activity, are non-invasive, and come at a relatively low-cost. However, they suffer from major shortcomings. First, long-term EEG is usually highly contaminated with artefacts. Second, changes in the EEG signal over long intervals, known as concept drift, are often neglected. We evaluate the influence of these problems on deep neural networks using EEG time series and on shallow neural networks using widely-used EEG features. Our patient-specific prediction models were tested in 1577 hours of continuous EEG, containing 91 seizures from 41 patients with temporal lobe epilepsy who were undergoing pre-surgical monitoring. Our results showed that cleaning EEG data, using a previously developed artefact removal method based on deep convolutional neural networks, improved prediction performance. We also found that retraining the models over time reduced false predictions. Furthermore, the results show that although deep neural networks processing EEG time series are less susceptible to false alarms, they may need more data to surpass feature-based methods. These findings highlight the importance of robust data denoising and periodic adaptation of seizure prediction models.
URI: https://hdl.handle.net/10316/111195
ISSN: 2045-2322
DOI: 10.1038/s41598-023-30864-w
Rights: openAccess
Appears in Collections:I&D CISUC - Artigos em Revistas Internacionais

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