Genetic algorithm designed for optimization of neural network architectures for intracranial EEG recordings analysis
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Date
2023-06-16
Authors
Pijáčková, Kristýna
Nejedlý, Petr
Křemen, Václav
Plešinger, Filip
Mívalt, Filip
Lepková, Kamila
Pail, Martin
Jurák, Pavel
Worrell, Gregory
Brázdil, Milan
ORCID
0000-0002-0693-9495Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
IOP Publishing
Altmetrics
Abstract
Objective. The current practices of designing neural networks rely heavily on subjective judgment and heuristic steps, often dictated by the level of expertise possessed by architecture designers. To alleviate these challenges and streamline the design process, we propose an automatic method, a novel approach to enhance the optimization of neural network architectures for processing intracranial electroencephalogram (iEEG) data. Approach. We present a genetic algorithm, which optimizes neural network architecture and signal pre-processing parameters for iEEG classification. Main results. Our method improved the macro F1 score of the state-of-the-art model in two independent datasets, from St. Anne's University Hospital (Brno, Czech Republic) and Mayo Clinic (Rochester, MN, USA), from 0.9076 to 0.9673 and from 0.9222 to 0.9400 respectively. Significance. By incorporating principles of evolutionary optimization, our approach reduces the reliance on human intuition and empirical guesswork in architecture design, thus promoting more efficient and effective neural network models. The proposed method achieved significantly improved results when compared to the state-of-the-art benchmark model (McNemar's test, p MUCH LESS-THAN 0.01). The results indicate that neural network architectures designed through machine-based optimization outperform those crafted using the subjective heuristic approach of a human expert. Furthermore, we show that well-designed data preprocessing significantly affects the models' performance.
Description
Citation
J NEURAL ENG. 2023, vol. 20, issue 3, p. 1-11.
https://iopscience.iop.org/article/10.1088/1741-2552/acdc54
https://iopscience.iop.org/article/10.1088/1741-2552/acdc54
Document type
Peer-reviewed
Document version
Published version
Date of access to the full text
Language of document
en