Publication details

Genetic algorithm designed for optimization of neural network architectures for intracranial EEG recordings analysis

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Authors

PIJACKOVA Kristyna NEJEDLÝ Petr KREMEN Vaclav PLESINGER Filip MIVALT Filip LEPKOVA Kamila PAIL Martin JURAK Pavel WORRELL Gregory BRÁZDIL Milan KLIMES Petr

Year of publication 2023
Type Article in Periodical
Magazine / Source JOURNAL OF NEURAL ENGINEERING
MU Faculty or unit

Faculty of Medicine

Citation
Web https://iopscience.iop.org/article/10.1088/1741-2552/acdc54
Doi http://dx.doi.org/10.1088/1741-2552/acdc54
Keywords intracranial EEG; genetic algorithms; optimization; neural network; deep learning
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Description 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.
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