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

Logo poskytovatele

Varování

Publikace nespadá pod Fakultu sportovních studií, ale pod Lékařskou fakultu. Oficiální stránka publikace je na webu muni.cz.
Autoři

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

Rok publikování 2023
Druh Článek v odborném periodiku
Časopis / Zdroj JOURNAL OF NEURAL ENGINEERING
Fakulta / Pracoviště MU

Lékařská fakulta

Citace
www https://iopscience.iop.org/article/10.1088/1741-2552/acdc54
Doi http://dx.doi.org/10.1088/1741-2552/acdc54
Klíčová slova intracranial EEG; genetic algorithms; optimization; neural network; deep learning
Přiložené soubory
Popis 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.
Související projekty:

Používáte starou verzi internetového prohlížeče. Doporučujeme aktualizovat Váš prohlížeč na nejnovější verzi.

Další info