- Genetic algorithm designed for optimization of neural network archite…
Počet záznamů: 1  

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

  1. 1.
    SYSNO ASEP0577393
    Druh ASEPJ - Článek v odborném periodiku
    Zařazení RIVJ - Článek v odborném periodiku
    Poddruh JČlánek ve WOS
    NázevGenetic algorithm designed for optimization of neural network architectures for intracranial EEG recordings analysis
    Tvůrce(i) Pijáčková, Kristýna (UPT-D) ORCID, SAI, RID
    Nejedlý, Petr (UPT-D) RID, SAI
    Křemen, V. (US)
    Plešinger, Filip (UPT-D) RID, ORCID, SAI
    Mívalt, F. (US)
    Lepková, K. (CZ)
    Pail, Martin (UPT-D) RID, SAI, ORCID
    Jurák, Pavel (UPT-D) RID, ORCID, SAI
    Worrell, G. A. (US)
    Brázdil, M. (CZ)
    Klimeš, Petr (UPT-D) RID, ORCID, SAI
    Celkový počet autorů11
    Číslo článku036034
    Zdroj.dok.Journal of Neural Engineering. - : Institute of Physics Publishing - ISSN 1741-2560
    Roč. 20, č. 3 (2023)
    Poč.str.11 s.
    Forma vydáníTištěná - P
    Jazyk dok.eng - angličtina
    Země vyd.US - Spojené státy americké
    Klíč. slovaintracranial EEG ; genetic algorithms ; optimization ; neural network ; deep learning
    Vědní obor RIVFH - Neurologie, neurochirurgie, neurovědy
    Obor OECDNeurosciences (including psychophysiology
    CEPNU22-08-00278 GA MZd - Ministerstvo zdravotnictví
    GA22-28784S GA ČR - Grantová agentura ČR
    LX22NPO5107 GA MŠMT - Ministerstvo školství, mládeže a tělovýchovy
    Způsob publikováníOpen access
    Institucionální podporaUPT-D - RVO:68081731
    UT WOS001085835700001
    EID SCOPUS85163311622
    DOI https://doi.org/10.1088/1741-2552/acdc54
    AnotaceObjective. 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.
    PracovištěÚstav přístrojové techniky
    KontaktMartina Šillerová, sillerova@ISIBrno.Cz, Tel.: 541 514 178
    Rok sběru2024
    Elektronická adresahttps://iopscience.iop.org/article/10.1088/1741-2552/acdc54
Počet záznamů: 1  

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