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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU149502" target="_blank" >RIV/00216305:26220/23:PU149502 - isvavai.cz</a>

Alternative codes found

RIV/68081731:_____/23:00577393 RIV/00159816:_____/23:00079587 RIV/68407700:21460/23:00366949 RIV/00216224:14110/23:00132262

Result on the web

<a href="https://iopscience.iop.org/article/10.1088/1741-2552/acdc54" target="_blank" >https://iopscience.iop.org/article/10.1088/1741-2552/acdc54</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1741-2552/acdc54" target="_blank" >10.1088/1741-2552/acdc54</a>

Result language

angličtina

Original language name

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

Original language 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.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20600 - Medical engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

J NEURAL ENG

ISSN

1741-2560

e-ISSN

1741-2552

Volume of the periodical

20

Issue of the periodical within the volume

3

Country of publishing house

GB - UNITED KINGDOM

Number of pages

11

Pages from-to

1-11

UT code for WoS article

001085835700001

EID of the result in the Scopus database

2-s2.0-85163311622