2026-08-17T12:12:42.070Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:2:12140 2026-08-07T15:15:49Z 2

Tuning gated recurrent unit models for electroencephalogram anomaly detection with modified particle swarm optimization metaheuristics 2026 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/2/12140 https://pubs.aip.org/aip/adv/article/16/8/085103/3400057/Tuning-gated-recurrent-unit-models-for T. Dogandzic N. Bacanin S. Vukmirovic A. Selakov M. Stankovic M. Zivkovic B. Radomirovic V. Simic M. Abdel-Salam M. Antonijevic Electroencephalography (EEG) represents an essential neurological assessment technique that captures the brain’s bioelectrical signals via electrodes positioned on the scalp. Although artificial intelligence has shown substantial potential across various areas of medical analysis, its application within neurodiagnostic procedures is still not widely examined. This study tackles that shortfall by introducing a novel methodology based on time-series categorization of EEG recordings, utilizing gated recurrent unit neural architectures to pinpoint irregular neural patterns, with a focus on seizure detection. To further boost the accuracy of the proposed framework, metaheuristic optimization strategies are applied for refining hyperparameter configurations. Moreover, a customized variant of particle swarm optimization is developed, designed specifically for this neurodiagnostic context. The performance of the approach is assessed using a carefully selected dataset containing authentic EEG traces from neurologically healthy subjects as well as individuals diagnosed with epilepsy. The resulting software-driven solution yields impressive outcomes, demonstrating strong capability in identifying anomalies even when operating with comparatively limited sample counts. article 10.1063/5.0349791 16 8: 085103 2158-3226 AIP Advances