Select for better learning: identifying high-quality training data for a multimodal cyclic transformer

Zhang, JingweiJingweiZhangLiu, ZhaoyiZhaoyiLiuChatzichristos, ChristosChristosChatzichristosMichiels, SamSamMichielsVan Paesschen, WimWimVan PaesschenHughes, DannyDannyHughesDe Vos, MaartenMaartenDe Vos2025-04-012025-04-0120251741-2560WOS:001451756500001https://imec-publications.be/handle/20.500.12860/45473Objective. Tonic–clonic seizures (TCSs), which present a significant risk for sudden unexpected death in epilepsy, require accurate detection to enable effective long-term monitoring. Previous studies have demonstrated the advantages of multimodal seizure detection systems in reliably detecting TCSs over extended periods. However, the effectiveness of these data-driven systems depends heavily on the availability of reliable training data. Approach. To address this need, we propose an innovative data selection method designed to identify high-quality training samples. Our approach evaluates sample quality based on learning difficulty, classifying samples with lower learning difficulty as higher quality. We then introduce a confidence-based method to quantify the proportion of high-quality samples within the dataset. Main results. Experimental results show that our method improves the performance of a state-of-the-art TCS detection model by 11%. Significance. Using this data selection method, we develop a training pipeline that enhances the training process of multimodal seizure detection models.Select for better learning: identifying high-quality training data for a multimodal cyclic transformerJournal article10.1088/1741-2552/adbec0WOS:001451756500001DEEPEEGMEDLINE:40064111