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Syst."],"published-print":{"date-parts":[[2026,12,15]]},"abstract":"<jats:p>Epilepsy manifests as a chronic neurological condition marked by recurrent seizures. Recent advances in computational analysis of Electroencephalography (EEG) signals have enabled new possibilities for identifying ictal events in extended recordings. This work develops a novel deep learning architecture that simultaneously resolves two fundamental challenges in automated seizure detection: comprehensive feature representation and class distribution imbalance. First, a multibranch neural network structure is proposed to process EEG signals across varying spectral and temporal resolutions. Then, an attention-based feature refinement mechanism is utilized to automatically emphasize clinically relevant signal characteristics. Finally, a modified loss function is leveraged to incorporate class-specific margin adjustments to handle data imbalance scenarios. Analysis of scalp EEG recordings yields detection accuracy with 96.06% sensitivity and 98.50% specificity, and false detection rate (FDR) is maintained at a low level of 0.34 events per hour. When applied to intracranial EEG data, the algorithm demonstrates similar efficacy (95.90% sensitivity, 98.65% specificity) with further reduced false detections (0.18\/h). The consistent efficacy validated on diverse EEG modalities (scalp and intracranial) supports its clinical utility as a practical diagnostic tool.<\/jats:p>","DOI":"10.1142\/s0129065726500474","type":"journal-article","created":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T10:16:01Z","timestamp":1778840161000},"source":"Crossref","is-referenced-by-count":1,"title":["Automatic Seizure Detection using Hierarchical Spectral-Temporal Feature Learning with an Imbalance-Aware Transformer"],"prefix":"10.1142","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2082-517X","authenticated-orcid":false,"given":"Zuyi","family":"Yu","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250000, P. R. 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