{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T05:40:22Z","timestamp":1792820422466,"version":"4.5.0"},"reference-count":54,"publisher":"World Scientific Pub Co Pte Ltd","funder":[{"name":"the Zhejiang Provincial Natural Science Foundation of China","award":["TGY24H090051"],"award-info":[{"award-number":["TGY24H090051"]}],"id":[{"id":"https:\/\/ror.org\/04aqat463","id-type":"ROR","asserted-by":"crossref"}]},{"name":"the Scientific Research Fund of the Zhejiang Provincial Education Department","award":["Y202352381"],"award-info":[{"award-number":["Y202352381"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"abstract":"<jats:p>Reliable identification and early prediction of epileptic seizures play a critical role in improving patient outcomes and supporting timely therapeutic decision-making. To address the challenges of spatiotemporal feature coupling and long-range dependency modeling in multi-channel EEG signals, this paper proposes a channel-adaptive temporal modeling framework named GCNN\u2013Informer. The framework follows a three-stage pipeline: a grouped convolutional network (GCNN) extracts local features and selects the five most discriminative channels; a convolutional block attention module (CBAM) performs dual spatiotemporal enhancement; and a lightweight Informer encoder captures global temporal dependencies via the ProbSparse self-attention mechanism. Experiments on the CHB-MIT dataset, conducted under a patient-specific evaluation scheme, demonstrated that the proposed method achieved accuracies of 97.33% for seizure detection and 97.28% for seizure prediction, with a low false detection rate (FDR) of 0.30\/h and a false prediction rate (FPR) of 0.03\/h. Grad-CAM visualization further shows that as EEG signals evolve from normal\/interictal to preictal and then to ictal stages, the model\u2019s attention heatmap progressively transitions from locally sparse highlighting to continuous block-like highlighting, and finally to dense deep-red responses\u00a0\u2014 a pattern that closely aligns with clinical electrophysiological dynamics. This study provides an accurate and generalizable deep-learning solution for real-time epileptic monitoring and early-warning systems.<\/jats:p>","DOI":"10.1142\/s0129065727500316","type":"journal-article","created":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T04:04:06Z","timestamp":1790827446000},"source":"Crossref","is-referenced-by-count":0,"title":["Channel-Adaptive Temporal Modeling with Grouped Convolutional Network and Informer for Seizure Detection and Prediction"],"prefix":"10.1142","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2480-6917","authenticated-orcid":false,"given":"Wenyan","family":"Zhao","sequence":"first","affiliation":[{"name":"Center for General Practice Medicine, General Practice and Health Management Center, Zhejiang Provincial People\u2019s Hospital, Hangzhou Medical College, Hangzhou 310014, P.\u00a0R.\u00a0China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1762-4852","authenticated-orcid":false,"given":"Ruiping","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Biomedical Engineering, Fudan University, Shanghai 200433, P.\u00a0R.\u00a0China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5008-6159","authenticated-orcid":false,"given":"Yanqi","family":"Shao","sequence":"additional","affiliation":[{"name":"Center for Rehabilitation Medicine, Department of Neurology, Zhejiang Provincial People\u2019s Hospital, Hangzhou Medical College, Hangzhou 310014, P.\u00a0R.\u00a0China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2488-3829","authenticated-orcid":false,"given":"Yihang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Informatics and Artificial Intelligence Department, Zhejiang Provincial People\u2019s Hospital, Hangzhou Medical College, Hangzhou 310014, P.\u00a0R.\u00a0China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4930-0766","authenticated-orcid":false,"given":"Xingchen","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Biomedical Engineering, Fudan University, Shanghai 200433, P.\u00a0R.\u00a0China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5269-3979","authenticated-orcid":false,"given":"Jie","family":"Zhou","sequence":"additional","affiliation":[{"name":"Center for General Practice Medicine, General Practice and Health Management Center, Zhejiang Provincial People\u2019s Hospital, Hangzhou Medical College, Hangzhou 310014, P.\u00a0R.\u00a0China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"219","published-online":{"date-parts":[[2026,10,23]]},"reference":[{"key":"S0129065727500316BIB001","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065724500655"},{"key":"S0129065727500316BIB002","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2025.3593337"},{"key":"S0129065727500316BIB003","doi-asserted-by":"publisher","DOI":"10.1016\/j.yebeh.2018.09.030"},{"key":"S0129065727500316BIB004","doi-asserted-by":"publisher","DOI":"10.1016\/S0165-0270(02)00340-0"},{"key":"S0129065727500316BIB005","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2025.103228"},{"key":"S0129065727500316BIB006","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.107075"},{"key":"S0129065727500316BIB007","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2006.886855"},{"key":"S0129065727500316BIB008","doi-asserted-by":"publisher","DOI":"10.1142\/S012906572550056X"},{"key":"S0129065727500316BIB009","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065726500103"},{"key":"S0129065727500316BIB010","doi-asserted-by":"publisher","DOI":"10.1515\/revneuro-2022-0024"},{"key":"S0129065727500316BIB011","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105659"},{"key":"S0129065727500316BIB012","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/adce33"},{"key":"S0129065727500316BIB013","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103919"},{"key":"S0129065727500316BIB014","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2022.3143540"},{"key":"S0129065727500316BIB015","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2022.111948"},{"key":"S0129065727500316BIB016","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2024.3438829"},{"key":"S0129065727500316BIB017","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2026.1756895"},{"key":"S0129065727500316BIB018","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2026.109518"},{"key":"S0129065727500316BIB019","doi-asserted-by":"publisher","DOI":"10.3390\/app15094690"},{"key":"S0129065727500316BIB020","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.02.053"},{"key":"S0129065727500316BIB021","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2870883"},{"key":"S0129065727500316BIB022","unstructured":"A. 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