{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T06:14:58Z","timestamp":1788243298156,"version":"build-2803163510"},"reference-count":21,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neuroinform."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Automatic seizure detection from long-term scalp electroencephalography (EEG) can reduce the burden of manual EEG review. However, multi-derivation EEG is commonly processed as a collection of input channels without explicitly modeling the structured relations defined by the bipolar montage.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this study, we propose MGF-Net, a montage-guided fusion network for seizure detection from long-term scalp EEG. MGF-Net represents 18 fixed bipolar derivations as nodes in a relation-labeled graph. The graph encodes same-chain adjacency, left-right correspondence, and shared-electrode relations derived from the bipolar montage. After epoch-wise robust normalization and wavelet-based signal reconstruction are applied independently to each epoch, a shared one-dimensional convolutional neural network extracts temporal features from each derivation. A relation-aware graph Transformer then performs feature fusion across derivations using relation-specific key and value projections. Attention-based graph pooling generates an epoch representation for ictal probability estimation, and post-processing converts epoch-wise probabilities into seizure-event detections.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Experiments on the CHB-MIT dataset show that MGF-Net achieves a segment-level accuracy of 98.38%, sensitivity of 93.18%, and specificity of 98.41%. At the event level, the proposed method detected all 65 annotated test events, achieving a sensitivity of 100.00%, a false detection rate of 0.82\/h, and a latency of 2.03 s.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>These results demonstrate MGF-Net's effectiveness for seizure detection from long-term scalp EEG.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fninf.2026.1942480","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T05:34:11Z","timestamp":1788240851000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Montage-guided fusion network for long-term scalp electroencephalography seizure detection"],"prefix":"10.3389","volume":"20","author":[{"given":"Hongtao","family":"Yin","sequence":"first","affiliation":[{"name":"Department of Neurology, Zibo Central Hospital","place":["Zibo, Shandong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Yin","sequence":"additional","affiliation":[{"name":"Department of Neurology, Zibo Central Hospital","place":["Zibo, Shandong, China"]},{"name":"First Clinical Medical College, Shandong Medical and Pharmaceutical University","place":["Yantai, Shandong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,1]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1016\/S0140-6736(23)01048-6","article-title":"Adult epilepsy","volume":"402","author":"Asadi-Pooya","year":"2023","journal-title":"Lancet"},{"key":"B2","first-page":"160","article-title":"\u201cTemporal graph convolutional networks for automatic seizure detection,\u201d","volume-title":"Machine learning for healthcare conference","author":"Covert","year":"2019"},{"key":"B3","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1111\/epi.18113","article-title":"Szcore: seizure community open-source research evaluation framework for the validation of electroencephalography-based automated seizure detection algorithms","volume":"66","author":"Dan","year":"2025","journal-title":"Epilepsia"},{"key":"B4","doi-asserted-by":"publisher","first-page":"2450012","DOI":"10.1142\/S0129065724500126","article-title":"Epileptic seizure detection with an end-to-end temporal convolutional network and bidirectional long short-term memory model","volume":"34","author":"Dong","year":"2024","journal-title":"Int. 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