{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T03:00:39Z","timestamp":1780974039270,"version":"3.54.1"},"reference-count":38,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100022963","name":"Key Research and Development Program of Zhejiang Province","doi-asserted-by":"publisher","award":["2025C01120"],"award-info":[{"award-number":["2025C01120"]}],"id":[{"id":"10.13039\/100022963","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008990","name":"Science and Technology Department of Zhejiang Province","doi-asserted-by":"publisher","award":["2024C03161"],"award-info":[{"award-number":["2024C03161"]}],"id":[{"id":"10.13039\/501100008990","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.eswa.2026.132136","type":"journal-article","created":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T16:23:50Z","timestamp":1774023830000},"page":"132136","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Weakly supervised single-channel interictal epileptiform discharge detection with spatial priors"],"prefix":"10.1016","volume":"319","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-4193-6824","authenticated-orcid":false,"given":"Chenhao","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5768-0586","authenticated-orcid":false,"given":"Wei","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7809-6601","authenticated-orcid":false,"given":"Jiale","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3912-6156","authenticated-orcid":false,"given":"Bo","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9740-2537","authenticated-orcid":false,"given":"Feng","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.132136_bib0001","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107782","article-title":"A review of signal processing and machine learning techniques for interictal epileptiform discharge detection","volume":"168","author":"Abdi-Sargezeh","year":"2024","journal-title":"Computers in Biology and Medicine"},{"key":"10.1016\/j.eswa.2026.132136_bib0002","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2023.104698","article-title":"Deep neural networks for the detection of temporal-lobe epileptiform discharges from scalp electroencephalograms","volume":"84","author":"Chan","year":"2023","journal-title":"Biomedical Signal Processing and Control"},{"issue":"1","key":"10.1016\/j.eswa.2026.132136_bib0003","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/TNNLS.2017.2716952","article-title":"Broad learning system: An effective and efficient incremental learning system without the need for deep architecture","volume":"29","author":"Chen","year":"2018","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.eswa.2026.132136_bib0004","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.seizure.2018.04.020","article-title":"Adaptive nocturnal seizure detection using heart rate and low-complexity novelty detection","volume":"59","author":"De Cooman","year":"2018","journal-title":"Seizure"},{"issue":"5","key":"10.1016\/j.eswa.2026.132136_bib0005","doi-asserted-by":"crossref","first-page":"1903","DOI":"10.1093\/brain\/awad015","article-title":"Interictal discharges in the human brain are travelling waves arising from an epileptogenic source","volume":"146","author":"Diamond","year":"2023","journal-title":"Brain"},{"issue":"7","key":"10.1016\/j.eswa.2026.132136_bib0006","doi-asserted-by":"crossref","first-page":"1433","DOI":"10.1016\/j.clinph.2021.02.403","article-title":"Machine learning for detection of interictal epileptiform discharges","volume":"132","author":"da Silva Louren\u00e7o","year":"2021","journal-title":"Clinical Neurophysiology"},{"issue":"6","key":"10.1016\/j.eswa.2026.132136_bib0007","doi-asserted-by":"crossref","first-page":"1174","DOI":"10.1016\/j.clinph.2020.02.032","article-title":"An artificial intelligence-based EEG algorithm for detection of epileptiform EEG discharges: Validation against the diagnostic gold standard","volume":"131","author":"F\u00fcrbass","year":"2020","journal-title":"Clinical Neurophysiology"},{"key":"10.1016\/j.eswa.2026.132136_bib0008","doi-asserted-by":"crossref","DOI":"10.3389\/fnhum.2019.00076","article-title":"Automatic analysis of EEGs using big data and hybrid deep learning architectures","volume":"13","author":"Golmohammadi","year":"2019","journal-title":"Frontiers in Human Neuroscience"},{"key":"10.1016\/j.eswa.2026.132136_bib0009","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2013.00267","article-title":"Meg and eeg data analysis with mne-python","volume":"7","author":"Gramfort","year":"2013","journal-title":"Frontiers in Neuroscience"},{"key":"10.1016\/j.eswa.2026.132136_bib0010","series-title":"2015 IEEE signal processing in medicine and biology symposium (SPMB)","first-page":"1","article-title":"Improved EEG event classification using differential energy","author":"Harati","year":"2015"},{"issue":"1","key":"10.1016\/j.eswa.2026.132136_bib0011","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11571-022-09816-z","article-title":"Epileptic seizure focus detection from interictal electroencephalogram: A survey","volume":"17","author":"Islam","year":"2022","journal-title":"Cognitive Neurodynamics"},{"key":"10.1016\/j.eswa.2026.132136_bib0012","doi-asserted-by":"crossref","first-page":"2939","DOI":"10.1109\/TNSRE.2022.3215526","article-title":"Deep learning-based detection of epileptiform discharges for self-limited epilepsy with centrotemporal spikes","volume":"30","author":"Jeon","year":"2022","journal-title":"IEEE Transactions on Neural Systems and Rehabilitation Engineering"},{"key":"10.1016\/j.eswa.2026.132136_bib0013","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2023.1150668","article-title":"An improved BECT spike detection method with functional brain network features based on PLV","volume":"17","author":"Jiang","year":"2023","journal-title":"Frontiers in Neuroscience"},{"issue":"11","key":"10.1016\/j.eswa.2026.132136_bib0014","first-page":"4613","article-title":"Bect spike detection based on novel multichannel data weighted fusion algorithm","volume":"69","author":"Jiang","year":"2022","journal-title":"IEEE Transactions on Circuits and Systems II: Express Briefs"},{"issue":"1","key":"10.1016\/j.eswa.2026.132136_bib0015","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1001\/jamaneurol.2019.3531","article-title":"Interrater reliability of experts in identifying interictal epileptiform discharges in electroencephalograms","volume":"77","author":"Jing","year":"2020","journal-title":"JAMA Neurology"},{"issue":"1","key":"10.1016\/j.eswa.2026.132136_bib0016","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1001\/jamaneurol.2019.3485","article-title":"Development of expert-level automated detection of epileptiform discharges during electroencephalogram interpretation","volume":"77","author":"Jing","year":"2020","journal-title":"JAMA Neurology"},{"key":"10.1016\/j.eswa.2026.132136_bib0017","series-title":"International conference on learning representations (ICLR)","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2017"},{"issue":"20","key":"10.1016\/j.eswa.2026.132136_bib0018","doi-asserted-by":"crossref","DOI":"10.1212\/WNL.0000000000009439","article-title":"Criteria for defining interictal epileptiform discharges in EEG: A clinical validation study","volume":"94","author":"Kural","year":"2020","journal-title":"Neurology"},{"key":"10.1016\/j.eswa.2026.132136_bib0019","series-title":"2017 39th annual international conference of the IEEE engineering in medicine and biology society (EMBC)","first-page":"475","article-title":"Surface and intracranial EEG spike detection based on discrete wavelet decomposition and random forest classification","author":"Le Douget","year":"2017"},{"key":"10.1016\/j.eswa.2026.132136_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106319","article-title":"vEpiNet: A multimodal interictal epileptiform discharge detection method based on video and electroencephalogram data","volume":"175","author":"Lin","year":"2024","journal-title":"Neural Networks"},{"issue":"1","key":"10.1016\/j.eswa.2026.132136_bib0021","doi-asserted-by":"crossref","DOI":"10.1038\/s41597-025-04572-1","article-title":"An EEG dataset for interictal epileptiform discharge with spatial distribution information","volume":"12","author":"Lin","year":"2025","journal-title":"Scientific Data"},{"key":"10.1016\/j.eswa.2026.132136_bib0022","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1109\/LSP.2023.3263433","article-title":"Scalp EEG-based automatic detection of epileptiform events via graph convolutional network and bi-directional LSTM co-embedded broad learning system","volume":"30","author":"Liu","year":"2023","journal-title":"IEEE Signal Processing Letters"},{"issue":"9","key":"10.1016\/j.eswa.2026.132136_bib0023","doi-asserted-by":"crossref","first-page":"12536","DOI":"10.3390\/s130912536","article-title":"Model-based spike detection of epileptic EEG data","volume":"13","author":"Liu","year":"2013","journal-title":"Sensors"},{"issue":"12","key":"10.1016\/j.eswa.2026.132136_bib0024","doi-asserted-by":"crossref","first-page":"2328","DOI":"10.1016\/j.clinph.2013.05.019","article-title":"Inter-ictal spike detection using a database of smart templates","volume":"124","author":"Lodder","year":"2013","journal-title":"Clinical Neurophysiology"},{"key":"10.1016\/j.eswa.2026.132136_bib0025","series-title":"2016 38th annual international conference of the IEEE engineering in medicine and biology society (EMBC)","first-page":"774","article-title":"Automatic threshold optimization in nonlinear energy operator based spike detection","author":"Malik","year":"2016"},{"key":"10.1016\/j.eswa.2026.132136_bib0026","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106136","article-title":"V2IED: Dual-view learning framework for detecting events of interictal epileptiform discharges","volume":"172","author":"Ming","year":"2024","journal-title":"Neural Networks"},{"key":"10.1016\/j.eswa.2026.132136_bib0027","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2023.102663","article-title":"Graph neural networks in EEG spike detection","volume":"145","author":"Mohammed","year":"2023","journal-title":"Artificial Intelligence in Medicine"},{"key":"10.1016\/j.eswa.2026.132136_bib0028","series-title":"2023 45th annual international conference of the IEEE engineering in medicine & biology society (EMBC)","first-page":"1","article-title":"Interictal epileptiform discharge detection using multi-head deep convolutional neural network","author":"Munia","year":"2023"},{"issue":"01","key":"10.1016\/j.eswa.2026.132136_bib0029","doi-asserted-by":"crossref","DOI":"10.1142\/S0129065723500016","article-title":"Automated interictal epileptiform discharge detection from scalp EEG using scalable time-series classification approaches","volume":"33","author":"Nhu","year":"2023","journal-title":"International Journal of Neural Systems"},{"key":"10.1016\/j.eswa.2026.132136_bib0030","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2016.00196","article-title":"The temple university hospital EEG data corpus","volume":"10","author":"Obeid","year":"2016","journal-title":"Frontiers in Neuroscience"},{"key":"10.1016\/j.eswa.2026.132136_bib0031","unstructured":"Redmon, J., & Farhadi, A. (2018). YOLOv3: An incremental improvement. 10.48550\/ARXIV.1804.02767."},{"issue":"6","key":"10.1016\/j.eswa.2026.132136_bib0032","doi-asserted-by":"crossref","DOI":"10.1007\/s11432-020-3100-8","article-title":"Detection of the interictal epileptic discharges based on wavelet bispectrum interaction and recurrent neural network","volume":"64","author":"Sabor","year":"2021","journal-title":"Science China Information Sciences"},{"issue":"1","key":"10.1016\/j.eswa.2026.132136_bib0033","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.clinph.2016.11.005","article-title":"Spike detection: Inter-reader agreement and a statistical turing test on a large data set","volume":"128","author":"Scheuer","year":"2017","journal-title":"Clinical Neurophysiology"},{"key":"10.1016\/j.eswa.2026.132136_bib0034","unstructured":"Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. 10.48550\/ARXIV.1409.1556."},{"key":"10.1016\/j.eswa.2026.132136_bib0035","doi-asserted-by":"crossref","DOI":"10.1111\/epi.18463","article-title":"Deep learning-based classification and segmentation of interictal epileptiform discharges using multichannel electroencephalography","author":"Sun","year":"2025","journal-title":"Epilepsia"},{"issue":"3","key":"10.1016\/j.eswa.2026.132136_bib0036","doi-asserted-by":"crossref","first-page":"480","DOI":"10.14778\/3570690.3570698","article-title":"iEDeal: A deep learning framework for detecting highly imbalanced interictal epileptiform discharges","volume":"16","author":"Wang","year":"2022","journal-title":"Proceedings of the VLDB Endowment"},{"issue":"2","key":"10.1016\/j.eswa.2026.132136_bib0037","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s40708-016-0044-4","article-title":"Spike pattern recognition by supervised classification in low dimensional embedding space","volume":"3","author":"Zacharaki","year":"2016","journal-title":"Brain Informatics"},{"key":"10.1016\/j.eswa.2026.132136_bib0038","doi-asserted-by":"crossref","DOI":"10.3389\/fmolb.2023.1146606","article-title":"Automatic interictal epileptiform discharge (IED) detection based on convolutional neural network (CNN)","volume":"10","author":"Zhang","year":"2023","journal-title":"Frontiers in Molecular Biosciences"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426010493?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426010493?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T02:54:49Z","timestamp":1780973689000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426010493"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":38,"alternative-id":["S0957417426010493"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132136","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Weakly supervised single-channel interictal epileptiform discharge detection with spatial priors","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132136","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"132136"}}