{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T08:04:41Z","timestamp":1743408281070},"reference-count":58,"publisher":"World Scientific Pub Co Pte Ltd","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2023,3]]},"abstract":" Neurologists typically identify epileptic seizures from electroencephalograms (EEGs) by visual inspection. This process is often time-consuming, especially for EEG recordings that last hours or days. To expedite the process, a reliable, automated, and patient-independent seizure detector is essential. However, developing a patient-independent seizure detector is challenging as seizures exhibit diverse characteristics across patients and recording devices. In this study, we propose a patient-independent seizure detector to automatically detect seizures in both scalp EEG and intracranial EEG (iEEG). First, we deploy a convolutional neural network with transformers and belief matching loss to detect seizures in single-channel EEG segments. Next, we extract regional features from the channel-level outputs to detect seizures in multi-channel EEG segments. At last, we apply post-processing filters to the segment-level outputs to determine seizures\u2019 start and end points in multi-channel EEGs. Finally, we introduce the minimum overlap evaluation scoring as an evaluation metric that accounts for minimum overlap between the detection and seizure, improving upon existing assessment metrics. We trained the seizure detector on the Temple University Hospital Seizure (TUH-SZ) dataset and evaluated it on five independent EEG datasets. We evaluate the systems with the following metrics: sensitivity (SEN), precision (PRE), and average and median false positive rate per hour (aFPR\/h and mFPR\/h). Across four adult scalp EEG and iEEG datasets, we obtained SEN of 0.617\u20131.00, PRE of 0.534\u20131.00, aFPR\/h of 0.425\u20132.002, and mFPR\/h of 0\u20131.003. The proposed seizure detector can detect seizures in adult EEGs and takes less than 15[Formula: see text]s for a 30[Formula: see text]min EEG. Hence, this system could aid clinicians in reliably identifying seizures expeditiously, allocating more time for devising proper treatment. <\/jats:p>","DOI":"10.1142\/s0129065723500120","type":"journal-article","created":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T14:54:47Z","timestamp":1671202487000},"source":"Crossref","is-referenced-by-count":20,"title":["Six-Center Assessment of CNN-Transformer with Belief Matching Loss for Patient-Independent Seizure Detection in EEG"],"prefix":"10.1142","volume":"33","author":[{"given":"Wei Yan","family":"Peh","sequence":"first","affiliation":[{"name":"Interdisciplinary Graduate School (IGS), Nanyang Technological University, Singapore 639798, Singapore"}]},{"given":"Prasanth","family":"Thangavel","sequence":"additional","affiliation":[{"name":"Interdisciplinary Graduate School (IGS), Nanyang Technological University, Singapore 639798, Singapore"}]},{"given":"Yuanyuan","family":"Yao","sequence":"additional","affiliation":[{"name":"Katholieke Universiteit Leuven, Oude Markt 13, 3000 Leuven, Belgium"}]},{"given":"John","family":"Thomas","sequence":"additional","affiliation":[{"name":"Montreal Neurological Institute, McGill University, Montreal QC H3A 2B4, Canada"}]},{"given":"Yee-Leng","family":"Tan","sequence":"additional","affiliation":[{"name":"National Neuroscience Institute, Singapore 308433, Singapore"}]},{"given":"Justin","family":"Dauwels","sequence":"additional","affiliation":[{"name":"Department of Microelectronics, Delft, University of Technology, 2628 CD Delft, Netherlands"}]}],"member":"219","published-online":{"date-parts":[[2023,2,22]]},"reference":[{"key":"S0129065723500120BIB001","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awu133"},{"key":"S0129065723500120BIB002","doi-asserted-by":"crossref","DOI":"10.1136\/bmj.e281","volume":"344","author":"Nunes V. 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