{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T15:13:21Z","timestamp":1783523601421,"version":"3.55.0"},"reference-count":55,"publisher":"World Scientific Pub Co Pte Ltd","issue":"03","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62271291"],"award-info":[{"award-number":["62271291"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key Program of the Natural Science Foundation of Shandong Province","award":["ZR2020LZH009"],"award-info":[{"award-number":["ZR2020LZH009"]}]},{"name":"Shenzhen Science and Technology Program","award":["GJHZ20220913142607013"],"award-info":[{"award-number":["GJHZ20220913142607013"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:p> Automatic seizure detection plays a key role in assisting clinicians for rapid diagnosis and treatment of epilepsy. In view of the parallelism of temporal convolutional network (TCN) and the capability of bidirectional long short-term memory (BiLSTM) in mining the long-range dependency of multi-channel time-series, we propose an automatic seizure detection method with a novel end-to-end TCN-BiLSTM model in this work. First, raw EEG is filtered with a 0.5\u201345 Hz band-pass filter, and the filtered data are input into the proposed TCN-BiLSTM network for feature extraction and classification. Post-processing process including moving average filtering, thresholding and collar technique is then employed to further improve the detection performance. The method was evaluated on two EEG database. On the CHB-MIT scalp EEG database, our method achieved a segment-based sensitivity of 94.31%, specificity of 97.13%, and accuracy of 97.09%. Meanwhile, an event-based sensitivity of 96.48% and an average false detection rate (FDR) of 0.38\/h were obtained. On the SH-SDU database we collected, the segment-based sensitivity of 94.99%, specificity of 93.25%, and accuracy of 93.27% were achieved. In addition, an event-based sensitivity of 99.35% and a false detection rate of 0.54\/h were yielded. The total detection time consumed for 1[Formula: see text]h EEG data was 5.65[Formula: see text]s. These results demonstrate the superiority and promising potential of the proposed method in real-time monitoring of epileptic seizures. <\/jats:p>","DOI":"10.1142\/s0129065724500126","type":"journal-article","created":{"date-parts":[[2023,12,17]],"date-time":"2023-12-17T13:31:17Z","timestamp":1702819877000},"source":"Crossref","is-referenced-by-count":48,"title":["Epileptic Seizure Detection with an End-to-End Temporal Convolutional Network and Bidirectional Long Short-Term Memory Model"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-4930-0766","authenticated-orcid":false,"given":"Xingchen","family":"Dong","sequence":"first","affiliation":[{"name":"School of Integrated Circuits, Shandong University, Jinan 250100, P.\u00a0R.\u00a0China"},{"name":"Shenzhen Institute of Shandong University, Shenzhen 518057, 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