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Seizure detection algorithms are essential in reducing the workload of medical staff reviewing electroencephalogram (EEG) records. In this work, we propose a novel automatic epileptic EEG detection method based on Stockwell transform and Transformer. First, the S-transform is applied to the original EEG segments, acquiring accurate time-frequency representations. Subsequently, the obtained time-frequency matrices are grouped into different EEG rhythm blocks and compressed as vectors in these EEG sub-bands. After that, these feature vectors are fed into the Transformer network for feature selection and classification. Moreover, a series of post-processing methods were introduced to enhance the efficiency of the system. When evaluating the public CHB-MIT database, the proposed algorithm achieved an accuracy of 96.15%, a sensitivity of 96.11%, a specificity of 96.38%, a precision of 96.33%, and an area under the curve (AUC) of 0.98 in segment-based experiments, along with a sensitivity of 96.57%, a false detection rate of 0.38\/h, and a delay of 20.62 s in event-based experiments. These outstanding results demonstrate the feasibility of implementing this seizure detection method in future clinical applications.<\/jats:p>","DOI":"10.3390\/s24010077","type":"journal-article","created":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T20:49:27Z","timestamp":1703450967000},"page":"77","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Automatic Seizure Detection Based on Stockwell Transform and Transformer"],"prefix":"10.3390","volume":"24","author":[{"given":"Xiangwen","family":"Zhong","sequence":"first","affiliation":[{"name":"School of Integrated Circuits, Shandong University, Jinan 260100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5879-809X","authenticated-orcid":false,"given":"Guoyang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Shandong University, Jinan 260100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingchen","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Shandong University, Jinan 260100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanyu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Shandong University, Jinan 260100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haotian","family":"Li","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Shandong University, Jinan 260100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haozhou","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Shandong University, Jinan 260100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9481-1696","authenticated-orcid":false,"given":"Weidong","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Shandong University, Jinan 260100, China"},{"name":"Shenzhen Institute, Shandong University, Shenzhen 518057, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,22]]},"reference":[{"key":"ref_1","unstructured":"(2023, October 13). 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