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Seizure detection by electroencephalogram (EEG) is associated with the primary interest of the evaluation and auxiliary diagnosis of epileptic patients. The aim of this study is to establish a hybrid model with improved particle swarm optimization (PSO) and a genetic algorithm (GA) to determine the optimal combination of features for epileptic seizure detection. First, the second-order difference plot (SODP) method was applied, and ten geometric features of epileptic EEG signals were derived in each frequency band (\u03b4, \u03b8, \u03b1 and \u03b2), forming a high-dimensional feature vector. Secondly, an optimization algorithm, AsyLnCPSO-GA, combining a modified PSO with asynchronous learning factor (AsyLnCPSO) and the genetic algorithm (GA) was proposed for feature selection. Finally, the feature combinations were fed to a na\u00efve Bayesian classifier for epileptic seizure and seizure-free identification. The method proposed in this paper achieved 95.35% classification accuracy with a tenfold cross-validation strategy when the interfrequency bands were crossed, serving as an effective method for epilepsy detection, which could help clinicians to expeditiously diagnose epilepsy based on SODP analysis and an optimization algorithm for feature selection.<\/jats:p>","DOI":"10.3390\/e24111540","type":"journal-article","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T11:53:51Z","timestamp":1666785231000},"page":"1540","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Epileptic Seizure Detection Using Geometric Features Extracted from SODP Shape of EEG Signals and AsyLnCPSO-GA"],"prefix":"10.3390","volume":"24","author":[{"given":"Ruofan","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin 300222, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6430-8088","authenticated-orcid":false,"given":"Haodong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin 300222, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lianshuan","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin 300222, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0647-1834","authenticated-orcid":false,"given":"Chunxiao","family":"Han","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Information Sensing & Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5988-7908","authenticated-orcid":false,"given":"Yanqiu","family":"Che","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Information Sensing & Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1901","DOI":"10.1007\/s11071-021-06420-4","article-title":"Epilepsy as a dynamical disorder orchestrated by epileptogenic zone: A review","volume":"104","author":"Yang","year":"2021","journal-title":"Nonlinear Dyn."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1016\/j.amjmed.2021.01.038","article-title":"Epilepsy: A clinical overview","volume":"134","author":"Milligan","year":"2021","journal-title":"Am. 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