{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T17:03:24Z","timestamp":1778259804471,"version":"3.51.4"},"reference-count":27,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T00:00:00Z","timestamp":1695600000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Epilepsy does great harm to the human body, and even threatens human life when it is serious. Therefore, research focused on the diagnosis and treatment of epilepsy holds paramount clinical significance. In this paper, we utilized variational modal decomposition (VMD) and an enhanced grey wolf algorithm to detect epileptic electroencephalogram (EEG) signals. Data were extracted from each patient\u2019s preseizure period and seizure period of 200 s each, with every 2 s as a segment, meaning 100 data points could be obtained for each patient\u2019s health period as well as 100 data points for each patient\u2019s epilepsy period. Variational modal decomposition (VMD) was used to obtain the corresponding intrinsic modal function (VMF) of the data. Then, the differential entropy (DE) and high frequency detection (HFD) of each VMF were extracted as features. The improved grey wolf algorithm is adopted for a selected channel to improve the maximum value of the channel. Finally, the EEG signal samples were classified using a support vector machine (SVM) classifier to achieve the accurate detection of epilepsy EEG signals. Experimental results show that the accuracy, sensitivity and specificity of the proposed method can reach 98.3%, 98.9% and 98.5%, respectively. The proposed algorithm in this paper can be used as an index to detect epileptic seizures and has certain guiding significance for the early diagnosis and effective treatment of epileptic patients.<\/jats:p>","DOI":"10.3390\/s23198078","type":"journal-article","created":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T03:45:23Z","timestamp":1695699923000},"page":"8078","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Epileptic EEG Signal Detection Using Variational Modal Decomposition and Improved Grey Wolf Algorithm"],"prefix":"10.3390","volume":"23","author":[{"given":"Yongxin","family":"Sun","sequence":"first","affiliation":[{"name":"College of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China"},{"name":"College of Physics and Electronic Information, Baicheng Normal University, Baicheng 137099, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojuan","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1007\/s00381-019-04152-w","article-title":"Risk factors associated with epilepsy development in children with cerebral palsy","volume":"35","author":"Karatoprak","year":"2019","journal-title":"Child\u2019s Nerv. 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