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We propose a model that incorporates two nonlinear characteristic parameters, MSE and PSE, extracted from artificially designed EEG signals as input. Additionally, we employ a Support Vector Machine (SVM) classifier algorithm to accurately identify sleep states, eliminating uncertainties associated with manually designed feature parameters. Experimental results demonstrate the superior accuracy of our proposed model for sleep state analysis, offering valuable insights for improving sleep quality and addressing related health concerns.<\/jats:p>","DOI":"10.3233\/jifs-230765","type":"journal-article","created":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T10:37:09Z","timestamp":1694169429000},"page":"8703-8716","source":"Crossref","is-referenced-by-count":2,"title":["Improving sleeping quality and health under CPS: An analysis based on EEG data and support vector machine algorithm"],"prefix":"10.1177","volume":"45","author":[{"given":"R.","family":"Subhashini","sequence":"first","affiliation":[{"name":"Department of Information Technology, Sona college of Technology, Salem"}]},{"given":"G.R.","family":"Hemalakshmi","sequence":"additional","affiliation":[{"name":"School of Computing Science and Engineering, Vellore Institute of Technology, Bhopal, 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