{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T01:12:56Z","timestamp":1776215576992,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2014,12,17]],"date-time":"2014-12-17T00:00:00Z","timestamp":1418774400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Sleep is a growing area of research interest in medicine and neuroscience. Actually, one major concern is to find a correlation between several physiologic variables and sleep stages. There is a scientific agreement on the characteristics of the five stages of human sleep, based on EEG analysis. Nevertheless, manual stage classification is still the most widely used approach. This work proposes a new automatic sleep classification method based on unsupervised feature classification algorithms recently developed, and on EEG entropy measures. This scheme extracts entropy metrics from EEG records to obtain a feature vector. Then, these features are optimized in terms of relevance using the Q-\u03b1 algorithm. Finally, the resulting set of features is entered into a clustering procedure to obtain a final segmentation of the sleep stages. The proposed method reached up to an average of 80% correctly classified stages for each patient separately while keeping the computational cost low.<\/jats:p>","DOI":"10.3390\/e16126573","type":"journal-article","created":{"date-parts":[[2014,12,17]],"date-time":"2014-12-17T10:19:28Z","timestamp":1418811568000},"page":"6573-6589","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":106,"title":["Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques"],"prefix":"10.3390","volume":"16","author":[{"given":"Jose","family":"Rodr\u00edguez-Sotelo","sequence":"first","affiliation":[{"name":"Grupo de Autom\u00e1tica, Universidad Aut\u00f3noma de Manizales, Antigua estaci\u00f3n del ferrocarril, Manizales 170002, Colombia"}]},{"given":"Alejandro","family":"Osorio-Forero","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n de Neuroaprendizaje, Universidad Aut\u00f3noma de Manizales, Antigua estaci\u00f3ndel ferrocarril, Manizales 170002, Colombia"}]},{"given":"Alejandro","family":"Jim\u00e9nez-Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n de Neuroaprendizaje, Universidad Aut\u00f3noma de Manizales, Antigua estaci\u00f3ndel ferrocarril, Manizales 170002, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0076-0515","authenticated-orcid":false,"given":"David","family":"Cuesta-Frau","sequence":"additional","affiliation":[{"name":"Technological Institute of Informatics, Polytechnic University of Valencia, Alcoi Campus, Plaza Ferr\u00e1ndiz y Carbonell, 2, Alcoi 03801, Spain"}]},{"given":"Eva","family":"Cirugeda-Rold\u00e1n","sequence":"additional","affiliation":[{"name":"Technological Institute of Informatics, Polytechnic University of Valencia, Alcoi Campus, Plaza Ferr\u00e1ndiz y Carbonell, 2, Alcoi 03801, Spain"}]},{"given":"Diego","family":"Peluffo","sequence":"additional","affiliation":[{"name":"Universidad Cooperativa de Colombia, Faculty of Medicine, Pasto 520002, Colombia"}]}],"member":"1968","published-online":{"date-parts":[[2014,12,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1016\/j.neuron.2010.11.032","article-title":"Sleep state switching","volume":"68","author":"Saper","year":"2010","journal-title":"Neuron"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Halasz, P., and Bodizs, R. 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