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Automatic sleep stages classification using EEG entropy features and unsupervised pattern analysis techniques

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Automatic sleep stages classification using EEG entropy features and unsupervised pattern analysis techniques

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Nombre: Cuesta;José Luís ...
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dc.contributor.author Rodríguez-Sotelo, Jose Luis es_ES
dc.contributor.author Osorio-Forero, Alejandro es_ES
dc.contributor.author Jiménez-Rodríguez, Alejandro es_ES
dc.contributor.author Cuesta Frau, David es_ES
dc.contributor.author Cirugeda Roldán, Eva María es_ES
dc.contributor.author Peluffo, Diego es_ES
dc.date.accessioned 2015-06-22T09:31:00Z
dc.date.available 2015-06-22T09:31:00Z
dc.date.issued 2014
dc.identifier.issn 1099-4300
dc.identifier.uri http://hdl.handle.net/10251/52087
dc.description.abstract 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-α 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. es_ES
dc.description.sponsorship The authors would like to thank Universidad Autonoma de Manizales for financial support in the present work (Research project 328-038). This work has also been supported by the Spanish Ministry of Science and Innovation, research project TEC2009-14222. en_EN
dc.language Inglés es_ES
dc.publisher MDPI es_ES
dc.relation.ispartof Entropy es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Sleep stages es_ES
dc.subject Feature extraction es_ES
dc.subject Signal entropy es_ES
dc.subject Feature selection es_ES
dc.subject Relevance analysis es_ES
dc.subject Q-alpha es_ES
dc.subject Clustering es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title Automatic sleep stages classification using EEG entropy features and unsupervised pattern analysis techniques es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/e16126573
dc.relation.projectID info:eu-repo/grantAgreement/UAM//328-038/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//TEC2009-14222/ES/Interpretacion Y Caracterizacion De Metodos De Analisis De Complejidad En El Contexto Del Procesado Biomedico De La Señal/ / es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario Mixto Tecnológico de Informática - Institut Universitari Mixt Tecnològic d'Informàtica es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors es_ES
dc.description.bibliographicCitation Rodríguez-Sotelo, JL.; Osorio-Forero, A.; Jiménez-Rodríguez, A.; Cuesta Frau, D.; Cirugeda Roldán, EM.; Peluffo, D. (2014). Automatic sleep stages classification using EEG entropy features and unsupervised pattern analysis techniques. Entropy. 16(12):6573-6589. https://doi.org/10.3390/e16126573 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.3390/e16126573 es_ES
dc.description.upvformatpinicio 6573 es_ES
dc.description.upvformatpfin 6589 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 16 es_ES
dc.description.issue 12 es_ES
dc.relation.senia 282880
dc.contributor.funder Ministerio de Ciencia e Innovación es_ES
dc.contributor.funder Universidad Autónoma de Manizales es_ES
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