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

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-a 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. © 2014 by the authors.

• Autores: Rodríguez-Sotelo J.L.; Osorio-Forero A.; Jiménez-Rodríguez A.; Cuesta-Frau D.; Cirugeda-Roldán E.; Peluffo Ordoñez, Diego H.
• Tipo de recurso: Article of journal
• Fecha de publicación: 2014
• Institución: Universidad Cooperativa de Colombia
• Repositorio: Repositorio UCC
• OAI Identifier: oai:repository.ucc.edu.co:20.500.12494/42807
• Acceso en línea: https://doi.org/10.5935/1678-9741.20140101; https://www.sciencedirect.com/science/article/abs/pii/S0109564112004034; https://hdl.handle.net/20.500.12494/42807
• Palabra clave: Clustering; Feature extraction; Feature selection; Q-a; Relevance analysis; Signal entropy; Sleep stages
• Rights: closedAccess
• License: http://purl.org/coar/access_right/c_14cb