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Entropy 2014, 16(12), 6573-6589; doi:10.3390/e16126573

Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques

1
Grupo de Automática, Universidad Autónoma de Manizales, Antigua estación del ferrocarril, Manizales 170002, Colombia
2
Grupo de Investigación de Neuroaprendizaje, Universidad Autónoma de Manizales, Antigua estacióndel ferrocarril, Manizales 170002, Colombia
3
Technological Institute of Informatics, Polytechnic University of Valencia, Alcoi Campus, Plaza Ferrándiz y Carbonell, 2, Alcoi 03801, Spain
4
Universidad Cooperativa de Colombia, Faculty of Medicine, Pasto 520002, Colombia
*
Author to whom correspondence should be addressed.
Received: 18 July 2014 / Revised: 28 November 2014 / Accepted: 9 December 2014 / Published: 17 December 2014
(This article belongs to the Special Issue Entropy and Electroencephalography)
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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. View Full-Text
Keywords: sleep stages; feature extraction; signal entropy; feature selection; relevance analysis; Q-α clustering sleep stages; feature extraction; signal entropy; feature selection; relevance analysis; Q-α clustering
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Rodríguez-Sotelo, J.L.; Osorio-Forero, A.; Jiménez-Rodríguez, A.; Cuesta-Frau, D.; Cirugeda-Roldán, E.; Peluffo, D. Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques. Entropy 2014, 16, 6573-6589.

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