Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques. Rodríguez-Sotelo, J., Osorio-Forero, A., Jiménez-Rodríguez, A., Cuesta-Frau, D., Cirugeda-Roldán, E., & Peluffo, D. Entropy, 16(12):6573-6589, 12, 2014. ![Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex © 2014 by the authors. 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.
@article{
title = {Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques},
type = {article},
year = {2014},
keywords = {Clustering,Feature extraction,Feature selection,Q-α,Relevance analysis,Signal entropy,Sleep stages},
pages = {6573-6589},
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abstract = {© 2014 by the authors. 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.},
bibtype = {article},
author = {Rodríguez-Sotelo, Jose and Osorio-Forero, Alejandro and Jiménez-Rodríguez, Alejandro and Cuesta-Frau, David and Cirugeda-Roldán, Eva and Peluffo, Diego},
doi = {10.3390/e16126573},
journal = {Entropy},
number = {12}
}
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