Entropy | |
Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques | |
Jose Luis Rodríguez-Sotelo2  Alejandro Osorio-Forero3  Alejandro Jiménez-Rodríguez3  David Cuesta-Frau1  Eva Cirugeda-Roldán1  | |
[1] Technological Institute of Informatics, Polytechnic University of Valencia, Alcoi Campus, Plaza Ferrándiz y Carbonell, 2, Alcoi 03801, |
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关键词: sleep stages; feature extraction; signal entropy; feature selection; relevance analysis; Q-α; clustering; | |
DOI : 10.3390/e16126573 | |
来源: mdpi | |
【 摘 要 】
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.
【 授权许可】
CC BY
© 2014 by the authors; licensee MDPI, Basel, Switzerland
【 预 览 】
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RO202003190018478ZK.pdf | 1157KB | download |