Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques
AbstractSleep 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
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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.
Rodríguez-Sotelo JL, 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(12):6573-6589.Chicago/Turabian Style
Rodríguez-Sotelo, Jose L.; Osorio-Forero, Alejandro; Jiménez-Rodríguez, Alejandro; Cuesta-Frau, David; Cirugeda-Roldán, Eva; Peluffo, Diego. 2014. "Automatic Sleep Stages Classification Using EEG Entropy Features and Unsupervised Pattern Analysis Techniques." Entropy 16, no. 12: 6573-6589.