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

Entropy - Tập 16 Số 12 - Trang 6573-6589
José Luis Rodríguez-Sotelo1, Alejandro Osorio-Forero2, Alejandro Jiménez-Rodríguez2, David Cuesta–Frau3, Eva Cirugeda3, Diego H. Peluffo-Ordóńez4
1Grupo de Automática, Universidad Autónoma de Manizales, Antigua estación del ferrocarril, Manizales 170002, Colombia
2Grupo de Investigación de Neuroaprendizaje, Universidad Autónoma de Manizales, Antigua estacióndel ferrocarril, Manizales 170002, Colombia
3Technological Institute of Informatics, Polytechnic University of Valencia, Alcoi Campus, Plaza Ferrándiz y Carbonell, 2, Alcoi 03801, Spain
4Universidad Cooperativa de Colombia, Faculty of Medicine, Pasto 520002, Colombia

Tóm tắt

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.

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