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

Entropy

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, Spain; E-Mail:;Grupo de Automática, Universidad Autónoma de Manizales, Antigua estación del ferrocarril, Manizales 170002, Colombia; E-Mail:;Grupo de Investigación de Neuroaprendizaje, Universidad Autónoma de Manizales, Antigua estación del ferrocarril, Manizales 170002, Colombia; E-Mails:

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