Automatic sleep stages classification using EEG entropy features and unsupervised pattern analysis techniques

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.The authors would like to thank Universidad Autonoma de Manizales for financial support in the present work (Research project 328-038). This work has also been supported by the Spanish Ministry of Science and Innovation, research project TEC2009-14222.Rodríguez-Sotelo, JL.; Osorio-Forero, A.; Jiménez-Rodríguez, A.; Cuesta Frau, D.; Cirugeda Roldán, EM.; Peluffo, D. (2014). Automatic sleep stages classification using EEG entropy features and unsupervised pattern analysis techniques. Entropy. 16(12):6573-6589. https://doi.org/10.3390/e16126573S657365891612Saper, C. B., Fuller, P. M., Pedersen, N. P., Lu, J., & Scammell, T. E. (2010). Sleep State Switching. Neuron, 68(6), 1023-1042. doi:10.1016/j.neuron.2010.11.032RAUCHS, G., DESGRANGES, B., FORET, J., & EUSTACHE, F. (2005). The relationships between memory systems and sleep stages. 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