Entity

Time filter

Source Type


Huang C.-S.,National Chiao Tung University | Huang C.-S.,Institute of Electrical Control | Lin C.-L.,National Chiao Tung University | Yang W.-Y.,National Chiao Tung University | And 6 more authors.
IEEE International Conference on Fuzzy Systems | Year: 2013

Having a well sleep quality is important factor in our daily life. The evaluation of sleep stages has become an important issue due to the distribution of sleep stages across a whole night relates to sleep quality. This study aims to propose a sleep classification system, consists of a preliminary wake detection rule, sleep feature extraction, fuzzy c-means based dimension reduction, support vector machine with radial basis function kernel, and adaptive adjustment scheme, with only FP1 and FP2 electroencephalography. Compared with the results from the sleep technologist, the average accuracy and Kappa coefficient of the proposed sleep classification system is 70.92% and 0.6130, respectively, for individual 10 normal subjects. Thus, the proposed sleep classification system could provide a preliminary report of sleep stages to assistant doctors to make decision if a patient needs to have a detailed testing in a sleep laboratory. © 2013 IEEE. Source

Discover hidden collaborations