Applying the fuzzy C-means based dimension reduction to improve the sleep classification system

Chih Sheng Huang*, Chun Ling Lin, Wen Yu Yang, Li-Wei Ko, Sheng Yi Liu, Chin Teng Lin

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

7 Scopus citations


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.

Original languageEnglish
JournalIEEE International Conference on Fuzzy Systems
StatePublished - 22 Nov 2013
Event2013 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2013 - Hyderabad, India
Duration: 7 Jul 201310 Jul 2013


  • Electroencephalography
  • Sleep classification system

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