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

研究成果: Conference article同行評審

7 引文 斯高帕斯(Scopus)

摘要

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.

原文English
期刊IEEE International Conference on Fuzzy Systems
DOIs
出版狀態Published - 22 十一月 2013
事件2013 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2013 - Hyderabad, India
持續時間: 7 七月 201310 七月 2013

指紋 深入研究「Applying the fuzzy C-means based dimension reduction to improve the sleep classification system」主題。共同形成了獨特的指紋。

引用此