Sleep Analysis Based on Non-Load Detection Technique and Fuzzy Logic

Article Preview

Abstract:

Polysomnogram (PSG) has been the standard of the Sleep Analysis for many years. However it is complicated to operate, and attaches a lot of electrodes on the body. So the development of a non-load sleep architecture stage is necessary. Under the support of non-load detection technique, a new method for sleep architecture which takes advantage of variation of heart beat interval, respiration period body movement and the other physiological parameters during sleep has been studied. Due to sleep architecture it differs person to person, so the result of sleep architecture stage involves great uncertainty, using fuzzy logic theory achieves uncertainty analysis, and it can produce the more accurately result. This method has been tested on with PSG result as contrast, sleep analysis based on non-load detection technique and fuzzy logic has a high compliance rate. The method is qualified as being useful in clinical application.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

1458-1461

Citation:

Online since:

September 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Yang Jun, Yu Mengsun, Wang Hongshan, Zhang Hongjin, Zhao Xianliang, A Non-EEG Approach to Sleep Analysis by Fusing Multi-Parameter Information, J. Chinese Journal of Biomedical Engineering. (3) 2006 315-321.

Google Scholar

[2] Information on http: /wiki. cnki. com. cn/HotWord/73331. htm.

Google Scholar

[3] M. Yu, J. Yang, Y. Zhou. Study on sleep monitering with micromovement sensitive mattress monitering system, J. Chinese journal of aerospace medicine. 10(1999) 40-45.

Google Scholar

[4] Zadeh L A, Fuzzy sets, J. Information and Control. (50) 1965 856-865.

Google Scholar

[5] Chen Jie, Sun Zhiying, Tan Manzhi, Application of fuzzy logic in landuse classification based on remote sensing data, J. Acta Pedologica sinica. 44 (2007) 769-774.

Google Scholar

[6] Cheng Yi, Zhuang Cheng, An Xienan, A general fuzzy logic controller and its application, J. Acta Automatica sinica. (6) 1992 647-653.

Google Scholar

[7] Benu U C, Hofmann P, Willhauck G, et al . Multi-resolution, object_oriented fuzzy analysis of remote sensing data for GIS-ready information, J. Journal of Photogrammetry and Remote Sensing. (58) 239-258.

DOI: 10.1016/j.isprsjprs.2003.10.002

Google Scholar

[8] Hu Chunbin, An Shi, Wang Jian, A route selection model based on fuzzy logic, J. Urban Transport of China. (5) 2009 91-95.

Google Scholar