Genetic Fuzzy Approach based Sleep Apnea/Hypopnea Detection

Publisher:
IACSIT Press
Publication Type:
Journal Article
Citation:
International Journal of Machine Learning and Computing, 2012, 2 (5), pp. 685 - 688
Issue Date:
2012-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2012000412OK.pdf1.53 MB
Adobe PDF
Sleep Apnea (SA) is one of the most common andimportant part of sleep disorders. Unfortunately, sleep apneamay be going undiagnosed for years, because of the personsunawareness. The common diagnose procedure usuallyrequired an overnight sleep test. During the test, a recording ofmany biosignals, which related to breath, are obtained bypolysomnography machine to detect this syndrome. Themanual process for detecting the sleep Apnea by analysis therecording data is highly cost and time consuming. So, severalworks tried to develop systems that achieve this automatically.This paper proposes a genetic fuzzy approach for detectingApnea/Hypopnea events by using Air flow, thoracic andabdominal respiratory movement signals and Oxygen desaturation as the inputs. Results show efficiently of this approach.
Please use this identifier to cite or link to this item: