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General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(5): 685-688 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.215

Genetic Fuzzy Approach based Sleep Apnea/Hypopnea Detection

Yashar Maali and Adel Al-Jumaily
Abstract—Sleep Apnea (SA) is one of the most common and important part of sleep disorders. Unfortunately, sleep apnea may be going undiagnosed for years, because of the person’s unawareness. The common diagnose procedure usually required an overnight sleep test. During the test, a recording of many biosignals, which related to breath, are obtained by polysomnography machine to detect this syndrome. The manual process for detecting the sleep Apnea by analysis the recording data is highly cost and time consuming. So, several works tried to develop systems that achieve this automatically. This paper proposes a genetic fuzzy approach for detecting Apnea/Hypopnea events by using Air flow, thoracic and abdominal respiratory movement signals and Oxygen desaturation as the inputs. Results show efficiently of this approach.

Index Terms—Sleep disorders, genetic fuzzy algorithm, fuzzy sets.

The authors are with the University of Technology, Sydney Faculty of Engineering and IT Sydney, Australia (Yashar.Maali@student.uts.edu.au; Adel@eng.uts.edu.au).


Cite:Yashar Maali and Adel Al-Jumaily, "Genetic Fuzzy Approach based Sleep Apnea/Hypopnea Detection," International Journal of Machine Learning and Computing vol.2, no. 5, pp. 685-688, 2012.

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