A snoring sound analysis application using k-mean clustering method on mobile devices

Abstract

Patientswith chronic diseases are increasing around the globe. Healthcare professionals attempt to find possible causes of the chronic diseases. One of the most possible causes is the sleep disorder. Sleep apnea, OSA and CSA, may be an evidence of chronic diseases. In order to detect the sleep apnea, the polysomnography (PSG) or the sleep test is required for patients. A number of parameters will be collected on patients whilst they are asleep. However, due to the limitation of the PSG test in some countries, researchers attempt to find other available alternative approaches. In this research work, a mobile application has been constructed to perform a screening test of OSA. With our initial experiment test, 74.70% instances have been correctly classified. An application of SMOTE into a minority class is performed and achieves up to 80.10% correctly classified instances. Limitations of the mobile application and our technique have also discussed.