Soft computing technique based on ANFIS for the early detection of sleep disorders

Abstract

Sleep is a great natural occurring state in which everything is forgotten for a while, become fresh and ready for next coming daily routine exercises. Any person can lag in these exercises, if their sleep is not well taken. But, sometime it is observed that the sleep gets disturbed due to some awkward behaviors known as sleep disorders. The various intelligent techniques/methods are proposed for the diagnosis, detection and classification of sleep disorders, sleep spindles and other sleep related events. In this paper, a system is proposed based on physio-psycho symptoms by using an adaptive neuro-fuzzy inference system (ANFIS). The major concern is taken towards the early detection of only four sleep disorders that are Sleep Apnea, Insomnia, Parasomnia and Snoring. The prior detection of all these disorders are having a prime importance, as it can help a person to safe itself from the further effects that can arise from these sleep disorders. To implement the system, the data set is collected from various physicians comprising the record of 96 patients.