ANALYSIS OF OBSTRUCTIVE SLEEP APNEA DISORDER WITH ACCURACY PREDICTION USING SVM FOR SMART ENVIRONMENT

Abstract

One of the most crucial sleep disorders that has a direct hit on the quality of life is sleep apnea disorder. Declined memory and disorders related to personality are some of the consequences of Sleep Apnea Disorder. Identifying the difference between normal and abnormal levels of snoring sound is important for the detection of sleep apnea. Diagnosis of the sleep apnea usually takes place in hospitals under the direct supervision of medical professionals. Frequent visits to hospital for diagnosis tend to be an inconvenience to the elderly. Along with it, usage of body monitors also acts as a parameter in disrupting sleep. To overcome them, the analysis equipment is placed in the usual sleeping environment of the patient and LM393 sound sensor is used to detect the snoring levels. The analysis between the normal and the acquired snoring levels using threshold values and SVM helps confirm the presence or absence of the sleep apnea disorder.