MONITORING OF NON-INVASIVE VITAL SIGNS FOR DETECTION OF SLEEP APNEA

Abstract

Sleep apnea (SA) syndrome is a respiratory disorder that occurs during the sleep. Polysomnography (PSG) has been widely applied by clinicians as a gold standard in the clinical diagnosis of SA syndrome. However, the use of PSG is inconvenient, intrusive, and significantly affects the sleep quality of patient. In this paper, we provide a nonintrusive solution for SA detection. Specifically, a force sensor was employed for the noninvasive vital sign acquisition during the patient’s sleep, where the respiratory signal was extracted adaptively by using the morphological filter. It was observed that the morphological variations before and during the occurrence of the SA events were significant for the SA discrimination. By taking advantage of the differential features with respect to the respiratory signal, the recognition of the SA event was performed using classifiers. For validation, the all-night PSG recordings of 12 volunteers with 8 SA syndrome patients were obtained from the National Clinical Research Center for Respiratory Disease. Numerical results showed that the proposed scheme achieved an averaged accuracy, sensitivity and specificity of 83.67%, 58.57% and 85.13%, respectively, for the SA recognition.