A study on Classification Algorithm of REM Sleep Behavior Disorder

Abstract

Sleep disorders are modern diseases that have recently increased in prevalence. In particular, the rapid-eye-movement (REM) sleep behavior disorder (RBD) is well-known. As a hallmark of the RBD, we can find the loss of skeletal muscle atonia during the REM sleep, which is called the REM sleep without atonia (RWA), in the polysomnography (PSG). Therefore, measurement of muscle activity is required for the diagnosis of RBD. Although close follow-up of patients' recovery process is required and several visual inspection methods have been developed for the PSG reading, clear diagnostic criteria for assessing RBD severity have not been established because of the complicated nature of the procedure. In this study, the author constructed automated algorithms based on the AASM (American Academy of Sleep Medicine) scoring manual. The percentage ratio of RWA in the REM sleep period was calculated by the algorithms. The results evaluated by the automated algorithms were compared to the diagnosis by the visual inspection method. In addition, nonlinear discriminant analysis was employed to examine whether the healthy subjects group was significantly different from the group of suspected RBD patients. The author has succeeded in finding an automated algorithm to yield results that are not significantly different from those of visual inspection diagnosis.