Automatic Event Detection of REM Sleep Without Atonia From Polysomnography Signals Using Deep Neural Networks

摘要

Rapid eye movement (REM) sleep behavior disorder (RBD) is a sleep disorder that features loss of atonia, or REM sleep without atonia (RSWA). RBD and RSWA are early manifestations of degenerative neurological diseases such as Parkinson's and Lewy Body Dementia. Accurate diagnosis of RBD is crucial for proper treatment planning and is invaluable for early detection of these neurodegenerative diseases. The current gold standard diagnosis of RSWA is through manual visual scoring by a clinician, which is labor-intensive, costly and error-prone. We develop a novel, efficient, and objective method using deep learning to detect RSWA events from polysomnography signals using a large cohort of 692 patients. Unlike previous automated methods that generate only a binary patient diagnosis, our method detects the location and class of all RSWA events. This finer-grained analysis forms the basis for subsequent diagnosis, and allows the quantification of event duration and frequency which in turn can help quantify disease load.