A low-cost approach for wide-spread screening of periodic leg movements related to sleep disorders

Abstract

Periodic Limb Movements of Sleep are nocturnal movements that increasingly more research has begun to associate with important cardiovascular outcomes. Polysomnography is the dominant tool currently being used to detect these movements. However, patient inconvenience and high costs associated with polysomnography has probed the need for an alternative screening tool to be developed. In the current research, a more cost-effective screening method specialized to detect these sleep-related movements is explored. In particular, the possibility of using more easily acquirable signals like electrocardiography in order to classify Periodic Limb Movements is investigated. Signal processing tools and the use of machine learning algorithms for the classification of the sleep-related movements is proposed.