Inferring Autonomic Arousals using Periodic Leg Movements during Sleep

Abstract

Autonomic arousals are closely associated with an increase in heart rate and systemic blood pressure. Frequent arousals might result in cognitive and cardiovascular complications in addition to sleep disorders. To detect these autonomic arousals, subjects undergo polysomnographic(PSG) recording in a sleep lab which is cumbersome and expensive. In this theses, we study the following hypotheses: certain periodic leg movements during sleep are correlated with significant autonomic arousals. We propose a machine learning technique to predict autonomic arousals from characteristic leg movements. Using a custom designed ankle band our system can detect autonomic arousals with an accuracy of 74%. Our system is the first to use leg movement as a marker for autonomic arousals and be used as an in-home technique to study these arousals.