Estimation of Sleeping Heart Rate from Body Pressure Distribution Time Series by Morphological Component Analysis

Abstract

For effectively and efficiently screening potential sleep-disordered patients, it is desired to establish simplified and easy-to-use techniques to measure the sleep state in a sufficient accuracy without elaborate facilities.Pressure sensor mats are a promising candidate but methods to estimate the vital signs of subjects from their sensing data are to be devised.The present paper focuses on the estimation of the heart rate from the high-dimensional time series of sleeping body pressure and proposes to apply a blind source separation technique named Generalized Morphological Component Analysis (GMCA) to the problem.We tested GMCA by data sets with two different sleeping positions of prone and supine, demonstrating that the latter is much harder to separate the heartbeat signal from the body pressure distribution time series.A detailed discussion is made on how to improve the estimation accuracy for the supine position.