Extraction of Respiratory Motion Features from Sleeping Body Pressure Distribution Time Series by Generalized Morphological Component Analysis

Abstract

For effectively and efficiently screening potential sleep-disordered patients, it is desired to establish sim- plified 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 sub- jects from their sensing data are to be devised. For non-invasive sleep disorders screening, Generalized Morphological Component Analysis (GMCA), which is a kind of blind source separation methods, is ap- plied to estimate the respiratory effort curve and its spatial distribution from time series of sleeping body pressure distribution. This study explores respiratory motion features that are effective for detecting and discriminating respiratory events such as apnea and hypopnea by examining the results of applying GMCA to the measurement data of the pressure sensor mat collected in polysomnography tests.