Detection of high-frequency respiratory movements during sleep

Sleep-related breathing disorders are common in adults and they have a sign ificant impact on vigilance and quality of life. Previous studies have show n the validity of the static-charge-sensitive bed (SCSB) in monitoring brea thing abnormalities during sleep. A whole nights sleep study produces a sig nal with considerable length, and therefore an automated analysis system wo uld be of great need. In this work we focus on detection of high-frequency respiratory movement (HFRM) patterns which are related to increased respira tory efforts. The paper documents four methods to automatically detect thes e patterns. The first two are based on classical statistical tests applied to the SCSB signal, and the other two use spectral characteristics in order to adaptively segment the SCSB signal. Finally we adjust each method to de tect patterns that coincide with the HFRMs determined by an expert, and eva luate the performance of the methods using independent test data. (C) 2000 Elsevier Science Ireland Ltd. All rights reserved.