Detection of high-frequency respiratory movements during sleep

Sleep-related breathing disorders are common in adults and they have a significant impact on vigilance and quality of life. Previous studies have shown the validity of the static-charge-sensitive bed (SCSB) in monitoring breathing abnormalities during sleep. A whole nights sleep study produces a signal with considerable length, and therefore an automated analysis system would be of great need. In this work we focus on detection of high-frequency respiratory movement (HFRM) patterns which are related to increased respiratory efforts. The paper documents four methods to automatically detect these 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 detect patterns that coincide with the HFRMs determined by an expert, and evaluate the performance of the methods using independent test data.