Am. J. Respir. Crit. Care Med.,
Volume 165, Number 7, April 2002, 940-944
Diagnosis of Sleep Apnea by Automatic Analysis of Nasal
Pressure and Forced Oscillation Impedance
Holger
Steltner,
Richard
Staats,
Jens
Timmer,
Michael
Vogel,
Josef
Guttmann,
Heinrich
Matthys,
and
J.
Christian Virchow
Center for Data Analysis and Modeling, University of Freiburg; and Department of Pneumology and Section for Experimental Anesthesiology,
University Hospital Freiburg, Freiburg, Germany
Detecting and differentiating central and obstructive respiratory
events is an important aspect of the diagnosis of sleep-related breathing disorders with respect to the choice of an appropriate treatment. The purpose of this study was to evaluate the performance of a new algorithm for automated detection and classification of apneas and hypopneas, compared with visual analysis of
standard polysomnographic signals. The algorithm is based on
time series analysis of nasal mask pressure and a forced oscillation
signal related to mechanical respiratory input impedance, measured at a frequency of 20 Hz throughout the night. The method
was applied to all-night measurements on 19 subjects. Two experts in sleep medicine independently scored the corresponding
simultaneously recorded polysomnographic signals. Evaluating
the agreement between two scorers by a weighted kappa statistic
on a second-by-second basis, we found that inter-expert variability
and the discrepancy between automatic analysis and visual analysis performed by an expert were not significantly different. Implementation of this algorithm in a device for home monitoring of
breathing during sleep might aid in the differential diagnosis of
sleep-related breathing disorders and/or as a means for follow-up
and treatment control.
