Detection and Classification of Sleep-Disordered Breathing Using Acoustic Respiratory Input Impedance and Nasal Pressure

Steltner, Holger; Staats, Richard; Vogel, Michael; Virchow, C; Matthys, H; Guttmann, J.; Timmer, Jens

doi:10.1007/3-540-39949-6_10

Cited by 1 publication

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The algorithm is described in more detail in Appendix B in the online data supplement. A first version has been published in part (29). Briefly, FOT and mask pressure time series are analyzed on a breathby-breath basis.…”

Section: Automatic Analysismentioning

confidence: 99%

Diagnosis of Sleep Apnea by Automatic Analysis of Nasal Pressure and Forced Oscillation Impedance

Steltner¹,

Staats²,

Timmer³

et al. 2002

Am J Respir Crit Care Med

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Automatic Analysismentioning

confidence: 99%