Original Article

Sleep and Breathing

, Volume 12, Issue 4, pp 339-345

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

Automated detection of sleep disordered breathing using a nasal pressure monitoring device

  • Sukhdev S. GroverAffiliated withSleep Center of Greater Pittsburgh
  • , Stephen D. PittmanAffiliated withDivision of Sleep Medicine, Brigham and Womens HospitalSleep and Home Respiratory Group, Respironics, Inc. Email author 


To assess the accuracy of a single channel portable monitoring device (RUSleeping™ RTS, Respironics, Murrysville, PA) that measures nasal pressure (a surrogate for airflow) to detect sleep disordered breathing (SDB). Twenty-five adult patients referred to a community sleep laboratory with suspected obstructive sleep apnea (OSA) participated in this investigation. The portable monitoring device was used in the sleep laboratory to acquire data concurrently with a standard multi-channel polysomnogram (PSG) to assess SDB. Respiratory events were scored manually on the PSG using standard criteria for clinical research to quantify an apnea–hypopnea index (AHI) based on events during sleep. The portable monitoring device automatically calculated an unedited respiratory event index (REI) based on recording time. These data were then compared using the Pearson product–moment correlation coefficient, Bland–Altman analysis, receiver operating characteristic (ROC) curves, and likelihood ratios. All 25 subjects completed the study. Mean age of subjects was 42.4 ± 12.9 years and mean body mass index was 31.0 ± 7.4 kg m−2. There was good agreement between the REI and the AHI (R = 0.77, p < 0.001, mean difference 2.6 events per hour [2 SD: 39.8] using a Bland–Altman plot). The area under the ROC curve for detecting SDB (PSG AHI greater than or equal to five events per hour) with the REI was 0.94 (95% CI 0.84–1.0). For an REI >11.9 events per hour, the sensitivity was 0.89 (95% CI 0.65–0.99) and the specificity was 0.86 (95% CI 0.42–1.0) with a likelihood ratio of 6.2 for a positive test (LR+) and 0.13 for a negative test (LR−). Similar results were observed for detecting moderate–severe SDB (PSG AHI ≥ 15 events h−1) using REI >15.2 events h−1. In a population of subjects with suspected OSA, this portable monitoring device can automatically quantify an REI that compares well to the AHI scored manually on a concurrent PSG. Such a device may prove useful to assess SDB in high risk populations with self-administered testing in ambulatory settings such as the home.


Automated detection OSA Sleep apnea SDB Sleep-disordered breathing Apnea hypopnea Nasal pressure Portable monitoring RUSleeping