Noncontact respiratory monitoring during sleep: comparison of the touchless flow signal with respiratory inductance plethysmography flow (RIP flow ) signal to assess respiratory events
Year: 2024
Type: article
Study design: Observational Study
Abstract: A nonintrusive sleep monitoring technology based on the data from a commercially available depth sensing camera has been utilized for respiratory monitoring and shown to have good performance in assessing respiratory rates across a range of rates. This noncontact or touchless , technology allows continuous respiratory monitoring without attaching probes to the patient. We have noticed a strikingly similar morphology between the noncontact flow signal (NCM flow ) and the flow signal derived using the chest and abdomen respiratory inductance plethysmography (RIP flow ) bands. Here we report on a quantitative assessment of the morphological similarity between the two signals for a cohort of patients undergoing polysomnography. We collected depth data from 25 sleep patients undergoing attended polysomnography. Correlation and mutual trending were assessed between the NCM flow and RIP flow signals in individuals undergoing diagnostic and split night polysomnography using Pearson correlation and concordance between the NCM flow and RIP flow signals. Strong correlation was observed across all patients between the NCM flow and RIP flow signals (range: 0.78–0.98, mean: 0.89 ± 0.06). Similarly, high values of concordance were achieved between the NCM flow and RIP flow signals (range: 0.85–1.0, mean: 0.96 ± 0.04). The high values of correlation and concordances confirm that the NCM flow signal can potentially be used as a surrogate for RIP flow signal during sleep. Our findings strongly support the potential for noncontact continuous monitoring of respiratory disturbances during sleep. Addison PS, Brewer L, Sundar KM, et al. Noncontact respiratory monitoring during sleep: comparison of the touchless flow signal with respiratory inductance plethysmography flow (RIP flow ) signal to assess respiratory events. J Clin Sleep Med . 2025;21(4):713–721.
DOI: 10.5664/jcsm.11486
Institutions: Medtronic (United Kingdom), University of Utah
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4404608023
