A non-contact vision-based cardiopulmonary monitoring system achieved a mean percentage error of 3.4% for respiratory rate and 5.0% for heart rate compared to polysomnography across five sleeping positions.
Observational (n=17)
No
Does a noncontact computer vision-based method accurately estimate respiratory and heart rates compared to polysomnography in healthy participants during simulated sleep?
A noncontact computer vision-based method using an infrared camera can accurately estimate respiratory and heart rates in various sleeping positions, providing a potential unobtrusive solution for sleep apnea monitoring.
Individuals with obstructive sleep apnea (OSA) can experience partial or complete collapse of the upper airway during sleep. This condition affects between 10-17% of adult men and 3-9% of adult women, requiring arousal to resume regular breathing. Frequent arousals disrupt proper sleeping patterns and cause daytime sleepiness. Untreated OSA has been linked to serious medical issues including cardiovascular disease and diabetes. Unfortunately, diagnosis rates are low (∼20%) and current sleep monitoring options are expensive, time consuming, and uncomfortable. Toward the development of a convenient, noncontact OSA monitoring system, this paper presents a simple, computer vision-based method to monitor cardiopulmonary signals (respiratory and heart rates) during sleep. System testing was performed with 17 healthy participants in five different simulated sleep positions. To monitor cardiopulmonary rates, distinctive points are automatically detected and tracked in infrared image sequences. Blind source separation is applied to extract candidate signals of interest. The optimal respiratory and heart rates are determined using periodicity measures based on spectral analysis. Estimates were validated by comparison to polysomnography recordings. The system achieved a mean percentage error of 3.4% and 5.0% for respiratory rate and heart rate, respectively. This study represents an important step in building an accessible, unobtrusive solution for sleep apnea diagnosis.
Li et al. (Wed,) conducted a observational in Healthy (n=17). Non-contact vision-based cardiopulmonary monitoring vs. Polysomnography (PSG) was evaluated on Mean percentage error (MPE) for respiratory rate. A non-contact vision-based cardiopulmonary monitoring system achieved a mean percentage error of 3.4% for respiratory rate and 5.0% for heart rate compared to polysomnography across five sleeping positions.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: