Key result
The continuous estimation method of respiratory frequency using the number of extreme points of heart rate time series achieved a minimum error rate of 13.8% with an 11-second time lag.
Why the study?
Can a simple continuous estimation method using extreme points of heart rate time series accurately measure respiratory frequency during sleep?
Observational (n=9)
No
Can a simple continuous estimation method using extreme points of heart rate time series accurately measure respiratory frequency during sleep?
A novel, simple algorithm using heart rate time series extreme points can continuously estimate respiratory frequency during sleep with reasonable accuracy.
Answers are grounded in this paper and cite their sources.
Supports simple HR-based respiratory frequency estimation in sleep; leaves open prospective validation before clinical adoption.
It is reported that frequency component of approximately 0.25Hz of heart rate time series (RSA) is corresponding to the respiratory frequency. In this paper, we proposed that continuous estimation method of respiratory fequency during sleep using the number of extreme points of heart rate time series in real time. Equation for calculation of the method is very simple and the method can continuously calculate frequency by window width of about 18 beats. To evaluate accuracy of proposal method, RSA frequency was calculated using proposal method from the heart rate time series during supine rest. Result, minimum error rate was observed when RSA had time lag for about 11s and error rate was about 13.8%. Result of estimating RSA frequency time series during sleep, it varied regularly during non-REM and varied irregularly during REM. This result is similar as report of previous study about respiratory variability during sleep. Therefore, it is considered that proposal method possible to apply respiratory monitoring system during sleep.
No takes yet. Share an insight, caveat, or question.
Yoshida et al. (2007) conducted an observational in Healthy (n=9). Continuous estimation method of respiratory frequency using the number of extreme points (NEP) of heart rate time series vs. Actual respiratory frequency (respiratory curve) was evaluated on Error rate of estimated respiratory frequency compared to actual respiratory frequency. The continuous estimation method of respiratory frequency using the number of extreme points of heart rate time series achieved a minimum error rate of 13.8% with an 11-second time lag.
Synapse has enriched 10 related papers. Consider them for comparative context: