Linear and nonlinear quantification of respiratory sinus arrhythmia during propofol general anesthesia

Abstract

Quantitative evaluation of respiratory sinus arrhythmia (RSA) may provide important information in clinical practice of anesthesia and postoperative care. In this paper, we apply a point process method to assess dynamic RSA during propofol general anesthesia. Specifically, an inverse Gaussian probability distribution is used to model the heartbeat interval, whereas the instantaneous mean is identified by a linear or bilinear bivariate regression on the previous R-R intervals and respiratory measures. The estimated second-order bilinear interaction allows us to evaluate the nonlinear component of the RSA. The instantaneous RSA gain and phase can be estimated with an adaptive point process filter. The algorithm's ability to track non-stationary dynamics is demonstrated using one clinical recording. Our proposed statistical indices provide a valuable quantitative assessment of instantaneous cardiorespiratory control and heart rate variability (HRV) during general anesthesia.

Fig. 1

Selected snapshots of raw R-R and non-calibrated respiratory (RP, arbitrary unit) recordings from one subject during 5 consecutive epochs (with gradually increasing levels of drug concentration from 0 to 5 mcg/ml propofol).

Fig. 2

Two snapshot examples of dynamic tracking (using a linear model) for instantaneous HR, HRV, and RSA (dashed, dash-dot and solid lines mark the onset time of propofol anesthesia, phenylephrine, and ventilation, respectively). The red trace in the top panel shows the observed R-R intervals, which overlays the μ_RR in blue trace.

Fig. 3

Goodness-of-fit tests by KS plot and autocorrelation plot. The line or dots falling within 95% confidence bounds (dashed line) indicate a good fit of the probability model for heartbeat intervals.