Statistical strategies to quantify respiratory sinus arrhythmia: are commonly used metrics equivalent?

Gregory F Lewis, Senta A Furman, Martha F McCool, Stephen W Porges, Gregory F Lewis, Senta A Furman, Martha F McCool, Stephen W Porges

Abstract

Three frequently used RSA metrics are investigated to document violations of assumptions for parametric analyses, moderation by respiration, influences of nonstationarity, and sensitivity to vagal blockade. Although all metrics are highly correlated, new findings illustrate that the metrics are noticeably different on the above dimensions. Only one method conforms to the assumptions for parametric analyses, is not moderated by respiration, is not influenced by nonstationarity, and reliably generates stronger effect sizes. Moreover, this method is also the most sensitive to vagal blockade. Specific features of this method may provide insights into improving the statistical characteristics of other commonly used RSA metrics. These data provide the evidence to question, based on statistical grounds, published reports using particular metrics of RSA.

Copyright © 2011 Elsevier B.V. All rights reserved.

Figures

Figure 1
Figure 1
Violations of stationary mean in the heart period time series.
Figure 2
Figure 2
Bootstrap estimates of Glass’s Δ. Error bars denote the 95% confidence interval for Glass’s Δ. Glass’s Δ greater than 0.8 is interpreted as large (Cohen, 1977). N = 25.
Figure 3
Figure 3
Distribution of each measure in the repeated measures MANOVA: Location(2)×Time(2)×Infusion(2). Error bars denote +/− 2 standard errors of the mean. N=47.
Figure 4
Figure 4
Effect size of the repeated measures MANOVA for each metric. N = 47/
Figure 5
Figure 5
Simple slopes of ΔP2T on ΔHeart Period at high and zero change in respiration rate following saline infusion. The high change group represents a sample drawn at +1 SD of the absolute change in respiration rate, 0.067 Hz. The zero change group is drawn at exactly 0 change. −1 SD is exactly 0.00085 Hz. Slopes calculated with software designed by Hayes and Matthes (2009). N = 24.
Figure 6
Figure 6
Simple slopes of P2T on RSAP-B at high and low respiration rates. Slopes calculated with software designed by Hayes and Matthes (2009).
Figure 7
Figure 7
Simple slopes of P2T on RSAP-B at high and low tidal volumes. Slopes calculated with software designed by Hayes and Matthes (2009).
Figure 8
Figure 8
Simple slopes of P2T on RSAP-B at high and low mean difference among 15 bins in the heart period time series. Slopes calculated with software designed by Hayes and Matthes (2009).
Figure 9
Figure 9
Simple slopes of HF on RSAP-B at high and low mean difference among 15 bins in the heart period time series. Slopes calculated with software designed by Hayes and Matthes (2009).
Figure 10
Figure 10
Bootstrap estimates of Glass’s Δ. Error bars denote the 95% confidence interval for Glass’s Δ. Glass’s Δ greater than 0.8 is interpreted as large (Cohen, 1977). N = 25.

Source: PubMed

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