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

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.

Stress classification by separation of respiratory modulations in heart rate variability using orthogonal subspace projection

“…Several remedies are proposed to deal with this issue, e.g. the use of alternative measures of RSA [3], [5], but so far, no agreement has been reached about a valid alternative for the conventional RSA definition.…”

When Interpolation-Induced Reflection Artifact Meets Time–Frequency Analysis

“…Right: the reassigned STFT result of the CS interpolation. Note that in the middle, middle right and right subfigures, in addition to f 2 (t), we could see components with IF ψ 2 − φ marked as (1), 2ψ 2 − φ marked as (2) and ψ 2 + φ marked as (3). Note that the artificial component with IF ψ 2 − φ is the reflection of φ associated with the INF.…”

“…In particular, the time-varying oscillatory pattern inside the electrocardiogram (ECG) or respiratory signal contains abundant health information, for example, the heart rate variability (HRV) [1,2,3] hidden inside the R peak to R peak interval (RRI) time series and the instantaneous heart rate (IHR), the breathing pattern variability (BPV) representing the time varying rate of the respiratory signal [4,5,6]. It is well known that power spectrum is not a suitable tool when the time-varying dynamics in the signal is the main target to analyze, as power spectrum reflects only the global oscillatory information, and hence could not properly extract the dynamical information, which is local in nature.…”

Untitled

“…Because of the statistical complexity of interpreting vagal tone data, many published reports require critical analysis to draw consistent conclusions, as different analysis methods can result in different interpretations of the same data (Lewis, Furman, McCool, & Porges, 2012). Publications often lack precision and accurate editing of artifact (electrical activity coming from places other than the brain, such as jaw clenching), and measures of vagal tone are often misinterpreted (Porges, 2007).…”