Linear and Non-linear Quantification of the Respiratory Sinus Arrhythmia Using Support Vector Machines

Respiratory sinus arrhythmia magnitude quantification as a potential marker of stress and pain in cows and sheep

Respiratory sinus arrhythmia (RSA) provides a quantitative evaluation of 'vagal tone' and may be used for pain and stress assessment in livestock. The aim was to quantify the magnitude of RSA in cows and sheep. In 7 cows and 4 sheep standing at rest we measured the 3-lead electrocardiogram (ECG) together with the pneumogram, to identify inspiration and expiration. For each breath, RSA was the difference in instantaneous heart rate (HR) between the inspiratory peak and the expiratory trough, in percent of mean HR. The resting breathing rates (28 ± 2 and 32 ± 5 breaths/min in cows and sheep, respectively) were about twice those expected for similar size non-ruminants, in conformity with previous reports. Both species had long-period (>15 s) HR fluctuations. The average values of RSA, 1.4 ± 0.2% in cows and 7.8 ± 3.1 in sheep, were lower than those previously computed by an identical approach in humans (12%), dogs (40%) and horses (9%). In conclusion, by breath-by-breath analysis of instantaneous HR we measured RSA in both cows and sheep. Results from the present study represent a preliminary step in assessing whether or not RSA could be used as a biomarker for stress or pain in ruminants.

Causality in cardiorespiratory signals in pediatric cardiac patients

Four different Granger causality-based methods - one linear and three nonlinear (Granger Causality, Kernel Granger Causality, large-scale Nonlinear Granger Causality, and Neural Network Granger Causality) were used for assessment and causal-based quantification of the respiratory sinus arrythmia (RSA) in the group of pediatric cardiac patients, based on the single-lead ECG and impedance pneumography signals (the latter as the tidal volume curve equivalent). Each method was able to detect the dependency (in terms of causal inference) between respiratory and cardiac signals. The correlations between quantified RSA and the demographic parameters were also studied, but the results differ for each method. Clinical relevance- The presented methods (among which NNGC seems to be the most valid) allow for quantification of RSA and study of dependency between tidal volume and RR intervals which may help to better understand association between respiratory and cardiovascular systems in different populations.

Technical aspects of cardiorespiratory estimation using subspace projections and cross entropy

BACKGROUND

Respiratory sinus arrhythmia (RSA) is a form of cardiorespiratory coupling. Its quantification has been suggested as a biomarker to diagnose different diseases. Two state-of-the-art methods, based on subspace projections and entropy, are used to estimate the RSA strength and are evaluated in this paper. Their computation requires the selection of a model order, and their performance is strongly related to the temporal and spectral characteristics of the cardiorespiratory signals.

OBJECTIVE

To evaluate the robustness of the RSA estimates to the selection of model order, delays, changes of phase and irregular heartbeats as well as to give recommendations for their interpretation on each case.

APPROACH

Simulations were used to evaluate the model order selection when calculating the RSA estimates explained before, as well as 3 different scenarios that can occur in signals acquired in non-controlled environments and/or from patient populations: the presence of irregular heartbeats; the occurrence of delays between heart rate variability (HRV) and respiratory signals; and the changes over time of the phase between HRV and respiratory signals.

MAIN RESULTS

It was found that using a single model order for all the calculations suffices to characterize the RSA estimates correctly. In addition, the RSA estimation in signals containing more than 5 irregular heartbeats in a period of 5 minutes might be misleading. Regarding the delays between HRV and respiratory signals, both estimates are robust. For the last scenario, the two approaches tolerate phase changes up to 54°, as long as this lasts less than one fifth of the recording duration.

SIGNIFICANCE

Guidelines are given to compute the RSA estimates in non-controlled environments and patient populations.