Influence of the Heart Rate Variability Representations on the Quantification of the Cardiorespiratory Interactions

Abstract

The Heart Rate Variability (HRV) is a noninvasive tool to evaluate the activity of the autonomic nervous system. To study the HRV, different mathematical representations can be used. The selection of a representation might have an effect on the evaluation of the mechanisms that modulate the Heart Rate (HR). One of these mechanisms is the Respiratory Sinus Arrhythmia (RSA), i.e. an increased HR during inhalation and a decreased HR during exhalation. Different methods exist to quantify the RSA. A common approach is to calculate the power in the High Frequency (HF, 0.15-0.4 Hz) band of the spectrum of the HRV representation. More recently proposed methods use the respiratory signals to estimate the strength of the RSA. This paper studies the effect of the HRV representations on the quantification of the RSA. To this end, an experiment is used in which the sympathetic and parasympathetic branches of the autonomic nervous system are selectively blocked. Three different HRV representations are considered. Afterwards, the strength of the RSA is estimated using three approaches, namely the spectral content in the HF band of the HRV representations, orthogonal subspace projections and a time-frequency representation. The results suggest that the selection of an HRV representation does not have a significant impact on the RSA estimates in a healthy population.

Model-Based Evaluation of Methods for Respiratory Sinus Arrhythmia Estimation

“…In contrast, the work reported in [50] and the results presented in section III-B indicate that P HF is an overestimation of the cardiorespiratory coupling. According to the results in the simulation, this might indicate that the weight of modulations related to the respiration were stronger in [17] and [51], with a β R > 0.5. Another possible explanation is shown in Figure 11, which shows an example of the RSA estimation in a 5-minutes epoch taken from the sleep dataset.…”

“…At this point, two previous works can be discussed taking into account the results presented in III-B. In [17] and [51], it was shown that P HF underestimates the percentage of power explained by the respiration in the HRV. In both cases, the data was recorded in a controlled environment.…”