Technical aspects of cardiorespiratory estimation using subspace projections and cross entropy

Abstract

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 introduced before, as well as three 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 RSA correctly. In addition, the RSA estimation in signals containing more than 5 irregular heartbeats in a period of 5 min 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.