A Robust Estimation of the Cardiorespiratory Coupling in the Presence of Abnormal Beats

Abstract

Respiratory sinus arrhythmia (RSA) is one of the forms of cardiorespiratory coupling. It has been suggested as a potential biomarker for diverse illnesses and conditions. In general, methods for non-invasive quantification of the RSA combine information from heart rate variability (HRV) and respiratory signals. Abnormal beats, which commonly occur in different populations, alter the reliability of the HRV and thus hinder the quantification of the RSA. To overcome this problem, several methods for detection and correction of irregular beats have been reported in literature. However, the effect of each of these methods on the quantification of the RSA is not well understood yet. For this reason, an approach that avoids this step might be useful. This paper presents an alternative based on robust regression models. For comparison purposes, an algorithm to detect and correct for irregular beats, in combination with a state-of-the-art RSA estimate, are tested. A similar performance is achieved with both approaches. These results show that the proposed robust methodology is able to capture the strength of the RSA, even when irregular beats are present, avoiding the irregularities correction step.