Correlation between heart rate variability and polysomnography-derived scores of obstructive sleep apnea

Rafael Rodrigues Dos Santos; Thais Marques da Silva; Luiz Eduardo Virgilio Silva; Alan Luiz Eckeli; Helio Cesar Salgado; Rubens Fazan Jr

doi:10.3389/fnetp.2022.958550

Correlation between heart rate variability and polysomnography-derived scores of obstructive sleep apnea

Front Netw Physiol. 2022 Sep 6:2:958550. doi: 10.3389/fnetp.2022.958550. eCollection 2022.

Authors

Rafael Rodrigues Dos Santos¹, Thais Marques da Silva¹, Luiz Eduardo Virgilio Silva¹, Alan Luiz Eckeli², Helio Cesar Salgado¹, Rubens Fazan Jr¹

Affiliations

¹ Department of Physiology, School of Medicine of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Brazil.
² Department of Neuroscience and Sciences of Behavior, Division of Neurology, School of Medicine of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Brazil.

Abstract

Obstructive sleep apnea (OSA) is one of the most common sleep disorders and affects nearly a billion people worldwide. Furthermore, it is estimated that many patients with OSA are underdiagnosed, which contributes to the development of comorbidities, such as cardiac autonomic imbalance, leading to high cardiac risk. Heart rate variability (HRV) is a non-invasive, widely used approach to evaluating neural control of the heart. This study evaluates the relationship between HRV indices and the presence and severity of OSA. We hypothesize that HRV, especially the nonlinear methods, can serve as an easy-to-collect marker for OSA early risk stratification. Polysomnography (PSG) exams of 157 patients were classified into four groups: OSA-free (N = 26), OSA-mild (N = 39), OSA-moderate (N = 37), and OSA-severe (N = 55). The electrocardiogram was extracted from the PSG recordings, and a 15-min beat-by-beat series of RR intervals were generated every hour during the first 6 h of sleep. Linear and nonlinear HRV approaches were employed to calculate 32 indices of HRV. Specifically, time- and frequency-domain, symbolic analysis, entropy measures, heart rate fragmentation, acceleration and deceleration capacities, asymmetry measures, and fractal analysis. Results with indices of sympathovagal balance provided support to reinforce previous knowledge that patients with OSA have sympathetic overactivity. Nonlinear indices showed that HRV dynamics of patients with OSA display a loss of physiologic complexity that could contribute to their higher risk of development of cardiovascular disease. Moreover, many HRV indices were found to be linked with clinical scores of PSG. Therefore, a complete set of HRV indices, especially the ones obtained by the nonlinear approaches, can bring valuable information about the presence and severity of OSA, suggesting that HRV can be helpful for in a quick diagnosis of OSA, and supporting early interventions that could potentially reduce the development of comorbidities.

Keywords: heart rate variability; obstructive sleep apnea; polysomnography; sleep; sympathetic.