Influence of Chronic Obstructive Pulmonary Disease and Moderate-To-Severe Sleep Apnoea in Overnight Cardiac Autonomic Modulation: Time, Frequency and Non-Linear Analyses

Abstract

Chronic obstructive pulmonary disease (COPD) is one of the most prevalent lung diseases worldwide. COPD patients show major dysfunction in cardiac autonomic modulation due to sustained hypoxaemia, which has been significantly related to higher risk of cardiovascular disease. Obstructive sleep apnoea syndrome (OSAS) is a frequent comorbidity in COPD patients. It has been found that patients suffering from both COPD and OSAS simultaneously, the so-called overlap syndrome, have notably higher morbidity and mortality. Heart rate variability (HRV) has demonstrated to be useful to assess changes in autonomic functioning in different clinical conditions. However, there is still little scientific evidence on the magnitude of changes in cardiovascular dynamics elicited by the combined effect of both respiratory diseases, particularly during sleep, when apnoeic events occur. In this regard, we hypothesised that a non-linear analysis is able to provide further insight into long-term dynamics of overnight cardiovascular modulation. Accordingly, this study is aimed at assessing the usefulness of sample entropy (SampEn) to distinguish changes in overnight pulse rate variability (PRV) recordings among three patient groups while sleeping: COPD, moderate-to-severe OSAS, and overlap syndrome. In order to achieve this goal, a population composed of 297 patients were studied: 22 with COPD alone, 213 showing moderate-to-severe OSAS, and 62 with COPD and moderate-to-severe OSAS simultaneously (COPD+OSAS). Cardiovascular dynamics were analysed using pulse rate (PR) recordings from unattended pulse oximetry carried out at patients’ home. Conventional time- and frequency- domain analyses were performed to characterise sympathetic and parasympathetic activation of the nervous system, while SampEn was applied to quantify long-term changes in irregularity. Our analyses revealed that overnight PRV recordings from COPD+OSAS patients were significantly more irregular (higher SampEn) than those from patients with COPD alone (0.267 [0.210–0.407] vs. 0.212 [0.151–0.267]; p < 0.05) due to recurrent apnoeic events during the night. Similarly, COPD + OSAS patients also showed significantly higher irregularity in PRV during the night than subjects with OSAS alone (0.267 [0.210–0.407] vs. 0.241 [0.189–0.325]; p = 0.05), which suggests that the cumulative effect of both diseases increases disorganization of pulse rate while sleeping. On the other hand, no statistical significant differences were found between COPD and COPD + OSAS patients when traditional frequency bands (LF and HF) were analysed. We conclude that SampEn is able to properly quantify changes in overnight cardiovascular dynamics of patients with overlap syndrome, which could be useful to assess cardiovascular impairment in COPD patients due to the presence of concomitant OSAS.

Assessment of Nocturnal Autonomic Cardiac Imbalance in Positional Obstructive Sleep Apnea. A Multiscale Nonlinear Approach

“…As increased disorderliness of the HRV has been suggested as an independent risk factor for mortality [ 55 ], our findings are in line with the study by Kulkas et al, suggesting higher cardiac dysfunction in POSA patients. Similarly, higher entropy measures of HRV time series have been linked to increased diseased states, such as sick sinus syndrome (patient vs. healthy) [ 54 ], sleep apnea (OSA positive vs. OSA negative) [ 28 ], cardiac abnormalities (atrial fibrillation vs. healthy) [ 36 ], and overlap syndrome (COPD + OSA vs. COPD) [ 29 ]. In the same regard, Kabbach et al recently found that COPD patients showed significantly higher variability during acute exacerbation than stable COPD patients [ 56 ].…”

“…Particularly, PRV samples reflecting intervals <0.33 s, >1.50 s or differing more than 0.66 s with the previous pulse-to-pulse period were removed. Finally, all 5 min segments with a >1% rate of artefacts were excluded from subsequent analyses [ 26 , 29 ].…”

“…The signal power in the conventional frequency bands of the HRV spectrum has been predominantly used due to its close relation with sympathetic and parasympathetic modulation of the heart rate [ 21 , 24 , 25 ]. Nevertheless, nonlinear analysis has been found to outperform common frequency domain indices in the context of OSA [ 26 , 27 , 28 , 29 ]. Particularly, entropy measures such as sample entropy (SampEn) and multiscale entropy (MSE), which quantify disorderliness or irregularity and complexity of a time series, recently demonstrated substantial superiority over spectral measures to assess changes in HRV of OSA patients [ 28 , 29 ].…”

“…Nevertheless, nonlinear analysis has been found to outperform common frequency domain indices in the context of OSA [ 26 , 27 , 28 , 29 ]. Particularly, entropy measures such as sample entropy (SampEn) and multiscale entropy (MSE), which quantify disorderliness or irregularity and complexity of a time series, recently demonstrated substantial superiority over spectral measures to assess changes in HRV of OSA patients [ 28 , 29 ]. Therefore, both SampEn and MSE are reliable tools, able to properly show potential differences in HRV dynamics between positional and non-positional patients.…”

Obstructive Sleep Apnea Recognition Based on Multi-Bands Spectral Entropy Analysis of Short-Time Heart Rate Variability

“…However, both the analysis methods of time domain and frequency domain are linear analysis of HRV signals, where it is difficult to reflect the intrinsic nonlinear characteristics of the HRV signal [ 13 , 14 ]. Therefore, the nonlinear analysis methods of HRV for OSA detection have been widely studied.…”