The Different Facets of Heart Rate Variability in Obstructive Sleep Apnea

Abstract

Obstructive sleep apnea (OSA), a heterogeneous and multifactorial sleep related breathing disorder with high prevalence, is a recognized risk factor for cardiovascular morbidity and mortality. Autonomic dysfunction leads to adverse cardiovascular outcomes in diverse pathways. Heart rate is a complex physiological process involving neurovisceral networks and relative regulatory mechanisms such as thermoregulation, renin-angiotensin-aldosterone mechanisms, and metabolic mechanisms. Heart rate variability (HRV) is considered as a reliable and non-invasive measure of autonomic modulation response and adaptation to endogenous and exogenous stimuli. HRV measures may add a new dimension to help understand the interplay between cardiac and nervous system involvement in OSA. The aim of this review is to introduce the various applications of HRV in different aspects of OSA to examine the impaired neuro-cardiac modulation. More specifically, the topics covered include: HRV time windows, sleep staging, arousal, sleepiness, hypoxia, mental illness, and mortality and morbidity. All of these aspects show pathways in the clinical implementation of HRV to screen, diagnose, classify, and predict patients as a reasonable and more convenient alternative to current measures.

Keywords: autonomic dysfunction; central autonomic networks; frequency-domain analysis; heart rate variability; non-linear analysis; obstructive sleep apnea; time-domain analysis; time-window analysis.

Figure 1

Depicts an example of the changes in beat-to-beat intervals (BBI) in an obstructive sleep apnea (OSA) subject with (upper) and without (middle) the presence of apneic events and a healthy subject (bottom) during stage 3 sleep in the supine position.

Figure 2

Shows an exemplary illustration of the respiratory power index (RPI) and electrocardiograph-derived respiration (EDR) methods in an OSA patient. Overnight electrocardiograph recordings are processed and cut into limited time segments. EDR signals are calculated via ECG respiration embeddings such as QRS complex (A) or respiratory sinus arrhythmia (RSA) (C). Spectrograms of both embeddings are also generated (B,D). These spectrograms are normalized and averaged to amplify the respiration-based component and mask non-respiration-related power (E). The power is calculated at each step with two selection events (F). A respiratory flow shows corresponding events to the power spectrum (G). The number of detected apneic events is the RPI.