Abstract
The interactions of heart rate variability and respiratory rate and tidal volume fluctuations provide key information about normal and abnormal sleep. A set of metrics can be computed by analysis of coupling and coherence of these signals, cardiopulmonary coupling (CPC). There are several forms of CPC, which may provide information about normal sleep physiology, and pathological sleep states ranging from insomnia to sleep apnea and hypertension. As CPC may be computed from reduced or limited signals such as the electrocardiogram or photoplethysmogram (PPG) vs. full polysomnography, wide application including in wearable and non-contact devices is possible. When computed from PPG, which may be acquired from oximetry alone, an automated apnea hypopnea index derived from CPC-oximetry can be calculated. Sleep profiling using CPC demonstrates the impact of stable and unstable sleep on insomnia (exaggerated variability), hypertension (unstable sleep as risk factor), improved glucose handling (associated with stable sleep), drug effects (benzodiazepines increase sleep stability), sleep apnea phenotypes (obstructive vs. central sleep apnea), sleep fragmentations due to psychiatric disorders (increased unstable sleep in depression).
Highlights
The prevalence of sleep disorders has been increasing over the last two decades (Acquavella et al, 2020)
One approach seeing increasing utilization both in formal medical and consumer wearable devices is through analysis of heart rate and respiration
There is a strong correlation between changes in heart rate variability and sleep during health and disease (Tobaldini et al, 2013)
Summary
The prevalence of sleep disorders has been increasing over the last two decades (Acquavella et al, 2020). Disorders like insomnia and sleep apnea have a prevalence of as much as 20% in the general population (Franklin and Lindberg, 2015; Acquavella et al, 2020). Deep NREM sleep (N3) typically has the greatest HF power. Sleep disruptive influences such as sleep apnea (Qin et al, 2021; Ucak et al, 2021), insomnia (Spiegelhalder et al, 2011; Dodds et al, 2017; Cosgrave et al, 2021), and depression (Hyunbin et al, 2017; Gao et al, 2019; Eddie et al, 2020) are associated with an increase in the LF components
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