Abstract
Characterizing respiratory rate variability (RRV) in humans during sleep is challenging, since it requires the analysis of respiratory signals over a period of several hours. These signals are easily distorted by movement and volitional inputs. We applied the method of spectral analysis to the nasal pressure transducer signal in 38 adults with no obstructive sleep apnea, defined by an apnea‐hypopnea index <5, who underwent all‐night polysomnography (PSG). Our aim was to detect and quantitate RRV during the various sleep stages, including wakefulness. The nasal pressure transducer signal was acquired at 100 Hz and consecutive frequency spectra were generated for the length of the PSG with the Fast Fourier Transform. For each spectrum, we computed the amplitude ratio of the first harmonic peak to the zero frequency peak (H1/DC), and defined as RRV as (100 − H1/DC) %. RRV was greater during wakefulness compared to any sleep stage, including rapid‐eye‐movement. Furthermore, RRV correlated with the depth of sleep, being lowest during N3. Patients spent most their sleep time supine, but we found no correlation between RRV and body position. There was a correlation between respiratory rate and sleep stage, being greater in wakefulness than in any sleep stage. We conclude that RRV varies according to sleep stage. Moreover, spectral analysis of nasal pressure signal appears to provide a valid measure of RRV during sleep. It remains to be seen if the method can differentiate normal from pathological sleep patterns.
Highlights
Heart rate variability (HRV) (Tobaldini et al 2013) has been used to describe sleep-related cardiovascular autonomic alterations
The approach of spectral analysis in sleep has been applied to the EEG for sleep staging (Campbell 2009) and to ECG signals in describing HRV (Chouchou and Desseilles 2014)
We explored the feasibility of applying spectral analysis to airway flow data gathered during all-night polysomnography (PSG) in spontaneously breathing individuals
Summary
Heart rate variability (HRV) (Tobaldini et al 2013) has been used to describe sleep-related cardiovascular autonomic alterations. The physiological consequences of respiratory rate variability (RRV) during sleep are poorly understood (Tobin et al 1983; Newton et al 2014). Heart rate variability is readily ascertained from analysis of the ECG, a periodic signal of distinctive morphology. On the other hand, defining RRV in sleep is challenging since it requires the accurate and continuous measurement of airway flow or pressure over many hours. These signals are distorted by volitional inputs, movement and other types of physical interference. The approach of spectral analysis in sleep has been applied to the EEG for sleep staging (Campbell 2009) and to ECG signals in describing HRV (Chouchou and Desseilles 2014)
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