Detrended fluctuation analysis of day and night breathing parameters from a wearable respiratory holter

Abstract

Background and objective

This study focuses on the application of Detrended Fluctuation Analysis (DFA) to understand the variability and correlation properties of respiratory parameters time series obtained by means of a wearable.

Methods

Data from 18 healthy volunteers collected using the Airgo™ band, which provides signals proportional to thoracic circumference at a sampling frequency of 10 Hz. The primary aim was to provide preliminary normative data for DFA scaling factors.

Results

DFA was applied to 6-h recordings, revealing significant differences (p < 0.001) in scaling factors (α values) for tidal volume (night: 0.97 [0.09], day: 0.88 [0.04]), minute ventilation (night: 1.02 [0.10], day: 0.91 [0.07), mean inspiratory flow (night: 0.98 [0.06], day: 0.88 [0.06]), mean expiratory flow (night: 0.89 [0.08], day: 0.81 [0.06]), and duty cycle (night: 0.64 [0.04], day: 0.59 [0.03]). Quadratic detrending highlighted additional differences not captured with linear detrending, particularly in inspiratory and expiratory time. These findings suggest distinct regulatory patterns during sleep.

Conclusions

DFA analysis of respiratory parameters obtained from wearable devices reveals distinct regulatory patterns between day and night conditions, particularly in parameters related to tidal volume and ventilation. These findings demonstrate the potential of DFA to uncover physiological differences in respiratory control mechanisms, especially during sleep, despite technical limitations such as the strong dependency of DFA scaling factors on sampling frequency, duration, and detrending order. Future research should address the limitations of sample size and expand normative datasets to include individuals with respiratory conditions, to translate this methodology into specific clinical applications.