Abstract
What is the central question of this study? What is the physiological interpretation of fluctuations observed during normobaric hypoxia in healthy individuals? What is the main finding and its importance? There is a significant flow of information between and other cardio-respiratory time series during graded hypoxia. Analysis of the pattern of variations has potential for non-invasive assessment of the engagement of respiratory control system in health and disease. Peripheral capillary oxygen saturation ( ) exhibits a complex pattern of fluctuations during hypoxia. The physiological interpretation of variability is not well understood. In this study, we tested the hypothesis that fluctuation carries information about integrated cardio-respiratory control in healthy individuals using a network physiology approach. We explored the use of transfer entropy in order to compute the flow of information between cardio-respiratory signals during hypoxia. Twelve healthy males (mean (SD) age 22 (4) years) were exposed to four simulated environments (fraction of inspired oxygen ( ): 0.12, 0.145, 0.17, and 0.2093) for 45min, in a single blind randomized controlled design. The flow of information between different physiological parameters ( , respiratory frequency, tidal volume, minute ventilation, heart rate, end-tidal pressure of O2 and CO2 ) were analysed using transfer entropy. Normobaric hypoxia was associated with a significant increase in entropy of the time series. The transfer entropy analysis showed that, particularly at 0.145 and 0.12, the flow of information between and other physiological variables exhibits a bidirectional relationship. While reciprocal interactions were observed between different cardio-respiratory parameters during hypoxia, remained the main hub of this network. fluctuations during graded hypoxia exposure carry information about cardio-respiratory control. Therefore, entropy analysis has the potential for non-invasive assessment of the functional connectivity of respiratory control system in various healthcare settings.
Highlights
Peripheral capillary oxygen saturation (SpO2 ) is measured noninvasively and is extensively used for monitoring patients in clinical settings
The absolute value of SpO2 is currently used by clinicians, SpO2 time series exhibit a complex pattern of fluctuations which may carry useful information (Bhogal & Mani, 2017)
By applying entropy analysis to SpO2 time series data, we have previously demonstrated that SpO2 entropy, and not its absolute value, can distinguish older healthy individuals from their younger counterparts (Bhogal & Mani, 2017)
Summary
Peripheral capillary oxygen saturation (SpO2 ) is measured noninvasively and is extensively used for monitoring patients in clinical settings. The absolute value of SpO2 is currently used by clinicians, SpO2 time series exhibit a complex pattern of fluctuations which may carry useful information (Bhogal & Mani, 2017). The complexity of physiological time series data can be measured by computing the degree of irregularity of the signal (i.e. entropy) (Pincus, 1991). By applying entropy analysis to SpO2 time series data, we have previously demonstrated that SpO2 entropy, and not its absolute value (or mean), can distinguish older healthy individuals from their younger counterparts (Bhogal & Mani, 2017). We have demonstrated that as the concentration of inspired oxygen (FIO2 ) is decreased, SpO2 entropy increases, and there is a strong significant negative correlation between mean SpO2 and its entropy during normobaric hypoxic exposure (Costello et al, 2020). SpO2 entropy, but not mean SpO2 , was correlated with perception of breathlessness in otherwise healthy individuals when hypoxic (Costello et al, 2020)
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