Many physiological readings exhibit a fractal-like dynamic. Thus, reduced entropy in a physiological setting can be interpreted as reduced information processing or less engagement the component of within a control system ( Pincus, 1994). Entropy is linked to the concept of information content in a given time-series ( Mitchell, 2009). MSE has been used to analyse many bio-signals ( Gao et al., 2015) such as EEG dynamics in Alzheimer's disease ( Mizuno et al., 2010) and heart rate variability analysis for predicting hospital mortality ( Norris et al., 2008). This can be applied in a clinical setting to provide extra prognostic information ( Watanabe et al., 2015). This can then be plotted to show these cross-scale correlations ( Costa et al., 2002). This is achieved mathematically by creating several sub-time series from the main series and calculating the sample entropy of each scaled series ( Costa et al., 2002). Complexity in this context can be defined as “meaningful structural richness” which incorporate correlations over multiple scales ( Costa et al., 2002). Multiscale Entropy (MSE) is an extension of sample entropy and has been used as a tool to describe complexity in a time series ( Costa et al., 2002). Reduction in the sample entropy of a time-series has been shown to reflect the difference between normal and diseased participants, i.e., in patients with sepsis ( Ahmad et al., 2009 Gholami et al., 2012), cirrhosis ( Mani et al., 2009), and in obstructive sleep apnoea ( Al-Angari and Sahakian, 2007). For example, sample entropy is a tool to describe regularity in time series and has been well-established in the study of the cardiovascular system dynamics ( Richman and Moorman, 2000). There exist indices that describe the pattern and complexity of fluctuations in physiological time-series. The tools selected for this study will be measuring the regularity, complexity, and self-similarity of the fluctuations, which have been shown to better understand biological systems ( Richman and Moorman, 2000). This can then allow the subsequent comparison with patient sub-populations to garner insight into the disease pathophysiology. Hence, the initial need for a study examining healthy individuals in an adult population to characterize the OSV with more sophisticated methods for variability analysis. The use of pulse oximetry in these settings has helped reduce the need for invasive arterial blood gases analysis and increase the detection of hypoxaemia ( Jubran, 2015), as defined as an SpO 2 value observed as 2%, etc.), which to some extent miss out the pattern of SpO 2 fluctuations ( Dipietro et al., 1994 Cox et al., 2011 De Jesus et al., 2011 De Oliveira et al., 2012 Amoian et al., 2013 Garde et al., 2014, 2016 Hoffman, 2016). It is a method commonly used clinically whether that be in intensive care, in surgery, or in some out-patient clinics ( Jubran, 2015). Pulse oximetry is a technique used to measure oxygen saturation (SpO 2) non-invasively. This may have the potential to be used in clinical practice to detect differences in diseased patient subsets. We have showed that entropy analysis of pulse oximetry signals carries information about body oxygenation. Overall, this study has characterized OSV using pre-existing tools. These findings suggest partial “uncoupling” of the cardio-respiratory control system that occurs with aging. Additionally, the MSE analysis described a complex fluctuation pattern, which was reduced with age ( p < 0.05). There was also a significant inverse correlation between mean oxygen saturation and sample entropy in healthy individuals. The sample entropy revealed the variability to be more regular than heart rate variability and respiratory rate variability. Through DFA analysis, OSV was shown to exhibit fractal-like patterns. The “young” population consisted of 20 individuals and the “old” population consisted of 16 individuals. The study population consisted of 36 individuals. Secondly, to determine if there were any changes that occur with age. The aim of this study was to characterize the pattern of OSV using several parameters the regularity (sample entropy analysis), the self-similarity and the complexity. There are few published studies on oxygen saturation variability (OSV), with none describing the variability and its pattern in a healthy adult population. Pulse oximetry is routinely used for monitoring patients' oxygen saturation levels with little regard to the variability of this physiological variable.
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