Sleep is a complex and dynamic interplay between the brain and various physiological systems. Functions such as heart rate, respiration, and brain wave activity are regulated by intricate physiological mechanisms involving nonlinear interactions across multiple control centres operating on different time scales. It is increasingly recognized that a more accurate understanding of physiological outputs can be achieved through nonlinear analytical approaches, rather than traditional linear methods such as the standard deviation of the mean.
Among nonlinear techniques, entropy is one of the most widely used metrics for assessing the irregularity of physiological signals. For example, sample entropy is a method used to quantify regularity in time series data and has demonstrated the ability to distinguish between healthy and diseased individuals. In some cases, recordings from a simple finger pulse oximeter (measuring oxygen saturation (SpO₂)) may be sufficient to screen for sleep apnoea, potentially reducing the need for full cardiorespiratory polygraphy.
While nonlinear methods are well established in cardiovascular research, their application to respiratory signal analysis in obstructive sleep apnoea (OSA) remains limited. This analytical approach may offer deeper insights into complex physiological interactions-such as those between oxygen saturation and heart rate using relatively simple equipment.
The aim of this study is to investigate differences in entropy values between healthy individuals and patients with chronic respiratory diseases, both with and without coexisting sleep-disordered breathing.
