The Use of Entropy to Assess Sleep Disordered Breathing in Chronic Respiratory Disease

Overview

Research is being conducted into chronic respiratory diseases, including chronic obstructive pulmonary disease (COPD), asthma, interstitial lung disease, and bronchiectasis. The investigation specifically focuses on sleep-disordered breathing (SDB) in individuals with chronic respiratory disease. SDB encompasses a range of conditions, the most common of which is obstructive sleep apnoea. In obstructive sleep apnoea, periodic pauses in breathing (apnoea) lead to reduced blood oxygen levels. To detect these events, patients typically undergo sleep studies that involve monitoring oxygen saturation, heart rate, and respiratory patterns during sleep. When chronic respiratory disease and SDB coexist, breathing disturbances during sleep may be exacerbated. To identify SDB, sleep studies are commonly used to assess oxygen levels, heart rate, and breathing patterns. The objective of this research is to identify differences between patients with chronic respiratory diseases who have SDB and those who do not. This will be achieved by analysing sleep study data using a novel analytical approach. The aim is to determine whether this method can yield more detailed insights into the underlying pathophysiology of these conditions.

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.