Evidence Hub

Physiological signal entropy in patients with chronic respiratory disease: a systematic review

Nawal Alotaibi, Maggie Cheung, Amar J. Shah, John R. Hurst, Alireza Mani, Swapna Mandal

European Respiratory Review, 2025

4 citations

Abstract

Background Chronic respiratory diseases (CRDs) such as COPD and asthma have a substantial impact on patients and healthcare systems. Recent research on diagnosing and monitoring CRDs highlights the potential of continuous measurement of physiological parameters using nonlinear measures such as entropy analysis. Entropy measures the irregularity and complexity of physiological signals, reflecting the engagement of physiological control mechanisms. This systematic review examines the current evidence on changes in the entropy of physiological signals in CRDs. Methods The review follows Preferred Reporting in Systematic Reviews and Meta-Analyses (PRISMA) guidelines and includes studies from databases such as Scopus, Medline, CINAHL and Embase. Quality assessment was conducted using the Newcastle–Ottawa Scale. Evidence was qualitatively synthesised, taking into account entropy signals, entropy type and results. Results 11 studies met the inclusion criteria. Entropy in signals including heart rate variability (HRV), airflow, peripheral oxygen saturation (SpO2), inter-breath interval and tidal volume were evaluated. The findings indicated that patients with COPD and asthma exhibit lower entropy in HRV and airflow compared to healthy controls, with entropy decreasing as disease severity increases. Conversely, SpO2 entropy values were increased during an exacerbation compared to stable COPD. Conclusion The review highlights the potential of entropy analysis of physiological signals…

How strong is the evidence?

Evidence Hub reads this as a systematic review, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.

How the five levels of evidence work. The level is assigned automatically from the abstract and can be wrong, so check the paper.

Read the paper

Related studies

Go deeper in Evidence Hub

Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.

Open this study Sign up free