Entropy change of biological dynamics in COPD

Abstract

In this century, the rapid development of large data storage technologies, mobile network technology, and portable medical devices makes it possible to measure, record, store, and track analysis of large amount of data in human physiological signals. Entropy is a key metric for quantifying the irregularity contained in physiological signals. In this review, we focus on how entropy changes in various physiological signals in COPD. Our review concludes that the entropy change relies on the types of physiological signals under investigation. For major physiological signals related to respiratory diseases, such as airflow, heart rate variability, and gait variability, the entropy of a patient with COPD is lower than that of a healthy person. However, in case of hormone secretion and respiratory sound, the entropy of a patient is higher than that of a healthy person. For mechanomyogram signal, the entropy increases with the increased severity of COPD. This result should give valuable guidance for the use of entropy for physiological signals measured by wearable medical device as well as for further research on entropy in COPD.

Keywords: COPD; entropy; heart rate variability; irregularity; physiological signal; respiratory pattern.

Figure 1

PRISMA flow diagram.

Figure 2

Entropy change in COPD. Notes: The error bar represents standard error. First part uses approximate entropy and sample entropy as mentioned in Goulart Cda et al. Second part uses sample entropy as mentioned in Borghi-Silva et al. Third part uses sample entropy as mentioned in Dames et al. ^Smoking subjects that presented a normal respiratory response to the spirometric exam. Fourth part uses fixed sample entropy as mentioned in Sarlabous et al. Abbreviations: HRV, heart rate variability; MMG, mechanomyogram signal; RSA-M, respiratory sinus arrhythmia maneuver.