Multiscale entropy analysis of biological signals

TLDR

Traditional methods for measuring biological signal complexity ignore the multiple time scales present in such time series. The study aims to describe and extend the multiscale entropy method, demonstrating its applicability to human heartbeat fluctuations in health and disease. The authors detail the basis and implementation of the multiscale entropy (MSE) method. The method reveals a loss of complexity with aging, atrial fibrillation, and congestive heart failure, distinct MSE profiles that suggest diagnostic potential, supports a general complexity‑loss theory, and shows higher multiscale entropy in noncoding DNA compared to coding sequences.

Abstract

Traditional approaches to measuring the complexity of biological signals fail to account for the multiple time scales inherent in such time series. These algorithms have yielded contradictory findings when applied to real-world datasets obtained in health and disease states. We describe in detail the basis and implementation of the multiscale entropy (MSE) method. We extend and elaborate previous findings showing its applicability to the fluctuations of the human heartbeat under physiologic and pathologic conditions. The method consistently indicates a loss of complexity with aging, with an erratic cardiac arrhythmia (atrial fibrillation), and with a life-threatening syndrome (congestive heart failure). Further, these different conditions have distinct MSE curve profiles, suggesting diagnostic uses. The results support a general ``complexity-loss'' theory of aging and disease. We also apply the method to the analysis of coding and noncoding DNA sequences and find that the latter have higher multiscale entropy, consistent with the emerging view that so-called ``junk DNA'' sequences contain important biological information.