Multiscale cross-approximate entropy analysis as a measure of complexity among the aged and diabetic.

Complex fluctuations within physiological signals can be used to evaluate the health of the human body. This study recruited four groups of subjects: young healthy subjects (Group 1, n = 32), healthy upper middle-aged subjects (Group 2, n = 36), subjects with well-controlled type 2 diabetes (Group 3, n = 31), and subjects with poorly controlled type 2 diabetes (Group 4, n = 24). Data acquisition for each participant lasted 30 minutes. We obtained data related to consecutive time series with R-R interval (RRI) and pulse transit time (PTT). Using multiscale cross-approximate entropy (MCE), we quantified the complexity between the two series and thereby differentiated the influence of age and diabetes on the complexity of physiological signals. This study used MCE in the quantification of complexity between RRI and PTT time series. We observed changes in the influences of age and disease on the coupling effects between the heart and blood vessels in the cardiovascular system, which reduced the complexity between RRI and PTT series.