Physiological signals often show complex fluctuation (CF) under the dual influence of temporal and spatial scales, and CF can be used to assess the health of physiologic systems in the human body. This study applied multiscale cross-approximate entropy (MC-ApEn) to quantify the complex fluctuation between R-R intervals series and photoplethysmography amplitude series. All subjects were then divided into the following two groups: healthy upper middle-aged subjects (Group 1, age range: 41–80 years, n = 27) and upper middle-aged subjects with type 2 diabetes (Group 2, age range: 41–80 years, n = 24). There are significant differences of heart rate variability, LHR, between Groups 1 and 2 (1.94 ± 1.21 versus 1.32 ± 1.00, P = 0.031). Results demonstrated differences in sum of large scale MC-ApEn (MC-ApEnLS) (5.32 ± 0.50 versus 4.74 ± 0.78, P = 0.003). This parameter has a good agreement with pulse-pulse interval and pulse amplitude ratio (PAR), a simplified assessment for baroreflex activity. In conclusion, this study employed the MC-ApEn method, integrating multiple temporal and spatial scales, to quantify the complex interaction between the two physical signals. The MC-ApEnLS parameter could accurately reflect disease process in diabetics and might be another way for assessing the autonomic nerve function.

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We applied multiscale entropy (MSE) to assess variation in crest time (CT), a parameter in arterial waveform analysis, in diagnosing patients with diabetes. Data on digital volume pulse were obtained from 93 individuals in three groups [Healthy young (Group 1, 20 < age ≤ 40, n = 30), healthy upper-middle-aged (Group 2, age > 40, n = 30), and diabetic (Group 3, n = 33) subjects]. Crest time, normalized crest time, crest time ratio (CTR), small- and large-scale MSE on CT [MSESS(CT) and MSELS(CT), respectively] were computed and correlated with anthropometric (i.e., body weight/height, waist circumference), hemodynamic (i.e., blood pressure), and biochemical parameters (i.e., serum triglyceride, high-density lipoprotein, fasting blood sugar, and glycosylated hemoglobin). The results demonstrated higher variability in CT in healthy subjects (Groups 1 and 2) compared with that in diabetic patients (Group 3) as reflected in significantly elevated MSESS(CT) and MSELS(CT) in the former (p < 0.003 and p < 0.001, respectively). MSELS(CT) also showed significant association with waist circumference and fasting blood sugar (i.e., two diagnostic criteria of metabolic syndrome) as well as glycosylated hemoglobin concentration. In conclusion, using MSE analysis for assessing CT variation successfully distinguished diabetic patients from healthy subjects. MSESS(CT) and MSELS(CT) therefore may serve as noninvasive tools for identifying subjects with diabetes and those at risk.

Despite the importance of translational medicine in cardiovascular research, rat vascular endothelial function and sympathetic activity assessment remains challenging.This study consisted of two parts: part A examined the consistency of reactive hyperemia-elicited dilatation index(DI) before (DI Original ) and after (DI EEMD ) ensemble empirical mode decomposition (EEMD) using Wistar-Kyoto (WKY)rats (n = 7). Part B compared the endothelial function and heart rate variability of normal WKY rats (group 1, n = 9)with those of spontaneously hypertensive rats (group 2,n = 9) at two time points of development. The SD1/SD2 ratio(SSR) was obtained from Poincaré plot analysis to evaluate sympathetic activities. Using a Bland-Altman plot, part A of the study demonstrated a lower agreement of DI Original than that of DI EEMD . In part B, despite no significant difference in DI EEMD between the two groups at age 10 weeks, DI EEMD was higher in group 1 (2.25 } 0.63 vs. 1.43 } 0.41 for groups 1 and 2, respectively, p = 0.004) at 20 weeks. Although no notable change in SSR existed in group 1 between the twotime points, a significant difference existed in group 2(p < 0.001). In conclusion, using algorithms of Poincaré plot analysis and EEMD, the impact of age and hypertension on rat vascular endothelial function and heart rate variability can be reliably assessed.

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Cardiac autonomic dysfunction assessed by power spectral analysis of electrocardigographic (ECG) R-R intervals (RRI) is a useful method in clinical research. The compatibility of pulse-pulse intervals (PPI) acquired by photoplethysmography (PPG) with RRI is equivocal. In this study, we would like to investigate factors influence the compatibility. We recruited 25 young and health subjects divided into two groups: normal subjects (Group1, BMI < 24, n=15) and overweight subjects (Group2, BMI >/= 24, n=10). ECG and PPG were measured for 5 minutes. Used cross-approximate entropy (CAE) and Fast Fourier transform (FFT) to obtained compatibility between RRI and PPI. The CAE value in Group1 were significantly lower than in Group2 (1.71 ± 0.12 vs. 1.83 ± 0.11, P = 0.011). A positive linear relationship between CAE value and risk factors of metabolic syndrome. No significantly difference between LFP/HFP ratio of RRI (LHRRRI) and LFP/HFP ratio of PPI (LHRPPI) in Group1 (1.42 ± 0.19 vs. 1.38 ± 0.17, P = 0.064), LHRRRI significantly higher than LHRPPI in Group2 (2.18 ± 0.37 vs. 1.93 ± 0.30, P = 0.005). It should be careful that using PPI to assess autonomic function in the obese subjects or the patients with metabolic syndrome.