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Combination of R-R Interval and Crest Time in Assessing Complexity Using Multiscale Cross-Approximate Entropy in Normal and Diabetic Subjects

1
School of Electrical and Information Engineering, North Minzu University, No. 204 North—Wenchang St., Xixia District, Yinchuan 750021, China
2
School of Computer and Information, Hefei University of Technology, No. 193, Tunxi Rd., Hefei 230009, China
3
Department of Electrical Engineering, National Dong Hwa University, No. 1, Sec. 2, Da Hsueh Rd., Shoufeng, Hualien 97401, Taiwan
4
Department of Emergency Medicine, E-Da Hospital, I-Shou University School of Medicine for International Students, No.1, Yida Road, Jiaosu Village, Yanchao District, Kaohsiung City 82445, Taiwan
*
Author to whom correspondence should be addressed.
Signifies equal contribution compared with the corresponding author.
Entropy 2018, 20(7), 497; https://doi.org/10.3390/e20070497
Received: 15 April 2018 / Revised: 16 June 2018 / Accepted: 22 June 2018 / Published: 27 June 2018
(This article belongs to the Special Issue The 20th Anniversary of Entropy - Approximate and Sample Entropy)
The present study aimed at testing the hypothesis that application of multiscale cross-approximate entropy (MCAE) analysis in the study of nonlinear coupling behavior of two synchronized time series of different natures [i.e., R-R interval (RRI) and crest time (CT, the time interval from foot to peakof a pulse wave)] could yield information on complexity related to diabetes-associated vascular changes. Signals of a single waveform parameter (i.e., CT) from photoplethysmography and RRI from electrocardiogram were simultaneously acquired within a period of one thousand cardiac cycles for the computation of different multiscale entropy indices from healthy young adults (n = 22) (Group 1), upper-middle aged non-diabetic subjects (n = 34) (Group 2) and diabetic patients (n = 34) (Group 3). The demographic (i.e., age), anthropometric (i.e., body height, body weight, waist circumference, body-mass index), hemodynamic (i.e., systolic and diastolic blood pressures), and serum biochemical (i.e., high- and low-density lipoprotein cholesterol, total cholesterol, and triglyceride) parameters were compared with different multiscale entropy indices including small- and large-scale multiscale entropy indices for CT and RRI [MEISS(CT), MEILS(CT), MEISS(RRI), MEILS(RRI), respectively] as well as small- and large-scale multiscale cross-approximate entropy indices [MCEISS, MCEILS, respectively]. The results demonstrated that both MEILS(RRI) and MCEILS significantly differentiated between Group 2 and Group 3 (all p < 0.017). Multivariate linear regression analysis showed significant associations of MEILS(RRI) and MCEILS(RRI,CT) with age and glycated hemoglobin level (all p < 0.017). The findings highlight the successful application of a novel multiscale cross-approximate entropy index in non-invasively identifying diabetes-associated subtle changes in vascular functional integrity, which is of clinical importance in preventive medicine. View Full-Text
Keywords: multiscale entropy (MSE); cross-approximate entropy; crest time; R-R interval; diabetes multiscale entropy (MSE); cross-approximate entropy; crest time; R-R interval; diabetes
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Xiao, M.-X.; Wei, H.-C.; Xu, Y.-J.; Wu, H.-T.; Sun, C.-K. Combination of R-R Interval and Crest Time in Assessing Complexity Using Multiscale Cross-Approximate Entropy in Normal and Diabetic Subjects. Entropy 2018, 20, 497.

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