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

Ming-Xia Xiao; Hai-Cheng Wei; Ya-Jie Xu; Hsien-Tsai Wu; Cheuk-Kwan Sun

doi:10.3390/e20070497

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

Entropy (Basel). 2018 Jun 27;20(7):497. doi: 10.3390/e20070497.

Authors

Ming-Xia Xiao^{1

2}, Hai-Cheng Wei¹, Ya-Jie Xu¹, Hsien-Tsai Wu³, Cheuk-Kwan Sun⁴

Affiliations

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.

Abstract

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 [MEI_SS(CT), MEI_LS(CT), MEI_SS(RRI), MEI_LS(RRI), respectively] as well as small- and large-scale multiscale cross-approximate entropy indices [MCEI_SS, MCEI_LS, respectively]. The results demonstrated that both MEI_LS(RRI) and MCEI_LS significantly differentiated between Group 2 and Group 3 (all p < 0.017). Multivariate linear regression analysis showed significant associations of MEI_LS(RRI) and MCEI_LS(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.

Keywords: R-R interval; crest time; cross-approximate entropy; diabetes; multiscale entropy (MSE).

Grants and funding

61461001/National Natural Science Foundation of China