Xiao Ming-Xia, Wei Hai-Cheng, Xu Ya-Jie, Wu Hsien-Tsai, Sun Cheuk-Kwan
School of Electrical and Information Engineering, North Minzu University, No. 204 North-Wenchang St., Xixia District, Yinchuan 750021, China.
School of Computer and Information, Hefei University of Technology, No. 193, Tunxi Rd., Hefei 230009, China.
Entropy (Basel). 2018 Jun 27;20(7):497. doi: 10.3390/e20070497.
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(CT), MEI(CT), MEI(RRI), MEI(RRI), respectively] as well as small- and large-scale multiscale cross-approximate entropy indices [MCEI, MCEI, respectively]. The results demonstrated that both MEI(RRI) and MCEI significantly differentiated between Group 2 and Group 3 (all < 0.017). Multivariate linear regression analysis showed significant associations of MEI(RRI) and MCEI(RRI,CT) with age and glycated hemoglobin level (all < 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.
在研究两个不同性质的同步时间序列[即R-R间期（RRI）和波峰时间（CT，脉搏波从起始点到峰值的时间间隔）]的非线性耦合行为时，应用多尺度交叉近似熵（MCAE）分析能够获取与糖尿病相关血管变化有关的复杂性信息。在一千个心动周期内，同时采集来自光电容积脉搏波描记法的单个波形参数（即CT）信号和来自心电图的RRI信号，以计算健康青年成年人（n = 22）（第1组）、中老年非糖尿病受试者（n = 34）（第2组）和糖尿病患者（n = 34）（第3组）的不同多尺度熵指数。将人口统计学参数（即年龄）、人体测量学参数（即身高、体重、腰围、体重指数）、血液动力学参数（即收缩压和舒张压）以及血清生化参数（即高密度和低密度脂蛋白胆固醇、总胆固醇和甘油三酯）与不同的多尺度熵指数进行比较，这些指数包括CT和RRI的小尺度和大尺度多尺度熵指数[分别为MEI(CT)、MEI(CT)、MEI(RRI)、MEI(RRI)]以及小尺度和大尺度多尺度交叉近似熵指数[分别为MCEI、MCEI]。结果表明，MEI(RRI)和MCEI在第2组和第3组之间均有显著差异（均P < 0.017）。多变量线性回归分析显示，MEI(RRI)和MCEI(RRI,CT)与年龄和糖化血红蛋白水平均有显著相关性（均P < 0.017）。这些发现突出了一种新型多尺度交叉近似熵指数在无创识别糖尿病相关血管功能完整性细微变化方面的成功应用，这在预防医学中具有临床重要性。
