Application of a modified entropy computational method in assessing the complexity of pulse wave velocity signals in healthy and diabetic subjects

Yi Chung Chang, Hsien Tsai Wu, Hong Ruei Chen, An Bang Liu, Jung Jen Yeh, Men Tzung Lo, Jen Ho Tsao, Chieh Ju Tang, I. Ting Tsai, Cheuk Kwan Sun

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

Using 1000 successive points of a pulse wave velocity (PWV) series, we previously distinguished healthy from diabetic subjects with multi-scale entropy (MSE) using a scale factor of 10. One major limitation is the long time for data acquisition (i.e., 20 min). This study aimed at validating the sensitivity of a novel method, short time MSE (sMSE) that utilized a substantially smaller sample size (i.e., 600 consecutive points), in differentiating the complexity of PWV signals both in simulation and in human subjects that were divided into four groups: healthy young (Group 1; n = 24) and middle-aged (Group 2; n = 30) subjects without known cardiovascular disease and middle-aged individuals with well-controlled (Group 3; n = 18) and poorly-controlled (Group 4; n = 22) diabetes mellitus type 2. The results demonstrated that although conventional MSE could differentiate the subjects using 1000 consecutive PWV series points, sensitivity was lost using only 600 points. Simulation study revealed consistent results. By contrast, the novel sMSE method produced significant differences in entropy in both simulation and testing subjects. In conclusion, this study demonstrated that using a novel sMSE approach for PWV analysis, the time for data acquisition can be substantially reduced to that required for 600 cardiac cycles (~10 min) with remarkable preservation of sensitivity in differentiating among healthy, aged, and diabetic populations.

Original languageEnglish
Pages (from-to)4032-4043
Number of pages12
JournalEntropy
Volume16
Issue number7
DOIs
StatePublished - 2014

Keywords

  • Age
  • Diabetes
  • Multi-scale entropy
  • Pulse wave velocity
  • Scale factor

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