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Entropy 2014, 16(7), 4032-4043; doi:10.3390/e16074032

Application of a Modified Entropy Computational Method in Assessing the Complexity of Pulse Wave Velocity Signals in Healthy and Diabetic Subjects

1 Graduate Institute of Communication Engineering, National Taiwan University, Taipei 10617, Taiwan 2 Department of Electrical Engineering, National Dong Hwa University, Hualien 97401, Taiwan 3 Department of Neurology, Buddhist Tzu Chi General Hospital and Buddhist Tzu Chi University, Hualien 97002, Taiwan 4 Research Center for Adaptive Data Analysis & Center for Dynamical Biomarkers and Translational Medicine, National Central University, Chungli 32001, Taiwan 5 Department of Internal Medicine, Hualien Hospital, Ministry of Health and Welfare, Hualien 97061, Taiwan 6 Department of Emergency Medicine, E-Da Hospital, I-Shou University, Kaohsiung 82445, Taiwan These authors contributed equally to this paper.
* Author to whom correspondence should be addressed.
Received: 19 May 2014 / Revised: 2 July 2014 / Accepted: 8 July 2014 / Published: 17 July 2014
(This article belongs to the Special Issue Entropy and Cardiac Physics)
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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.
Keywords: multi-scale entropy; scale factor; pulse wave velocity; age; diabetes multi-scale entropy; scale factor; pulse wave velocity; age; diabetes
This is an open access article distributed under the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Chang, Y.-C.; Wu, H.-T.; Chen, H.-R.; Liu, A.-B.; Yeh, J.-J.; Lo, M.-T.; Tsao, J.-H.; Tang, C.-J.; Tsai, I.-T.; Sun, C.-K. Application of a Modified Entropy Computational Method in Assessing the Complexity of Pulse Wave Velocity Signals in Healthy and Diabetic Subjects. Entropy 2014, 16, 4032-4043.

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