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.
Highlights
Atherosclerosis, which is the major pathological change underlying most cardiovascular diseases, has been reported to be associated with advanced age, history of stroke, diabetes, hypertension, and cerebrovascular disease
To refine the assessment approach, the present study proposes a novel means of computation, “short time multiscale entropy”, in an attempt to reduce the time for data acquisition through refined computation of the acquired data
Values of sample entropy were acquired through multi-scale entropy (MSE) (Figure 2a) and short time multi-scale entropy (Figure 2b) methods using simulation white noise and 1/f noise with different scale factors on 30 sets of 1000 successive signals
Summary
Atherosclerosis, which is the major pathological change underlying most cardiovascular diseases, has been reported to be associated with advanced age, history of stroke, diabetes, hypertension, and cerebrovascular disease. Pulse wave velocity (PWV) is one of the most popular non-invasive parameters used for the assessment of atherosclerosis. Despite the different equipment used for data acquisition, a mean value is usually obtained from the examinee for evaluating the severity of the condition [1,2,3,4,5,6]. “Multi-scale entropy (MSE)” is a non-linear means of assessing the complexity of physiological signals [8,9,10]. Compared to the traditional complexity measures, MSE has the advantage of being applicable to both physiologic and simulated signals of finite length.
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