On multiscale entropy analysis for physiological data

Falls are unpredictable accidents and resulting injuries can be serious to the elderly. A preventative solution can be the use of vibration stimulus of white noise to improve the sense of balance. In this work, a pair of vibration shoes were developed and controlled by a touch-type switch which can generate mechanical vibration noise to stimulate the patient's feet while wearing the shoes. In order to evaluate the balance stability and treatment effect of vibrating insoles in these shoes, multivariate multiscale entropy (MMSE) algorithm is applied to calculate the relative complexity index of reconstructed center of pressure (COP) signals in antero-posterior and medio-lateral directions by the multivariate empirical mode decomposition (MEMD). The results show that the balance stability of 61.5% elderly subjects is improved after wearing the developed shoes, which is OPEN ACCESSEntropy 2012, 14 2158 more than 30.8% using multiscale entropy. In conclusion, MEMD-enhanced MMSE is able to distinguish the smaller differences between before and after the use of vibration shoes in both two directions, which is more powerful than the empirical mode decomposition (EMD)-enhanced MSE in each individual direction.

Section: Multivariate Multiscale Entropymentioning

confidence: 99%

Multivariate Multiscale Entropy Applied to Center of Pressure Signals Analysis: An Effect of Vibration Stimulation of Shoes

Wei

Liu

Wang

et al. 2012

“…While current publications on MSE may discuss one or two methodological issues, no publication-to our knowledge-comprehensively covers all the issues presented here. For the MSE-naïve researcher, designing a protocol for the purpose of MSE analysis can be daunting, and this difficulty can be further amplified by the recognition that improper choice of parameters during MSE analysis can lead to ambiguity in complexity signatures between healthy and diseased states [13]. In this paper, we address a number of key issues involved in study design, analysis and interpretation of MSE for physiological signals using COP as a model example.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Entropy Analysis of Center-of-Pressure Dynamics in Human Postural Control: Methodological Considerations

Gow

Peng

Wayne

et al. 2015

Multiscale entropy (MSE) is a widely used metric for characterizing the nonlinear dynamics of physiological processes. Significant variability, however, exists in the methodological approaches to MSE which may ultimately impact results and their interpretations. Using publications focused on balance-related center of pressure (COP) dynamics, we highlight sources of methodological heterogeneity that can impact study findings. Seventeen studies were systematically identified that employed MSE for characterizing COP displacement dynamics. We identified five key methodological procedures that varied significantly between studies: (1) data length; (2) frequencies of the COP dynamics analyzed; (3) sampling rate; (4) point matching tolerance and sequence length; and (5) filtering of displacement changes from drifts, fidgets, and shifts. We discuss strengths and limitations of the various approaches employed and supply flowcharts to assist in the decision making process regarding each of these procedures. Our guidelines are intended to more broadly inform the design and analysis of future studies employing MSE for continuous time series, such as COP.

“…In doing so, the value of α chosen will of necessity be sensitive to different aspects of sympathovagal balance, which may reflect changes in the baroreflex responsiveness due to CAN progression. Further, the decrease in entropy for negative values of α may reflect the loss of nonlinear dynamics naturally inherent as part of the cardiac rhythm and therefore loss of capacity of the heart to respond to different physiological demands [38]. Although participants were comparable for age, gender, and heart rate, they were not fully matched and this may be a limitation of this study.…”

Section: Discussionmentioning

confidence: 92%

A Comparison of Nonlinear Measures for the Detection of Cardiac Autonomic Neuropathy from Heart Rate Variability

Cornforth

Jelinek

Tarvainen

2015

Abstract:In this work we compare three multiscale measures for their ability to discriminate between participants having cardiac autonomic neuropathy (CAN) and aged controls. CAN is a disease that involves nerve damage leading to an abnormal control of heart rate, so one would expect disease progression to manifest in changes to heart rate variability (HRV). We applied multiscale entropy (MSE), multi fractal detrended fluctuation analysis (MFDFA), and Renyi entropy (RE) to recorded datasets of RR intervals. The latter measure provided the best separation (lowest p-value in Mann-Whitney tests) between classes of participants having CAN, early CAN or no CAN (controls). This comparison suggests the efficacy of RE as a measure for diagnosis of CAN and its progression, when compared to the other multiscale measures.