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

Abstract

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.

The Complexity of Standing Postural Sway Associates with Future Falls in Community-Dwelling Older Adults: The MOBILIZE Boston Study

“…Sample entropy is defined by the negative natural logarithm of the conditional probability that a time-series, having repeated itself within a tolerance r for m points (pattern length), will also repeat itself for m  +  1 points without self-matches. The sample entropy of each coarse-grained time-series in this study was computed by choosing m  = 2 and r  = 15% 19 . Figure 1C showed the MSE curves generated by plotting sample entropy as a function of time scale from the two COP time-series presented in Fig.…”

Postural Control Entropy Is Increased When Adopting an External Focus of Attention

“…Relative to aging and postural control, some research has shown that postural control entropy is increased in older adults relative to younger adults (Borg & Laxåback, 2010;Duarte & Sternad, 2008). However, recent review papers by Gow, Peng, Wayne, and Ahn (2015) and , along with several research studies (Costa et al, 2007;Jiang, Yang, Shieh, Fan, & Peng, 2013;Kang et al, 2009;Lamoth & van Heuvelen, 2012;Yeh, Lo, Chang, & Hsu, 2014), highlight the observation that the majority of research examining postural control entropy in older adults suggests that it declines with aging and frailty. A potential reason for these dichotomist observations could be methodological differences between the studies, as suggested by Gow et al (2015).…”

“…However, recent review papers by Gow, Peng, Wayne, and Ahn (2015) and , along with several research studies (Costa et al, 2007;Jiang, Yang, Shieh, Fan, & Peng, 2013;Kang et al, 2009;Lamoth & van Heuvelen, 2012;Yeh, Lo, Chang, & Hsu, 2014), highlight the observation that the majority of research examining postural control entropy in older adults suggests that it declines with aging and frailty. A potential reason for these dichotomist observations could be methodological differences between the studies, as suggested by Gow et al (2015). Since a decline in postural control entropy has recently been associated with fall risk in a longitudinal and large-sample-size study (Zhou, Habtemariam, Iloputaife, Lipsitz, & Manor, 2017), we take the position that a decline in postural control entropy is expected with aging, and it reflects a change in postural control that characterizes the difficulty in responding to perturbations.…”

Entropy-based EEG time interval selection for improving motor imagery classification

“…Selecting the parameters of the proposed algorithm The parameters of the proposed MSE-based EEG interval selection algorithm should be carefully chosen as their values may have great influence on the performance. These parameters include the matching threshold (r), the embedding dimension (m), the length of the coarse-grained signal (C), and the scale factor (ß) [19]. In the following, we discuss how to define the values of the abovementioned parameters.…”