## 4 Discussion
[16] and our findings, there were also differences when comparing the two entropy value magnitudes. We found that, overall, ApEn magnitudes were greater than SampEn values, whereas [PERSON] et al. reported that generally mean ApEn values were lower than mean SampEn values in the analysis of step time gait data. We believe our study's results can provide more than academic interest. Although our participants were young healthy adults, we have shown that ApEn and SampEn can differentiate COP signal regularity among different quiet standing positions, i.e., EOFT and EOTanDB. Similarly, [PERSON] et al. [19] demonstrated, with young healthy adults, the sensitivity of ApEn to COP changes related to secondary cognitive tasks during quiet standing, suggesting support for the potential of ApEn to detect subtle changes in postural control. [PERSON] and colleagues [10; 18; 64] reported using ApEn to examine COP anterior-posterior and medial-lateral time series in athletes with concussion. They found, for example, that (1) concussed athletes with normal postural stability nonetheless demonstrated more regular COP oscillations, i.e., lower ApEn values; and (2) ApEn values for the medial-lateral time series remained significantly depressed among athletes whose initial postural stability had resolved. Others [65] examined the use of ApEn, SampEn, and fuzzy entropy (FuzzyEn), computed using multiple combinations of \(m\) and \(r\), to analyze COP time series in type-2 diabetic patients with and without neuropathy during quiet trials with eyes open. Similar to our findings, they reported the consistency of entropy measures for different input values and showed significant differences between the two cohorts in terms of COP regularity in the AP and ML directions. In that study, the FuzzyEn results suggested low complexity in the postural control of neuropathic patients in the ML direction. The authors of this report indicated that measures of complexity, like FuzzyEn, could complement the use of ApEn and SampEn. Although our results suggest that the application of ApEn and SampEn may be equally effective in detecting subtle changes in COP time series from balance studies involving elderly individuals, for those with head injuries, e.g., post-concussion, or other neuropathologies, the use of additional non-linear analyses may be needed. For example, since the functions of physiological systems are deployed through the interactions between different control dynamics, across multiple spatial and temporal scales, multiscale entropy (MSE), which is a derivative of SampEn, performs an entropy analysis over multiple time scales, providing a measure of the complexity of a time series rather than only quantification of its regularity [66; 67; 68; 69]. Previous research has demonstrated that multiscale entropy (MSE) has greater potential for use in tests of standing balance for other clinical entities, e.g., peripheral neuropathies related to diabetes and multiple sclerosis. [PERSON] et al. [70], examining COP time series during unperturbed standing in patients with and without neuropathic symptoms, reported a significant loss of complexity in the COP time series medial-lateral direction in the symptomatic neuropathic cohort. In another application, [PERSON] and colleagues [71]aimed to identify whether the complexity index (a derivative of MSE) of the COP time series was reduced in those with multiple sclerosis (MS) compared to controls under conditions of limited vision and increasingly demanding standing tasks. They found that COP complexity was significantly reduced in the anterior-posterior and medial-lateral directions in the MS group compared to controls during the performance of maximal self-regulated leans and with reduced vision, and that complexity was correlated with cutaneous sensitivity. These data suggest that the future use of entropy measures for clinical testing consider several alternative options.