Complexity Measures of Postural Control in Type-2 Diabetic Subjects

Area of Science:

• Biomedical Engineering
• Neuroscience
• Diabetology

Background:

• Balance maintenance is crucial for preventing falls and is often assessed using center of pressure (COP) displacement.
• COP time-series exhibit non-stationary behavior, necessitating advanced analytical methods like complexity measures.
• Approximate entropy (AppEn) and sample entropy (SampEn) are established complexity measures but are sensitive to input parameters.

Purpose of the Study:

• To evaluate the efficacy of AppEn, SampEn, and fuzzy entropy (FuzzyEn) in analyzing COP time-series in type-2 diabetic subjects.
• To compare the postural control complexity between diabetic individuals with and without neuropathy during quiet standing.
• To assess the consistency of entropy measures across different input parameter values.

Main Methods:

• Analysis of center of pressure (COP) time-series data from type-2 diabetic subjects with and without neuropathy.
• Computation of COP regularity using approximate entropy (AppEn), sample entropy (SampEn), and fuzzy entropy (FuzzyEn).
• Quiet standing trials with eyes open condition were employed to collect postural data.

Main Results:

• Entropy measures demonstrated consistent results across various input parameter values.
• Significant differences in COP regularity were observed between diabetic subjects with and without neuropathy in both anterior-posterior and medial-lateral directions.
• Neuropathic subjects exhibited lower complexity in postural control, suggesting a reduced capacity for adaptable balance responses.

Conclusions:

• Fuzzy entropy (FuzzyEn) offers a reliable method for analyzing COP time-series, consistent across different parameter settings.
• Diabetic neuropathy significantly impacts postural control complexity, even in the medial-lateral direction.
• Complexity measures are valuable tools for assessing balance deficits in patients with diabetic neurological impairment.