Multiscale Fuzzy Entropy Analysis of Balance: Evidences of Scale-Dependent Dynamics on Diabetic Patients With and Without Neuropathy

Abstract

Postural control is usually assessed by examining the fluctuations of the center of pressure (COP). Balance maintenance is based on sensory feedback and neural interactions, deployed over multiple temporal scales and producing less complex outputs with aging and disease. This paper aims to investigate postural dynamics and complexity on diabetic patients, since diabetic neuropathy (DN) affects the somatosensory system and impairs postural steadiness. A multiscale fuzzy entropy (MSFEn) analysis, over a wide range of temporal scales, was performed on COP timeseries during unperturbed stance in a group of diabetic individuals without neuropathy and two groups of DN patients, with and without symptoms. A parameterization of the MSFEn curve is also proposed. A significant loss of complexity was recognized for the medial-lateral direction in DN groups with respect to non-neuropathic population. For the anterior-posterior direction, symptomatic DN group showed a lowered sway complexity for longer time scales with respect to non neuropathic and asymptomatic patients. The MSFEn approach and the related parameters highlighted that the loss of complexity might be attributed to different factors depending on sway direction, i.e. related to the presence of neuropathy along the medial-lateral axis and to a symptomatic state on the anterior-posterior direction. Results of this study support the use of the MSFEn for gaining insights into balance control mechanisms for diabetic patients, in particular when comparing non neuropathic with neuropathic asymptomatic patients, whose identification by posturographic analysis would be of great value.

Approximate Entropy and Velocity of Center of Pressure to Determine Postural Stability: A Pilot Study

“…With an understanding of how ApEn and velocity of COP behave for varying levels of stability in neurotypical subjects, these indices may be useful to screen for conditions such as concussion in a more objective manner. Linear and nonlinear measures have been previously used in studies to determine changes to stability based on different neurological and physical impairments [46,47]. Although these studies compared a baseline control group to a group with impaired postural control as opposed to comparing a baseline condition of an individual to adjusted conditions of the same individual and utilized a different quiet standing methodology than this study, the clinical application of these measures on COP to distinguish postural control encourages the motivation for this work.…”

Kalman filtering to reduce measurement noise of sample entropy: An electroencephalographic study

“…Electroencephalography (EEG) is highly nonlinear and entropy measures have long been used in clinical practice to reveal the nonlinear nature, for example, in classifying walking limitations [ 1 ], analyzing complexity and variability of trunk accelerations in patients with Parkinson’s Disease [ 2 , 3 ], differentiating balance patterns in diabetic patients with and without neuropathy [ 4 ], assessing anesthetic drug effects on the brain [ 5 ], identifying fetal distress [ 6 ], autism spectrum disorder in children [ 7 ], tinnitus [ 8 ], attention deficit hyperactivity disorder [ 9 ], epilepsy [ 10 ], Alzheimer’s disease [ 11 ], schizotypy [ 12 ], mind wandering [ 13 ] and psychogenic non-epileptic seizures [ 14 ]. Examples of entropy measures are permutation entropy [ 15 ], approximate entropy [ 16 ], neural network entropy [ 17 ], dispersion entropy [ 18 ], sample entropy [ 19 , 20 ] and their variants [ 21 – 25 ].…”

Multiscale Entropy Algorithms to Analyze Complexity and Variability of Trunk Accelerations Time Series in Subjects with Parkinson’s Disease

“…When the probability is low, the subsequent data points in the system provide a greater amount of new information, resulting in high entropy values, indicating greater gait irregularity or complexity of the gait pattern. Several methods for calculating entropy have been proposed [ 41 , 42 , 43 , 44 , 45 , 46 ]. Among them, sample entropy (SampEn) [ 43 ]-based methods have been described as valid tools for assessing gait regularity in healthy subjects and pathological conditions [ 40 , 47 , 48 ], including trunk acceleration-derived gait signals from swPD [ 49 ].…”