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2 Biocybernetics and Biomedical Engineering

2 2021

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Machine learning prediction of future peripheral neuropathy in type 2 diabetics with percussion entropy and body mass indices

Xiao Ming-Xia, Lu Chang-Hua, Ta Na, Wei Hai-Cheng, Haryadi Bagus, Wu Hsien-Tsai

Biocybernetics and Biomedical Engineering

2021

Vol. 41, no. 3

1140--1149

This study was designed to evaluate the clinical applications of body mass index (BMI) and a percussion-entropy-based index (PEINEW) for predicting the development of diabetic peripheral neuropathy (DPN) in a group of type 2 diabetes mellitus (DM) patients. The study population comprised a sample of 90 subjects with diabetics (aged 37–86 years), who went through a blood test and photoplethysmography (PPG) measurement and were then followed for 5.5 years. Conventional parameters, including the small-scale multiscale entropy index (MEISS), pulse wave velocity with electrocardiogram located (PWVmean), and PEIoriginal, were computed and compared. A logistic regression model with PEINEW and a single categorical variable (BMI) showed a graded association between the diabetics, with a high BMI (i.e., ‘‘high” category) associated with a 12.53-fold greater risk of developing DPN relative to the diabetics with a low BMI (i.e., ‘‘low” category) (p = 0.001). The odds ratio for PEINEW was 0.893. The Kaplan-Meier survival analysis showed that the diabetic patients with BMI ＞ 30 had a significantly higher cumulative incidence of PN on follow-up than those with BMI [...] 30 (log-rank test, p < 0.001). These findings suggest that BMI and PEINEW are both important risk and protective factors for new-onset DPN from diabetes mellitus and, thus, BMI and percussion entropy calculation can provide valid information that may help to identify diabetics with a high BMI and a low PEINEW as being at increased risk of future DPN.

Characterization of the anteroposterior center of pressure in upright position in type 2 diabetics with peripheral diabetic neuropathy and paired healthy controls

Toloza-Cano Daissy Carola, Zequera Martha, González Gustavo Hernando Castro

Biocybernetics and Biomedical Engineering

2021

Vol. 41, no. 1

306--315

Peripheral diabetic neuropathy (PDN) is a complication of type 2 diabetes (T2DM) that impairs posture control and increases the risk of falling. The aim of this study was to characterize the anteroposterior center of pressure (COP-AP) in the time and the frequency domains in the T2DM/PDN group in relation to the control group. To that end we: (1) evaluated the efficacy of using both linear and non-linear discrete wavelet transform (DWT) analyses to evaluate oscillation patterns in the anteroposterior center of pressure (COP-AP) in the bipedal position in terms of time and frequency and (2) established input parameters for a model for predicting the risk of falling. This study included an experimental sample of 30 people with T2DM/PDN matched by gender, age, weight and height with a control sample of 30 healthy individuals. Unreported techniques for analyzing the COP-AP literature were assessed for their capacity to model patient bodily stability in the proprioceptive, visual and vestibular systems. To measure COP-AP, five tests were performed under different conditions as outlined in the Romberg Test using the ‘‘PEDAR System’’ for measuring plantar pressure. DWTs were used to calculate excursion parameters, average speeds, range, RMS values, the average maximum and minimum amplitude, power spectral densities and energy percentages in 11 frequency bands (D1 to D10 and A10). There were significant differences between the two study groups in terms of the calculated linear parameters ( p < 0.05). Using linear and non-linear DWT analyses, a preliminary characterization of COP-AP patterns was achieved. DWT could be used alongside linear analysis to determine the effect changes in these systems have on postural oscillation in people with T2DM.