Altered EMG patterns in diabetic neuropathic and not neuropathic patients during step ascending and descending

Surface electromyography for characterizing neuromuscular changes in diabetic peripheral neuropathy

PURPOSE

Distal symmetric polyneuropathy (DSP) is the most common diabetic neuropathy (75% of all diabetic neuropathies), potentially leading to plantar ulcers and lower limb amputation. Early detection of at-risk individuals is essential for timely intervention. This study aimed to evaluate different surface EMG (sEMG) parameters from isometric and dynamic exercises as biomarkers for early DSP detection and monitoring.

METHODS

The study involved 61 participants (34 controls and 27 cases with low, moderate and high levels of severity). sEMG signals were recorded from four lower-limb muscles (tibialis anterior, medial gastrocnemius, extensor digitorum brevis, and flexor digitorum brevis) and characterized by their amplitude, frequency, complexity and shape.

RESULTS

Significant sEMG differences were found between controls and diabetic patients at low and moderate/high DSP risk, especially in the extensor digitorum and flexor digitorum muscles during isometric and dynamic exercises. Diabetic patients showed lower amplitude and complexity, with higher frequency, peakedness, and asymmetry.

CONCLUSION

Parameters like root mean square, sample entropy, and central shape distance effectively distinguished between groups, highlighting changes in motor unit recruitment and muscle quality. Dynamic and isometric exercises emphasized their complementary roles in assessing muscle function, supporting sEMG's potential as a non-invasive tool for monitoring neuromuscular changes in diabetes.

Advancing the Diagnosis of Diabetic Neuropathies: Electrodiagnostic and Skin Autofluorescence Methods

INTRODUCTION

Diabetic neuropathy (DN) is a generic term for various neuropathies coexisting in a single patient. Clinical diagnosis alone can be misleading, yet routine electrodiagnostic studies in diabetes care are rare. Skin autofluorescence (SAF) is a recognized DN risk factor with potential screening value. This article highlights the diagnostic challenges and raises awareness of the often underdiagnosed neuropathic conditions in diabetes patients.

MATERIAL AND METHODS

We present common entrapment neuropathy cases from our diabetes clinic's electrodiagnosis laboratory in Iași, Romania. We selected seven type 2 diabetes patients with sensory or sensory-motor distal polyneuropathy and atypical DN presentations investigated through electroneurography (ENG) and electromyography (EMG) with the Neurosoft® EMG instrument and SAF measured by standard procedures. Subsequently, a narrative literature review was conducted.

RESULTS

Entrapment neuropathies were diagnosed in all the patients: three carpal tunnel syndromes, two ulnar neuropathies (one proximal, one distal), one peroneal neuropathy, and one case of meralgia paresthetica. The lower-limb cases showed radiculoplexopathy, and there was one case of superficial radial nerve neuropathy. The SAF values ranged from 2.5 AU to 3.4 AU.

CONCLUSIONS

Electrodiagnosis is essential for detecting focal neuropathies in patients with sensory-motor distal polyneuropathy. Elevated SAF levels may correlate with symptom severity, although further research, including large cohorts, is needed.

Muscle Network Connectivity Study in Diabetic Peripheral Neuropathy Patients

Diabetic peripheral neuropathy (DPN) is a prevalent complication of chronic diabetes mellitus and has a significant impact on quality of life. DPN typically manifests itself as a symmetrical, length-dependent sensorimotor polyneuropathy with severe effects on gait. Surface electromyography (sEMG) is a valuable low-cost tool for assessing muscle activation patterns and precise identification of abnormalities. For the present study, we used information theory methods, such as cross-correlation (CC), normalized mutual information (NMI), conditional granger causality (CG-Causality), and transfer entropy (TE), to evaluate muscle network connectivity in three population groups: 33 controls (healthy volunteers, CT), 10 diabetic patients with a low risk of DPN (LW), and 17 moderate/high risk patients (MH). The results obtained indicated significant alterations in the intermuscular coupling mechanisms due to diabetes and DPN, with the TE group showing the best performance in detecting differences. The data revealed a significant increase in information transfer and muscle connectivity in the LW group over the CT group, while the MH group obtained significantly lower values for these metrics than the other two groups. These findings highlight the sEMG coupling metrics' potential to reveal neuromuscular mechanisms that could aid the development of targeted rehabilitation strategies and help monitor DPN patients.

Effect of Foot Reflexology on Muscle Electrical Activity, Pressure, Plantar Distribution, and Body Sway in Patients with Type 2 Diabetes Mellitus: A Pilot Randomized Controlled Trial

OBJECTIVE

To verify the effect of foot reflexology on the electrical muscle activity of the lateral and medial gastrocnemius muscle, and to examine the distribution, plantar pressure, and body sway in patients with type 2 diabetes mellitus.

METHODS

This pilot randomized controlled trial enrolled 17 volunteers who were clinically diagnosed with diabetes mellitus. The sample was assigned to one of two groups: the control group (CG, n = 7), who received information on foot care and health, and the intervention group (IG, n = 10), who received the application of foot reflexology on specific areas of the feet, for 10 consecutive days. There was blinding of the evaluator and the therapist. Surface electromyography (EMG) was used to assess the electrical activity of the medial and lateral gastrocnemius muscles in maximum voluntary isometric contraction (MVIC) and isotonic contraction (IC); baropodometry and stabilometry were used to analyze unloading, plantar weight distribution, and body sway.

RESULTS

There was a statistically significant difference for the variables of maximum peak electrical activity of the left medial gastrocnemius (p = 0.03; effect size = 0.87 and power = 0.81) and left lateral gastrocnemius muscles (p = 0.04, effect size = 0.70 and power = 0.66) respectively, in the intragroup IC, and median frequency of the left medial gastrocnemius muscle in the intragroup MVIC (p = 0.03; effect size = 0.64 and power = 0.59), and in the variables intergroups of the total area on the right side (p = 0.04; effect size = 1.03 and power = 0.50) and forefoot area on the left side (p = 0.02; effect size = 0.51 and power = 0.16).

CONCLUSIONS

We conclude that foot reflexology influenced some variables of the intergroup plantar distribution and intragroup EMG in the sample studied. There is a need for a placebo group, a larger sample and a follow-up to strengthen the findings of these experiments.

EMG analysis across different tasks improves prevention screenings in diabetes: a cluster analysis approach

The aim of this work was twofold: on one side to determine the most suitable parameters of surface electromyography (sEMG) to classify diabetic subjects with and without neuropathy and discriminate them from healthy controls and second to assess the role of the task acquired in the classification process. For this purpose 30 subjects were examined (10 controls, 10 diabetics with and 10 without neuropathy) whilst walking and stair ascending and descending. The electrical activity of six muscles was recorded bilaterally through a 16-channel sEMG system synchronised with a stereophotogrammetric system: Rectus Femoris, Gluteus Medius, Tibialis Anterior, Peroneus Longus, Gastrocnemius Lateralis and Extensor Digitorum. Spatiotemporal parameters of gait and stair climbing and the following sEMG parameters were extracted: signal envelope, activity duration, timing of activation and deactivation. A hierarchical clustering algorithm was applied to the whole set of parameters with different distances and linkage methods. Results showed that only by applying the Ward agglomerative hierarchical clustering (Hamming distance) to the all set of parameters extracted from both tasks, 5 well-separated clusters were obtained: cluster 3 included only DS subjects, cluster 2 and 4 only controls and cluster 1 and 5 only DNS subjects. This method could be used for planning rehabilitation treatments.

Graphical abstract

Machine Learning-Based Diabetic Neuropathy and Previous Foot Ulceration Patients Detection Using Electromyography and Ground Reaction Forces during Gait

Diabetic neuropathy (DN) is one of the prevalent forms of neuropathy that involves alterations in biomechanical changes in the human gait. Diabetic foot ulceration (DFU) is one of the pervasive types of complications that arise due to DN. In the literature, for the last 50 years, researchers have been trying to observe the biomechanical changes due to DN and DFU by studying muscle electromyography (EMG) and ground reaction forces (GRF). However, the literature is contradictory. In such a scenario, we propose using Machine learning techniques to identify DN and DFU patients by using EMG and GRF data. We collected a dataset from the literature which involves three patient groups: Control (n = 6), DN (n = 6), and previous history of DFU (n = 9) and collected three lower limb muscles EMG (tibialis anterior (TA), vastus lateralis (VL), gastrocnemius lateralis (GL)), and three GRF components (GRFx, GRFy, and GRFz). Raw EMG and GRF signals were preprocessed, and different feature extraction techniques were applied to extract the best features from the signals. The extracted feature list was ranked using four different feature ranking techniques, and highly correlated features were removed. In this study, we considered different combinations of muscles and GRF components to find the best performing feature list for the identification of DN and DFU. We trained eight different conventional ML models: Discriminant analysis classifier (DAC), Ensemble classification model (ECM), Kernel classification model (KCM), k-nearest neighbor model (KNN), Linear classification model (LCM), Naive Bayes classifier (NBC), Support vector machine classifier (SVM), and Binary decision classification tree (BDC), to find the best-performing algorithm and optimized that model. We trained the optimized the ML algorithm for different combinations of muscles and GRF component features, and the performance matrix was evaluated. Our study found the KNN algorithm performed well in identifying DN and DFU, and we optimized it before training. We found the best accuracy of 96.18% for EMG analysis using the top 22 features from the chi-square feature ranking technique for features from GL and VL muscles combined. In the GRF analysis, the model showed 98.68% accuracy using the top 7 features from the Feature selection using neighborhood component analysis for the feature combinations from the GRFx-GRFz signal. In conclusion, our study has shown a potential solution for ML application in DN and DFU patient identification using EMG and GRF parameters. With careful signal preprocessing with strategic feature extraction from the biomechanical parameters, optimization of the ML model can provide a potential solution in the diagnosis and stratification of DN and DFU patients from the EMG and GRF signals.

A Supervised Classification of Children with Fragile X Syndrome and Controls Based on Kinematic and sEMG Parameters

Fragile X syndrome (FXS) is caused by pathologic expansions of the CGG repeat polymorphic region of the FMR1 gene. There are two main categories of FMR1 mutations, “premutation” and “full mutation”, that are associated with different clinical phenotypes, and somatic mosaicism can represent a strong FXS phenotype modulator. FXS is the leading cause of inherited intellectual disability and autism, and it is characterized by musculoskeletal manifestations such as flexible flat feet, joint laxity and hypotonia. The former have been associated with altered joint kinematics and muscle activity during gait. The aim of this study was to use gait analysis parameters to classify FXS children from healthy controls and, within FXS children with full mutation, to classify children with mosaicism. Seven supervised machine learning algorithms were applied to a dataset of joint kinematics and surface electromyographic signals collected on twenty FXS children and sixteen controls. Results showed that the k-NN algorithm outperformed in terms of accuracy (100%) in classifying FXS children from controls, while CN2 rule induction obtained the best accuracy (97%) in classifying FXS children with mosaicism. The proposed pipeline might be used for developing assisted decision-making systems aiming at identifying and treating the musculoskeletal alterations associated with FXS.

Investigation of surface electromyography amplitude values during stair climbing task in children with Duchenne muscular dystrophy

OBJECTIVE

The aims of this study were (a) to examine the surface electromyography (sEMG) amplitude values of the lower limb muscles during stair climbing both between different functional levels of Duchenne muscular dystrophy (DMD), in comparison with healthy children, and (b) to investigate the relationships between sEMG amplitudes and physical performance.

METHODS

sEMG amplitudes of the lower limbs of twenty-one children with DMD between levels I and III according to the Brooke Lower Extremity Functional Classification Scale and eleven healthy peers were evaluated by using sEMG during stair climbing task. Physical performance was evaluated by 6-min walk test and ascending 4-step timed performance test.

RESULTS

The lower limb sEMG amplitude values of children with DMD were statistically higher than healthy children (p < 0.001). sEMG amplitudes of the right (p = 0.01) and left (p = 0.003) biceps femoris, the right (p < 0.001) and left (p = 0.001) gastrocnemius medialis, and the right vastus lateralis (p = 0.02) muscles were higher in children with levels 2-3 than those in level 1. Moderate-to-strong relations were found between the gastrocnemius medialis and biceps femoris sEMG amplitudes and physical performance assessments (p < 0.05).

CONCLUSION

Increased sEMG amplitude values in the lower limbs during stair climbing task are thought to be caused by the effort to compensate for progressive muscle weakness and are associated with lower physical performance in children with DMD. Further, sEMG amplitude values are determined to increase as the functional level deteriorates.

CLINICAL TRIAL REGISTRATION NUMBER AND URL

NCT04287582 ( https://clinicaltrials.gov/ct2/show/NCT04287582?term=merve+bora&draw=2&rank=1 ).

A Scoping Review of Epidemiological, Ergonomic, and Longitudinal Cohort Studies Examining the Links between Stair and Bathroom Falls and the Built Environment

Stair and bathroom falls contribute to injuries among older adults. This review examined which features of stairs and bathrooms have been assessed in epidemiological, ergonomic, and national aging studies on falls or their risk factors. Epidemiological and ergonomic studies were eligible if published from 2006-2017, written in English, included older persons, and reported built environment measures. The data extracted included the following: study population and design, outcome measures, and stair and bathroom features. National aging studies were eligible if English questionnaires were available, and if data were collected within the last 10 years. Sample characteristics; data collection methods; and data about falls, the environment, and assistive device use were extracted. There were 114 eligible articles assessed-38 epidemiologic and 76 ergonomic. Among epidemiological studies, 2 assessed stair falls only, 4 assessed bathroom falls only, and 32 assessed falls in both locations. Among ergonomic studies, 67 simulated stairs and 9 simulated bathrooms. Specific environmental features were described in 14 (36.8%) epidemiological studies and 73 (96%) ergonomic studies. Thirteen national aging studies were identified-four had stair data and six had bathroom data. Most epidemiologic and national aging studies did not include specific measures of stairs or bathrooms; the built environment descriptions in ergonomic studies were more detailed. More consistent and detailed environmental measures in epidemiologic and national aging studies would better inform fall prevention approaches targeting the built environment.

SIGNIFICANCE OF ELECTROMYOGRAPHY IN THE ASSESSMENT OF DIABETIC NEUROPATHY

Diabetic neuropathy is one of the physical complications of diabetes mellitus (DM) patients with a long history of diabetes. An electromyography (EMG)-based assessment may be very useful for the management of diabetic neuropathy. In the present study, we aimed to summarize all of the findings and recommendations obtained from previous studies that investigated the application of EMG to the assessment of diabetic neuropathy. An extensive search of the prominent electronic databases PubMed, Google Scholar and Scopus was performed to evaluate the following areas: (i) what are the muscles to be evaluated by EMG for neuropathy assessment, (ii) what type of EMG methodologies have been used and (iii) what recommendation can be made for neuropathy detection. The major findings are summarized as follows: (i) very few studies have analyzed the correlation of the EMG signals acquired from peripheral muscles affected in neuropathy with those obtained with non-neuropathic complications, such as ankle sprain; (ii) EMG has been applied for the detection of diabetic neuropathy more than diabetes treatment; and (iii) neuropathy detection using an EMG-based assessment were mainly performed for type 2 DM patients aged at least 50 years.

Comparison of lower limb muscle strength between diabetic neuropathic and healthy subjects using OpenSim

Diabetes neuropathy and vasculopathy are the two major complications of diabetes mellitus, leading to diabetic foot disease, of which the worst consequences are plantar ulcers and amputations. Motor impairments like joint stiffness and loss of balance are distinctive effects of diabetes and they have been extensively explored. However, while altered muscle function has been also assessed through experimentally measured surface electromyography, little is known about muscle forces. The objective of this study was to estimate muscle forces in subjects with diabetes and to use these data to identify differences with respect to a population of healthy subjects matched for age and BMI. This was obtained by generating musculoskeletal models of 10 diabetic and 10 control subjects in OpenSim starting from experimentally recorded data. Dynamic simulations of motion were run and hence muscle forces calculated. Student T test (p<0.05) was used to compare joints kinematics, kinetics and muscle forces between the two populations. Significant changes were observed between lower limb muscle forces and activation of diabetic and healthy subjects, as well as between joints kinematics and kinetics. In particular muscles related to foot movements proved to be stronger in the healthy population. The typical ankle rigidity of the diabetic population was confirmed by a lower range of motion registered at the ankle plantar/flexion angle associated with weaker dorsal-plantar flexor muscles. The information provided by this methodology can help planning specific training programs aiming at augmenting muscle strength and joints mobility, and they can also improve the evaluation of the potential benefits.

Acute strength training promotes responses in whole blood circulating levels of miR-146a among older adults with type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) consists of a set of metabolic and endocrine disorders which evolve into deficiency in insulin action and hyperglycemia. Physical exercise is considered the main intervention to prevent and control T2DM. Literature has suggested that circulating microRNAs (miRs) help to understand responses to physical activity among diabetic patients. Thus, the aim of this study was to analyze the acute effect of two interventions (strength and cardiovascular) on the total, whole blood circulating concentrations of miR-126, miR-146a and miR-155 in older adults with and without T2DM. A total of 23 male and female older adults (68.2±5.3 years) participated in the trial, 13 of whom presented with controlled T2DM and 10 were nondiabetics. They underwent both interventions separately, performed with intensity from 60% to 70% of reserve heart rate. Glucose and miRs levels were quantified and compared across groups with baseline titers as covariables. Diabetic patients showed more reduction in serum blood glucose than nondiabetics, with a great magnitude of reduction after the strength training intervention, which was paralleled by a positive change of the whole blood circulating levels of miR-146a, but not of the other miRs. Our report supports evidence that miR-146a levels in peripheral blood leukocytes are negatively associated with a state of insulin resistance, which is suggested as a novel marker to trace response to antidiabetic interventions.