A Preliminary Review on EMG Signals for Assessment of Diabetic Peripheral Neuropathy Disorder

Ullah, Safi; Iqbal, Kamran

doi:10.1109/iceee49618.2020.9102488

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…These signals are also used in developing biomechatronic prosthetic limbs (Sheibani and Pourmina 2016) and hybrid assistive limbs (George et al 2011). EMG signals are used in assessing diabetic peripheral neuropathy (DPN) disorder (Ullah and Iqbal 2020;Rabbi et al 2019;Perkins and Bril 2014) and the development of foot ulcers in diabetic patients (Rabbi et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Time-domain Feature and Ensemble Model based Classification of EMG Signals for Hand Gesture Recognition

Bhattacharjee

Singh

2021

Preprint

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The electromyography (EMG) signal is the electrical current generated in muscles due to the inter-change of ions during their contractions. It has many applications in clinical diagnostics and the biomedical field. This paper has experimented with various ensemble algorithms and time-domain features to classify eight types of hand gestures. To train and test the machine learning models, we have extracted eight types of time-domain features from the raw EMG signals, such as integrated EMG (IEMG), variance, mean absolute value (MAV), modified mean absolute value type 1, waveform length, root mean square, average amplitude change, and difference absolute standard deviation value. The ensemble machine learning models are based on stacking, bagging, and gradient boosting. We have used four different-sized training sets to evaluate the performance of these classifiers. From the performance evaluation, we have identified the XG-Boost (gblinear) classifier with the IEMG feature as the best classifier-feature pair. The proposed classifier-feature pair has given better performance with a classification accuracy of 98.33% and a processing time of 5.67 μs for one vector than the existing extended associative memory-MAV classifier-feature pair.

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Section: Introductionmentioning

confidence: 99%

Time-domain Feature and Ensemble Model based Classification of EMG Signals for Hand Gesture Recognition

Bhattacharjee

Singh

2021

Preprint

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Simulation on Application of EMG Signals for DC Motor Control

Lim,

Chai

2023

Lecture Notes in Electrical Engineering

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Muscle Network Connectivity Study in Diabetic Peripheral Neuropathy Patients

Junquera-Godoy,

Martinez-De-Juan,

González-Lorente

et al. 2024

Sensors

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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.

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A Preliminary Review on EMG Signals for Assessment of Diabetic Peripheral Neuropathy Disorder

Cited by 3 publications

References 6 publications

Time-domain Feature and Ensemble Model based Classification of EMG Signals for Hand Gesture Recognition

Time-domain Feature and Ensemble Model based Classification of EMG Signals for Hand Gesture Recognition

Simulation on Application of EMG Signals for DC Motor Control

Muscle Network Connectivity Study in Diabetic Peripheral Neuropathy Patients

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