Classification of myopathy and neuropathy EMG signals using neural network

Swaroop, R.; Kaur, Maninder; Suresh, Padma; Sadhu, Pradip Kumar

doi:10.1109/iccpct.2017.8074330

Cited by 10 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recognition of various gait activities based on the sEMG signal of the lower limb has an important role in controlling the exoskeleton for the knee abnormal person or in the prosthesis control for the lower limb amputee. Neural network based myopathy and neuropathy classification using sEMG signal was proposed in [13]. Kugler et al have recognized Parkinson's disease using sEMG signal [14].…”

mentioning

confidence: 99%

A Hybrid WD-EEMD sEMG Feature Extraction Technique for Lower Limb Activity Recognition

Vijayvargiya

Gupta²,

Kumar

et al. 2021

IEEE Sensors J.

View full text Add to dashboard Cite

Classification and analysis of surface EMG (sEMG) signals have been of particular interest due to their numerous applications in the biomedical field. They can be used for the diagnosis of neuromuscular diseases, kinesiological studies, and human-machine interaction. However, these signals are difficult to process due to their noisy nature. To overcome this problem, a hybrid of wavelet with ensemble empirical mode decomposition pre-processing technique called WD-EEMD is proposed for classifying lower limb activities based on sEMG signals in healthy and knee abnormal subjects. First, Wavelet De-noising is used for filtering out white Gaussian Noise (WGN) and unwanted signals (contribution of other muscle signals). Next, an Ensemble Empirical Mode Decomposition is used for filtering out power line interference (PLI) and baseline wandering (BW) noises, followed by extraction of a total of nine time-domain features. Finally, the performance parameters of the Linear Discriminant Analysis (LDA) classifier are calculated with a 3-fold cross-validation technique. This study involves 11 healthy and 11 individuals with a knee abnormality for three different activities: walking, flexion of the leg up (standing), and leg extension from sitting position (sitting). Different pre-processing techniques similar to that of WD-EEMD were compared. It was observed that the proposed method achieves an average classification accuracy of 90.69% and 97.45% for healthy subjects and knee abnormal subjects, respectively.

show abstract

mentioning

confidence: 99%

A Hybrid WD-EEMD sEMG Feature Extraction Technique for Lower Limb Activity Recognition

Vijayvargiya

Gupta²,

Kumar

et al. 2021

IEEE Sensors J.

View full text Add to dashboard Cite

show abstract

“…Swaroop. R et al in this paper [5] has presented a back-propagation algorithm which classify the healthy, myopathy and neuropathy EMG signals. I. Elamvazhuthi et al [15] presents a classification of neuromuscular disorders using Artificial Neural Network (ANN) based on the features known as Auto Regressive (AR), Root Mean Square (RMS), Zero Crossing (ZC), Mean Absolute Value (MAV) and Waveform Length (WL).…”

Section: Related Workmentioning

confidence: 99%

Emg Based Diagnosis of Myopathy and Neuropathy Using Machine Learning Techniques

D¹,

Arulanand²

2020

IJETMS

View full text Add to dashboard Cite

Myopathy and Neuropathy are non-progressive and progressive neuromuscular disorders which weakens the muscles and nerves respectively. Electromyography (EMG) signals are bio signals obtained from the individual muscle cells. EMG based diagnosis for neuromuscular disorders is a safe and reliable method. Integrating the EMG signals with machine learning techniques improves the diagnostic accuracy. The proposed system performs analysis on the clinical raw EMG dataset which is obtained from the publicly available PhysioNet database. The two-channel raw EMG dataset of healthy, myopathy and neuropathy subjects are divided into samples. The Time Domain (TD) features are extracted from divided samples of each subject. The extracted features are annotated with the class label representing the state of the individual. The annotated features split into training and testing set in the standard ratio 70: 30. The comparative classification analysis on the complete annotated features set and prominent features set procured using Pearson correlation technique is performed. The features are scaled using standard scaler technique. The analysis on scaled annotated features set and scaled prominent features set is also implemented. The hyperparameter space of the classifiers are given by trial and error method. The hyperparameters of the classifiers are tuned using Bayesian optimization technique and the optimal parameters are obtained. and are fed to the tuned classifier. The classification algorithms considered in the analysis are Random Forest and Multi-Layer Perceptron Neural Network (MLPNN). The performance evaluation of the classifiers on the test data is computed using the Accuracy, Confusion Matrix, F1 Score, Precision and Recall metrics. The evaluation results of the classifiers states that Random Forest performs better than MLPNN wherein it provides an accuracy of 96 % with non-scaled Time Domain (TD) features and MLPNN outperforms better than Random Forest with an accuracy of 97% on scaled Time Domain (TD) features which is higher than the existing systems. The inferences from the evaluation results is that Bayesian optimization tuned classifiers improves the accuracy which provides a robust diagnostic model for neuromuscular disorder diagnosis.

show abstract

“…Bonato et al studied the effect of exhaustion of hamstring muscles and quadriceps on sEMG activity [27]. According to Swaroop et al, myopathy and neuropathy can be classified through sEMG signals using a three-layered neural network with backpropagation [28]. In a study by Kugler et al [29], EMG data was used for the identification of rare Parkinson's disease via SVM-based methods.…”

Section: Introductionmentioning

confidence: 99%

S-WD-EEMD: A hybrid framework for imbalanced sEMG signal analysis in diagnosis of human knee abnormality

Vijayvargiya,

Sinha,

Gehlot

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

The diagnosis of human knee abnormalities using the surface electromyography (sEMG) signal obtained from lower limb muscles with machine learning is a major problem due to the noisy nature of the sEMG signal and the imbalance in data corresponding to healthy and knee abnormal subjects. To address this challenge, a combination of wavelet decomposition (WD) with ensemble empirical mode decomposition (EEMD) and the Synthetic Minority Oversampling Technique (S-WD-EEMD) is proposed. In this study, a hybrid WD-EEMD is considered for the minimization of noises produced in the sEMG signal during the collection, while the Synthetic Minority Oversampling Technique (SMOTE) is considered to balance the data by increasing the minority class samples during the training of machine learning techniques. The findings indicate that the hybrid WD-EEMD with SMOTE oversampling technique enhances the efficacy of the examined classifiers when employed on the imbalanced sEMG data. The F-Score of the Extra Tree Classifier, when utilizing WD-EEMD signal processing with SMOTE oversampling, is 98.4%, whereas, without the SMOTE oversampling technique, it is 95.1%.

show abstract

Classification of myopathy and neuropathy EMG signals using neural network

Cited by 10 publications

References 5 publications

A Hybrid WD-EEMD sEMG Feature Extraction Technique for Lower Limb Activity Recognition

A Hybrid WD-EEMD sEMG Feature Extraction Technique for Lower Limb Activity Recognition

Emg Based Diagnosis of Myopathy and Neuropathy Using Machine Learning Techniques

S-WD-EEMD: A hybrid framework for imbalanced sEMG signal analysis in diagnosis of human knee abnormality

Contact Info

Product

Resources

About