Discrimination of Myopathy, Neuropathy and Healthy EMG Signals

Singh, Amit Kumar; Agrawal, Niruj; Gupta, Sumit

doi:10.17148/ijarcce.2017.6504

Cited by 2 publications

(2 citation statements)

References 12 publications

(12 reference statements)

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“…The analysis focus on classification of neuromuscular disease based on EMG signals on various combinations of feature extracted techniques. Amit kumar singh et al [3], discriminates the neuromuscular disorder EMG signals by decomposing the signals using Empirical Mode Decomposition (EMD) wherein the features namely mean, standard deviation, variance and entropy were obtained. Swaroop.…”

Section: Related Workmentioning

confidence: 99%

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Emg Based Diagnosis of Myopathy and Neuropathy Using Machine Learning Techniques

D¹,

Arulanand²

2020

IJETMS

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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.

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Section: Related Workmentioning

confidence: 99%

“…Myopathy is a non-progressive neuromuscular disorder which affects the muscle cells. Neuropathy is a progressive neuromuscular disorder which affects the nerve cells [3]. Integration of Machine Learning algorithms with EMG signals improves the speed and accuracy of diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Emg Based Diagnosis of Myopathy and Neuropathy Using Machine Learning Techniques

D¹,

Arulanand²

2020

IJETMS

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Prediction of biomedical signals using deep learning techniques

Kalaivani

Kshirsagarr

Devi³

et al. 2023

IFS

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The electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG) are all very useful diagnostic techniques. The widespread availability of mobile devices plus the declining cost of ECG, EEG, and EMG sensors provide a unique opportunity for making this kind of study widely available. The fundamental need for enhancing a country’s healthcare industry is the ability to foresee the plethora of ailments with which people are now being diagnosed. It’s no exaggeration to say that heart disease is one of the leading causes of mortality and disability in the world today. Diagnosing heart disease is a difficult process that calls for much training and expertise. Electrocardiogram (ECG) signal is an electrical signal produced by the human heart and used to detect the human heartbeat. Emotions are not simple phenomena, yet they do have a major impact on the standard of living. All of these mental processes including drive, perception, cognition, creativity, focus, attention, learning, and decision making are greatly influenced by emotional states. Electroencephalogram (EEG) signals react instantly and are more responsive to changes in emotional states than peripheral neurophysiological signals. As a result, EEG readings may disclose crucial aspects of a person’s emotional states. The signals generated by electromyography (EMG) are gaining prominence in both clinical and biological settings. Differentiating between neuromuscular illnesses requires a reliable method of detection, processing, and classification of EMG data. This study investigates potential deep learning applications by constructing a framework to improve the prediction of cardiac-related diseases using electrocardiogram (ECG) data, furnishing an algorithmic model for sentiment classification utilizing EEG data, and forecasting neuromuscular disease classification utilizing EMG signals.

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Discrimination of Myopathy, Neuropathy and Healthy EMG Signals

Cited by 2 publications

References 12 publications

Emg Based Diagnosis of Myopathy and Neuropathy Using Machine Learning Techniques

Emg Based Diagnosis of Myopathy and Neuropathy Using Machine Learning Techniques

Prediction of biomedical signals using deep learning techniques

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