Robust Classification of Intramuscular EMG Signals to Aid the Diagnosis of Neuromuscular Disorders (original) (raw)

Techniques of EMG signal analysis and classification of Neuromuscular diseases

Artificial intelligence techniques are being used effectively in medical diagnostic tools to increase the diagnostic accuracy and provide additional knowledge. Electromyography (EMG) signals are becoming increasingly important in clinical and biomedical applications. Detection, processing and classification of EMG signals are very desirable because it allows a more standardized evaluation to discriminate between different neuromus-cular diseases. This paper reviews a brief explanation of the different features extraction and classification techniques for classifying EMG signals used in literatures. Wavelet Transform (WT), Principle Component Analysis (PCA), and Independent Component Analysis (ICA) are different feature extraction techniques. Literature presents different techniques to classify EMG data such as probabilistic neural network (PNN), Support Vector Machine (SVM), Artificial Neural Networks (ANN), etc. In this paper neuromuscular disease classification from electromyography (EMG) signals are proposed based on different combination of features extraction methods and types of classifiers. Combination of WT and SVM improved the classification accuracy than other combinations such as DWT with ANN, ICA with MLPN, PCA with ANN and DWT with PNN. Keywords: Electromyogram (EMG), Wavelet Transform (WT), Principle Component Analysis (PCA), Support Vector Machine (SVM), Multilayer Perceptron Neural Network (MLPNN), Probabilistic Neural Network (PNN).

Classification of Normal and Myopathy EMG Signals using BP Neural Network

International Journal of Computer Applications, 2013

Electromyography (EMG) signal is the muscle electrical activity. Electromyography is a technique for detecting and recording the electrical potential generated by muscle cells. This EMG signals are used in medical professionals to determine specific disorders. This paper basically deals with the analysis of different electromyography signals (NOR & MYO). In this paper, new method for classification of myopathy patient's and healthy subjects with the help of EMG signal by using back propagation neural network classifier are proposed. This methodology provided 96.75 % accuracy in classification of Myopathy and normal EMG signals.

Automatic Classification of Intramuscular EMG to Recognize Pathologies

2019

This paper proposes to assess the relevance of new automated tools for electromyography (EMG) analysis, in order to differentiate neuropathic from myopathic patterns. The challenge is to define the diagnosis with only one iEMG signal per patient. Our proposed method uses the decomposition of the EMG signal to characterize motor unit action potentials (MUAPs). The decomposition of each iEMG signal is carried out with EMGLAB. For each signal, the decomposition provides a code which is used by the automated classification algorithms.We use here the linear Support Vector Machine (SVM) and the Bagging Trees methods. For the learning process we use several EMG signals and in different parts of the muscle. Only one recorded electromyography EMG signal per subject is used for the diagnostic test. We evaluate the k—fold cross-validation and the confusion matrix for both models. The accuracy is 77.3% for the SVM and 68.2% for the Bagging Trees. These are the first developments of this tool to...

Emg Based Diagnosis of Myopathy and Neuropathy Using Machine Learning Techniques

2020

Myopathy and Neuropathy are nonprogressive 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 twochannel 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 promine...

Classification of EMG Signals for Assessment of Neuromuscular Disorders

International Journal of Electronics and Electrical Engineering, 2014

An accurate and computationally efficient means of feature extraction of electromyographic (EMG) signal patterns has been the subject of considerable research effort in recent years. Quantitative analysis of EMG signals provides an important source of information for the classification of neuromuscular disorders. The objective of this study is to discriminate between normal (NOR), myopathic (MYO) and neuropathic (NEURO) subjects. The experiment consisted of 22 pathogenic (11 MYO and 11 NEURO) and 12 healthy persons. The signals were recorded at 30% Maximum Voluntary Contraction (MVC) for 5 seconds. Features of MUAPs extracted in time have been quantitatively analysed. We have used binary SVM for classification. Separation of normal subjects from neuromuscular disease subjects has an accuracy of 83.45%, whereas separation of subjects from the two types of subjects (myopathic and neuropathic) has an accuracy of 68.29% which is again high. 

Characterizing EMG Data using Machine- Learning Tools

Comput Biol Med. , 2014

Effective electromyographic (EMG) signal characterization is critical in the diagnosis of neuromuscular disorders. Machine-learning based pattern classification algorithms are commonly used to produce such characterizations. Several classifiers have been investigated to develop accurate and computationally efficient strategies for EMG signal characterization. This paper provides a critical review of some of the classification methodologies used in EMG characterization, and presents the state-of-the-art accomplishments in this field, emphasizing neuromuscular pathology. The techniques studied are grouped by their methodology, and a summary of the salient findings associated with each method is presented.

Classification_of_EMG_signals_using_wavelet_neural_network

An accurate and computationally efficient means of classifying electromyographic (EMG) signal patterns has been the subject of considerable research effort in recent years. Quantitative analysis of EMG signals provides an important source of information for the diagnosis of neuromuscular disorders. Following the recent development of computer-aided EMG equipment, different methodologies in the time domain and frequency domain have been followed for quantitative analysis. In this study, feedforward error backpropagation artificial neural networks (FEBANN) and wavelet neural networks (WNN) based classifiers were developed and compared in relation to their accuracy in classification of EMG signals. In these methods, we used an autoregressive (AR) model of EMG signals as an input to classification system. A total of 1200 MUPs obtained from 7 normal subjects, 7 subjects suffering from myopathy and 13 subjects suffering from neurogenic disease were analyzed. The success rate for the WNN technique was 90.7% and for the FEBANN technique 88%. The comparisons between the developed classifiers were primarily based on a number of scalar performance measures pertaining to the classification. The WNN-based classifier outperformed the FEBANN counterpart. The proposed WNN classification may support expert decisions and add weight to EMG differential diagnosis.

Neural network models in EMG diagnosis

1995

Abstract In previous years, several computer-aided quantitative motor unit action potential (MUAP) techniques were reported. It is now possible to add to these techniques the capability of automated medical diagnosis so that all data can be processed in an integrated environment.

A Review on EMG Signal Classification for neurological disorder using neural network

2013

Specialists diagnose the neuromuscular diseases using visual inspection of the recorded Electromyogram (EMG) of the patients and compare their shape and key points to the standard ones. This paper represent the review on implementation of EMG classification of recorded signals from bicep muscles. The signals are collected from various patient group like normal, mayopathic and nueropathic during 25%, 50% and 75% muscles contraction. This recorded signals is then processed to extract some predefined features as input to the nueral network. The time and frequency based extracted features are use to train the nueral network. The trained nueral network will classify these signals using decompositioning the EMG signals.

ONLINE EMG SIGNAL ANALYSIS FOR DIAGNOSIS OF NEUROMUSCULAR DISEASES BY USING PCA AND PNN

The surface electromyography (sEMG) signal is a biomedical signal that measures electrical currents generated in muscles during its contraction representing neuromuscular activities. The proposed method is a noninvasive technique used for diagnosis of different neuromuscular diseases by two different pattern recognition techniques. Principal Component Analysis (PCA) technique is adopted for features extraction and Probabilistic neural network (PNN) technique is used to classify the sEMG signal. The bioelectrical signals were processed by using LabVIEW and MATLAB.