Abstract
Electromyographic (EMG) signal provide a significant source of information for diagnosis, treatment and management of neuromuscular disorders. This paper is aim at introducing an effective multi-classifier approach to enhance classification accuracy .The proposed system employs both time domain and time-frequency domain features of motor unit action potentials (MUAPs) extracted from an EMG signal. Different classification strategies including single classifier and multiple classifiers with time domain and time frequency domain features were investigated. Support Vector Machine (SVM) and K-nearest neighborhood (KNN) classifier used predict class label (Myopathic, Neuropathic, or Normal) for a given MUAP. Extensive analysis is performed on clinical EMG database for the classification of neuromuscular diseases and it is found that the proposed methods provide a very satisfactory performance in terms overall classification accuracy.
Highlights
Electromyographic (EMG) signal analysis plays a major role in the diagnosis of neuromuscular diseases, such as amyotrophic lateral sclerosis (ALS) and myopathy
Each group consist of four Support Vector Machine (SVM) classifier as base classifier, two scheme is employed for class discrimination one against one (OAO) and one against all (OAA) given in table 1, [29], [30]
The proposed multi-classifier model provides average accuracy 97% for time-frequency feature and WKNN Classifier achieved classification accuracy 95%. Both the models were tested on data of 150 EMG signal,50 sample of each class The segmentation of EMG is carried by remove inactive region around base line and use of window function around peak gives simple approach for MAUPS extraction
Summary
Electromyographic (EMG) signal analysis plays a major role in the diagnosis of neuromuscular diseases, such as amyotrophic lateral sclerosis (ALS) and myopathy. The methods reported in [1], [11] used wavelet-domain features extracted through multi-level decomposition using a filter bank structure consisting of only the analysis bank with Daubechies 4 wavelet filters, and several time domain features are used, such as zero crossing rate, turns-amplitude ratio, root-meansquare (RMS) value and autoregressive (AR) coefficients [13], [14]. Several classification methods such as fusion classifier, multi-classifier, an SVM that provides such probabilities for each class is reported in [1], [16].
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