Digital spectral analysis: with applications
Digital spectral analysis: with applications
A model for diagnosing and explaining multiple disorders
Computers and Biomedical Research
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
A two-stage method for MUAP classification based on EMG decomposition
Computers in Biology and Medicine
A novel method for automated EMG decomposition and MUAP classification
Artificial Intelligence in Medicine
High-precision EMG signal decomposition using communicationtechniques
IEEE Transactions on Signal Processing
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
SVM-based supervised and unsupervised classification schemes
WSEAS Transactions on Computers
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The shapes of motor unit action potentials (MUAPs) in an electromyographic (EMG) signal provide an important source of information for the diagnosis of neuromuscular disorders. In order to extract this information from the EMG signals recorded at low to moderate force levels, it is required to: i) identify the MUAPs composed by the EMG signal, ii) cluster the MUAPs with similar shapes, iii) extract the features of the MUAP clusters and iv) classify the MUAPs according to pathology. In this work, three techniques for segmentation of EMG signal are presented: i) segmentation by identifying the peaks of the MUAPs, ii) by finding the beginning extraction point (BEP) and ending extraction point (EEP) of MUAPs and iii) by using discrete wavelet transform (DWT). For the clustering of MUAPs, statistical pattern recognition technique based on euclidian distance is used. The autoregressive (AR) features of the clusters are computed and are given to a multi-class support vector machine (SVM) classifier for their classification. A total of 12 EMG signals obtained from 3 normal (NOR), 5 myopathic (MYO) and 4 motor neuron diseased (MND) subjects were analyzed. The success rate for the segmentation technique used peaks to extract MUAPs was highest (95.90%) and for the statistical pattern recognition technique was 93.13%. The classification accuracy of multi-class SVM with AR features was 100%.