EMG Diagnosis using Neural Network Classifier
with Time Domain and AR Features
D. Er. Gurmanik Kaur, and D. Jain. International Journal on Electrical and Power Engineering, 1 (3):
5(December 2010)
Abstract
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. To extract this
information from the EMG signals, the first step is
identification of the MUAPs composed by the EMG
signal, second step is clustering of MUAPs with similar
shapes, third step is extraction of the features of MUAP
clusters and last step is classification of MUAPs. In this
work, the MUAPs are identified by using a data driven
segmentation algorithm, statistical pattern recognition
technique is used for clustering of MUAPs. Followed by
the extraction of time domain and autoregressive (AR)
features of the MUAP clusters. Finally, a neural
network (NN) classifier is used for classification of
MUAPs. 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 is 95.90%
and for the statistical technique is 93.13%. The
classification accuracy of NN is 66.72% with time
domain parameters and 75.06 % with AR parameters
%0 Journal Article
%1 ergurmanikkaur2010diagnosis
%A Er. Gurmanik Kaur, Dr. A S Arora
%A Jain, Dr. V K
%D 2010
%E Das, Dr. Vinu V
%J International Journal on Electrical and Power Engineering
%K Electromyography classification neural_networks
%N 3
%P 5
%T EMG Diagnosis using Neural Network Classifier
with Time Domain and AR Features
%U http://doi.searchdl.org/01.IJEPE.1.3.70
%V 1
%X 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. To extract this
information from the EMG signals, the first step is
identification of the MUAPs composed by the EMG
signal, second step is clustering of MUAPs with similar
shapes, third step is extraction of the features of MUAP
clusters and last step is classification of MUAPs. In this
work, the MUAPs are identified by using a data driven
segmentation algorithm, statistical pattern recognition
technique is used for clustering of MUAPs. Followed by
the extraction of time domain and autoregressive (AR)
features of the MUAP clusters. Finally, a neural
network (NN) classifier is used for classification of
MUAPs. 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 is 95.90%
and for the statistical technique is 93.13%. The
classification accuracy of NN is 66.72% with time
domain parameters and 75.06 % with AR parameters
@article{ergurmanikkaur2010diagnosis,
abstract = {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. To extract this
information from the EMG signals, the first step is
identification of the MUAPs composed by the EMG
signal, second step is clustering of MUAPs with similar
shapes, third step is extraction of the features of MUAP
clusters and last step is classification of MUAPs. In this
work, the MUAPs are identified by using a data driven
segmentation algorithm, statistical pattern recognition
technique is used for clustering of MUAPs. Followed by
the extraction of time domain and autoregressive (AR)
features of the MUAP clusters. Finally, a neural
network (NN) classifier is used for classification of
MUAPs. 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 is 95.90%
and for the statistical technique is 93.13%. The
classification accuracy of NN is 66.72% with time
domain parameters and 75.06 % with AR parameters},
added-at = {2012-10-01T08:55:39.000+0200},
author = {Er. Gurmanik Kaur, Dr. A S Arora and Jain, Dr. V K},
biburl = {https://www.bibsonomy.org/bibtex/2cb65e4e943eaabad1e5f68a09ad42dd3/ideseditor},
editor = {Das, Dr. Vinu V},
interhash = {7e99d9af6e1fbe24298ebc2d5d231eda},
intrahash = {cb65e4e943eaabad1e5f68a09ad42dd3},
journal = {International Journal on Electrical and Power Engineering},
keywords = {Electromyography classification neural_networks},
month = {December},
number = 3,
pages = 5,
timestamp = {2012-10-01T08:55:39.000+0200},
title = {EMG Diagnosis using Neural Network Classifier
with Time Domain and AR Features},
url = {http://doi.searchdl.org/01.IJEPE.1.3.70},
volume = 1,
year = 2010
}