Volume & Issue no: Volume 6, Issue 4, July - August 2017
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Title: |
Classification of Electromyography Signal for Identifying of Nerve Muscles Disorder using ANFIS |
Author Name: |
Prof. S.I Ali, Prof. S.M. Ali and Prof. S.A.Ali |
Abstract: |
Abstract
The Electromyography signal indicates the electrical activity
and comprehensive analysis of muscles. Electromyography uses
electrodes to measure the electrical activity of the heart.
Extracting Electromyography(EMG) signals is a noninvasive
process that opens the door to new possibilities for the
application of advanced signal processing and data analysis
techniques in the diagnosis of heart diseases. With the help of
today’s large database of Electromyography (EMG) signals, a
computationally intelligent system can learn and take the place
of a Neurologist. Detection of various abnormalities in the
patient’s Nerve to identify various Neuromuscular Disorder
can be made through an Adaptive Neuro-Fuzzy Inference
System (ANFIS) preprocessed by subtractive clustering. ANFIS
combines both neural networks and fuzzy logic principles; it
can capture the benefits of both in a single framework. Various
types of neuromuscular disorders are classified: Amyotrophic
lateral sclerosis (ALS),Charcot-Marie-Tooth disease,
Multiplesclerosis, Muscular dystrophy, Myasthenia gravis,
Myopathy, Myositis, including polymyositis and
dermatomyositis, Peripheral neuropathy. The goal is to detect
important characteristics of an EMG signal to determine if the
patient’s Neuromuscular is normal or irregular.
Keywords: ANFIS, Neuromascular Disorder, EMG,Signal
Processing. |
Cite this article: |
Prof. S.I Ali, Prof. S.M. Ali and Prof. S.A.Ali , "
Classification of Electromyography Signal for Identifying of Nerve Muscles Disorder using ANFIS" , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) ,
Volume 6, Issue 4, July - August 2017 , pp.
133-137 , ISSN 2278-6856.
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