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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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International Journal of Emerging Trends & Technology in Computer Science (IJETTCS)
ISSN 2278-6856
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