Title :
EMG signal processing and diagnostic of muscle diseases
Author :
Alim, O.A. ; Moselhy, Mohamed ; Mroueh, F.
Author_Institution :
Electr. & Comput. Eng. Dept., Beirut Arab Univ., Beirut, Lebanon
Abstract :
Real time recordings of motor unit action potential (MUAP) signals from myopathy (MYO), neuropathy (NEU), and normal (NOR) subjects, using intramuscular electromyography (needle EMG) are treated and processed in order to be classified for the diagnosis of neuromuscular pathology. Feedforward-backpropagation neural network is used for the classification. Recognition rates were found to be higher than 70% and higher when using time domain features as inputs for the neural network.
Keywords :
backpropagation; diseases; electromyography; feedforward; medical signal processing; neural nets; EMG signal processing; feed forward back propagation neural network; intramuscular electromyography; motor unit action potential; muscle disease diagnosis; myopathy subjects; needle EMG; neuromuscular pathology diagnosis; neuropathy subjects; real time MUAP recordings; recognition rate; Biological neural networks; Electromyography; Feature extraction; Frequency domain analysis; MATLAB; Support vector machine classification; Time domain analysis; Biomedical; EMG; Neural Network; Signal processing;
Conference_Titel :
Advances in Computational Tools for Engineering Applications (ACTEA), 2012 2nd International Conference on
Conference_Location :
Beirut
Print_ISBN :
978-1-4673-2488-5
DOI :
10.1109/ICTEA.2012.6462866
