EMG signal processing and diagnostic of muscle diseases

EMG signal processing and diagnostic of muscle diseases

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-backpro...

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Bibliographische Detailangaben
Hauptverfasser: Alim, O. A., Moselhy, M., Mroueh, F.
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Biological neural networks
Biomedical
Electromyography
EMG
Feature extraction
Frequency domain analysis
MATLAB
Neural Network
Signal processing
Support vector machine classification
Time domain analysis
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Beschreibung
Zusammenfassung: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.
DOI:10.1109/ICTEA.2012.6462866