Classification and Diagnosis of Myopathy from EMG Signals*

Abstract

We present a methodology to predict the presence of myopathy (muscle disease) from intramuscular electromyography (EMG) signals. By evaluating the shape and frequency of electrical action potentials produced by muscular fibers and captured in EMG measurements, a physician can often detect both the presence and the severity of such disorders. However, EMG measurements can vary significantly across different subjects, different muscles, and according to session-specific characteristics such as muscle fatigue and degree of contraction. By considering fixed-duration (0.5-2 sec) frequency-domain samples of diagnostic regions in EMG signals measured at full muscle contraction, we can automatically detect the presence of myopathies across different subjects and muscles with ~90% accuracy. We argue that our methodology is more generally applicable than existing methods that depend upon accurate segmentation of individual motor unit action potential (MUAP) waveforms. We present a rigorous evaluation of our technique across several different subjects and muscles.

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Cite this paper

@inproceedings{Bue2013ClassificationAD, title={Classification and Diagnosis of Myopathy from EMG Signals*}, author={Brian D. Bue and Erzs{\'e}bet Mer{\'e}nyi and James M. Killian}, year={2013} }