Automatic Classification of Intramuscular EMG to Recognize Pathologies

This paper proposes to assess the relevance of new automated tools for electromyography (EMG) analysis, in order to differentiate neuropathic from myo-pathic patterns. The challenge is to define the diagnosis with only one iEMG signal per patient. Our proposed method uses the decomposition of the EMG signal to characterize motor unit action potentials (MUAPs). The decomposition of each iEMG signal is carried out with EMGLAB. For each signal, the decomposition provides a code which is used by the automated classification algorithms. We use here the linear Support Vector Machine (SVM) and the Bagging Trees methods. For the learning process we use several EMG signals and in different parts of the muscle. Only one recorded electromyography EMG signal per subject is used for the diagnostic test. We evaluate the k − f old cross-validation and the confusion matrix for both models. The accuracy is 77.3% for the SVM and 68.2% for the Bagging Trees. These are the first developments of this tool to make it useful for clinical practice.