Classification of electromyographic signals by autoregressive modeling

The classification of a set of intramuscular electromyographic (EMG) signals collected from normal, neuropathic, and myopathic patient groups is discussed. The signal is recorded in real time for 2 or 3 s, during which the patient performs a continuous ramp contraction. The time-varying dynamic nature of the neuromuscular system was observed by autoregressive (AR) modeling of the running windowed data segments. The 0.2-s length window runs along the entire data length of 1.6 seconds. The time varying nature of the model coefficients and the prediction error variance was investigated. The prediction error variance parameter is found to have significant time-varying characteristics. A first-order regression model is used to quantify the trend of this parameter. The probability density functions are estimated for the regression model parameters, and results of classifications for various pathologic classes are presented