Chaotic classification of electromyographic (EMG) signals via correlation dimension measurement

A set of intramuscular electromyographic signals were collected from various patient groups during ramp muscle contraction. The signals were collected using a real-time data acquisition system. The signals were tested for their chaotic behavior using spectral analysis and Poincare map techniques. MATLAB based software tools were developed to compute and plot the correlation function for each data set to determine the time lag for the first zero crossing. This time lag was used to create the signals state-space model. A two-dimensional state-space was created and plotted one state versus the other to observe the Poincare map. A correlation integral was computed for each state-space data set, and the correlation dimension values were then calculated by differentiating these correlation integral signals for each data set. The correlation dimension values were found to be different for different patient groups. The results show promise for online classification of neuromuscular patient groups