Spherical classification of wavelet transformed EMG intensity patterns

Abstract

Electromyograms of different muscles can be submitted to a wavelet-transform and arranged in a Multi-Muscle Pattern (MMP). The MMP represents the intensity of the EMG signals of a number of muscles simultaneously in time/frequency space. As previously shown, the MMPs can be represented by points in an Euclidian vector space that was called pattern space. The variability of the MMPs is represented by the distribution of the scattered points in pattern space. The purpose of this study was to investigate the distribution of the points and use the properties of the distribution to classify MMPs. The first task was to test whether the points representing a group of MMPs were located between the inner and outer boundary of a sphere-like domain in whitened pattern space as theoretically predicted. The mean of these points and thus of the MMPs is represented by a point at the center of the sphere. The hypothesis was that the spheres representing points of the MMPs of barefoot and shod runners were sufficiently separated and distinguishable in pattern space to allow classification of the runners according to their shod condition. The results confirmed the hypothesis and revealed that the recognition rate was over 80%. One can conclude and generalize that the points representing MMPs recorded for a certain condition reside between the inner and outer boundary of the sphere. The classification based on the spherical feature represents a much better discrimination than one based on the distance from the mean.