Wavelet analysis of motor unit action potentials

In this study the usefulness of the wavelet transforms (WT) Daubechies with 4 and 20 coefficients, Chui, and Battle-Lemarie in analyzing MUAPs recorded from normal subjects and subjects suffering with motor neuron disease and myopathy was investigated. The results of this study are summarised as follows: (i) The orthogonal WT decomposes the MUAP signal into a set of orthogonal basis functions where each coefficient represents an entirely different signal feature descibing the energy content in the given time-frequency window. Most of the energy of the MUAP signal is distributed among a small number of well-localized (in time) WT coefficients in the region of the main spike. (ii) The WT uses long duration windows for low frequencies, and short duration windows for high frequencies. For MUAP signals, this means that we to look to the low frequency coefficients for capturing the average behaviour of the MUAP signal over long durations, and we look to the low frequency coefficients for locating MUAP spike changes. (iii) In the case of the Daubechies 4 wavelet an extremely high time-resolution of only four signal samples is provided tracking effectively the transient components of the MUAP signal. (iv) Finally, it is shown that the diagnostic performance of neural network models trained with the Battle-Lemarie wavelet feature set is similar to the empirically determined time domain feature set.