Title :
Wavelet analysis of motor unit action potentials
Author :
Pattichis, C.S. ; Pattichis, M.S. ; Schizas, C.N.
Author_Institution :
Dept. of Comput. Sci., Univ. of Cyprus, Nicosia, Cyprus
fDate :
31 Oct-3 Nov 1996
Abstract :
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 describing 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 the authors to look to the low frequency coefficients for capturing the average behaviour of the MUAP signal over long durations, and the authors 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
Keywords :
electromyography; medical signal processing; wavelet transforms; Battle-Lemarie wavelet feature set; Daubechies; diagnostic performance; electrodiagnostics; empirically determined time domain feature set; low frequency coefficients; main spike; motor neuron disease; motor unit action potentials; myopathy; neural network models; normal subjects; orthogonal basis functions set; orthogonal transform; signal energy; time-frequency window; wavelet analysis; Computer science; Diseases; Electromyography; Low pass filters; Machine vision; Neurons; Time frequency analysis; Wavelet analysis; Wavelet domain; Wavelet transforms;
Conference_Titel :
Engineering in Medicine and Biology Society, 1996. Bridging Disciplines for Biomedicine. Proceedings of the 18th Annual International Conference of the IEEE
Conference_Location :
Amsterdam
Print_ISBN :
0-7803-3811-1
DOI :
10.1109/IEMBS.1996.647519