Quantification of EMG Signal By Wavelet Analysis

Abstract

Intramuscular Biomedical signals has been processed by using discrete dyadic wavelet transform, to identify the distinct events like positive sharp waves, fibrillation potential and motor unit action potentials(MUAPs). Wavelet, filter bank and multi-resolution scales are used and compare the wavelet transform with the more classical short-time Fourier transform approach to EMG signal analysis. The cubic B-spline wavelets and its first derivatives were used to convolute the signal. Wavelet maxima (WM) vectors are considered as detection parameter to identify the events. The maxima are always found to be the highest in the first scale, so it is considered to detect. PSWs and MUAPs are detected from EMG signal on the basis of their characteristics, through the MATLABGUI and the corresponding FP and PSW are being modified into different scales of dyadic wavelet transform which could be analyzed quantitatively or qualitatively or both. Normal and abnormal MUAPs are distinguished by their amplitude and subsequently identify type of neuromuscular disorder.