Analysis Of Electromyography Signals Using Multi-Resolution Wavelet And Fourier Transform Based Biomedical Signal Processing Techniques

Author's Name: Shammi Farhana Islam
Subject Area: Health Science
Subject Other
Section Research Paper


Electromyography, Daubechies wavelet(db5), neuropathic, myopathic , Wavelet and Fourier Transform


In this paper, a comprehensive study has been made on applicability of wavelet transformed aided biological signal processing technique using fifth ordered Daubechies wavelet(db5) on electroencephalography (EEG) signals collected from three different categorized persons( normal person, neuropathic and myopathic patients). These three persons are well discriminated based on the characteristic features of the reconstructed signals at five different levels in discrete wavelet analysis. It is noticeable from the decomposed wavelets and reconstructed signals at five levels that the firing rate linking with the contractile activity of individual or a small group of muscle fibers in case of a myopathic patient is comparatively high due to muscular damage as compared to normal healthy person. In case of a neuropathic patient, the wavelet decomposed and reconstructed signals are characterized by the presence of spikes in irregular fashion with irregular firing pattern due to muscular clustering. In perspective of EMG signal amplitude, it is found that the signal amplitude is the highest for neuropathic patient as compared to others due to significant force produced by the muscle. The EMG signals have also been analyzed with classical Fourier Transform technique.

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