The aim of this experiment was to implement any Signal Processing operation on one dimensional Signal.
We formed a group of 5 students. The members were Nikita Pagar, Golappagouda Patil, Anushree Mhatre, Apoorva Raut and Sahil Rai. The topic we selected was “Detection and Processing of Electromyography signals”.
Title : EMG SIGNAL DENOISING VIA BAYESIAN WAVELET SHRINKAGE BASED ON GARCH
MODELING
Inventors : Maryam Amirmazlaghani, Hamidreza Amindavar
Description: Electromyography(EMG) is a biomedical signal which shows the muscle response to neural stimulation.It is very difficult to obtain high quality EMG signal from EMG sources as it has a very low amplitude and are very easily corrupted by noise. Hence it is necessary to filter out the noise before storing the signal digitally or before processing it.
This paper mainly concentrates on this issue of filtering noise out of an EMG signal using GARCH(Generalized Autoregressive Conditional Heteroscedasticity) modelling.
There has been considerable interest in using the wavelet transform as a powerful tool for processing EMG signals. In general, wavelet denoising procedures consist of three main steps:
First, Calculate the wavelet transform of the noisy EMG signal.
Second, Manipulate the wavelet coefficients.
Third, Compute the inverse transform using the modified coefficients.
As EMG are extremely weak signals, the general approach of processing gives large errors. Manipulation of wavelet co-efficients so as to reduce noise helps in obtaining better results
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