Authors : Geeta Kaushik, H.P. Sinha and Lillie Dewan
Abstract: The core intention of this research is to investigate the wavelet function that is optimum in identifying and de-noising the various biomedical signals. Using traditional methods, it is difficult to recover the noises present in the signals. This study presents a detail analysis of Discrete Wavelet Transform (DWT) de-noising on various wavelet families and biomedical signals such as ECG, EMG and EEG. Researchers have developed a trained network in order to optimally denoise the signals by using a Back Propagation algorithm in the neural network. Initially noise is added to the original signal then the signal is decomposed using the Shift Invariant Method. After decomposition, the proposed Wavelet Based Method is used for noise removal. Then, the signal is reconstructed by using Wavelet Reconstruction Method. The denoised signals will be compressed by a hybrid wavelet shannon fano coding for reducing its storage size.
Geeta Kaushik, H.P. Sinha and Lillie Dewan, 2014. Biomedical Signals Analysis by DWT Signal De-Noising with Neural Networks. Research Journal of Applied Sciences, 9: 244-256.