@article{MAKHILLRJAS2014959378,
    title = {Biomedical Signals Analysis by DWT Signal De-Noising with Neural Networks},
    journal = {Research Journal of Applied Sciences},
    volume = {9},
    number = {5},
    pages = {244-256},
    year = {2014},
    issn = {1815-932x},
    doi = {rjasci.2014.244.256},
    url = {https://makhillpublications.co/view-article.php?issn=1815-932x&doi=rjasci.2014.244.256},
    author = {Geeta,H.P. and},
    keywords = {DWT,ECG,EEG,EMG,neural network,wavelet frequency thresholding},
    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.}
    }