Analysis of DWT signal denoising on various biomedical signals by neural network

A detailed analysis of Discrete Wavelet Transform (DWT) denoising and identification on various wavelet families and biomedical signals (ECG, EEG and EMG) is presented in this paper. The main intention of this work is to explore the wavelet function which is optimal for denoising the signals. Nevertheless, wavelet transforms offer better results for denoising biomedical signals, but identification is a crucial process. This paper proposes an artificial neural network in which the wavelet types are used to denoise the signals optimally by using a learning back propagation algorithm. Also the, performances of the various wavelet types are tabulated and compared with the existing techniques, in terms of the evaluation parameters signal to noise ratio, percent root mean square difference, mean-square error and compression ratio. The simulation results expose the efficiency of the proposed method for the denoising of biomedical signals.