Application of a Wavelet Adaptive Filter Based on Neural Network to Minimize Distortion of the Pulsatile Spectrum

Abstract

The approach of the Dynamic Spectrum evaluates the pulsatile part of the entire optical signal at different wavelength. In the course of collecting the pulsatile spectrum signal in vivo, it is inevitable to be interfused with yawp signals as high frequency interference, baseline drift and so on. Using the traditional adaptive filter, it is very difficult to collect the reference signal from the in vivo experiment. In this paper, Daubechies wavelet adaptive filter based on Adaptive Linear Neuron networks is used to extract the signal of the pulse wave. Wavelet transform is a powerful tool to disclose transient information in signals. The wavelet used is adaptive because the parameters are variable, and the neural network based adaptive matched filtering has the capability to ”learn” and to become time-varying. So this filter estimates the deterministic signal and removes the uncorrelated noises with the deterministic signal. This method can get better result than nonparametric results. This filter is found to be very effective in detection of symptoms from pulsatile part of the entire optical signal.