Abstract:

In order to overcome the oscillation caused by hard threshold wavelet filtering and the waveform distortion brought by soft threshold wavelet filtering, a wavelet threshold de-noising method based on genetic optimization function curve named GOCWT was proposed. In the GOCWT, a quadratic function was used to simulate the optimal threshold function curve. The Root Mean Square Error (RMSE) and smoothness of the reconstructed signal were applied to design the fitness function. Furthermore, the Genetic Algorithm (GA) was utilized to optimize the parameters of the new thresholding function. Through the analysis of 48 segments of ECG signals, it was found that the new method resulted in a 36% increase of smoothness value comparing to the hard threshold method, and a 32% decrease of RMSE value comparing to the soft threshold method. The results show that the proposed algorithm outperforms hard threshold wavelet filtering and soft threshold wavelet filtering, it can not only avoid the undesirable oscillation phenomenon of the filtered signal, but also reserve the minute features of the signal including peak value.