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.

摘要：

针对小波阈值滤波方法中硬阈值方法易产生震荡和软阈值方法易产生波形失真的缺点，提出了一种基于遗传优化函数曲线的小波阈值法GOCWT。该方法利用二次函数模拟阈值转换函数曲线，并根据均方根误差(RMSE)与平滑度建立适应度函数，运用遗传算法(GA)对转换函数参数进行寻优。通过对48段心电信号滤波性能指标分析发现：与硬阈值滤波方法相比，GOCWT的平滑度性能提升了36%；与软阈值滤波方法相比，其均方根误差性能提升了32%。实验结果表明，GOCWT的滤波性能优于硬、软阈值滤波方法，既避免了心电信号滤波时产生的震荡现象，同时又很好地保留了信号的峰值等细节特征。