基于改进小波阈值函数的癫痫信号去噪算法

An Epileptic Signal Denoising Algorithm Based on Improved Wavelet Threshold Function

针对癫痫信号中存在的各类伪差信号,论文构造并采用了一种改进小波阈值函数。通过调整改进阈值函数当中的参数,实现在去除高频信号部分噪声的同时尽量保留信号的细节系数。通过分析癫痫信号的Matlab仿真实验数据,论文所构造阈值函数去噪后信噪比高于21.232而均方根误差均低于3.473。相较于软、硬阈值函数的信噪比至少提高了9%,而均方根误差相应至少降低19%。该算法不仅有效去除癫痫信号中的噪声,而且癫痫信号去噪后的信噪比和均方根误差数据均明显优于软硬阈值函数,验证了信号去噪的有效性。

Aiming at all kinds of pseudo-aberration signals in epileptic signals,this paper constructs and adopts an improved wavelet threshold function.By adjusting the parameters in the improved threshold function,it is possible to remove the detail coefficient of the signal while removing part of the noise of the high frequency signal.By analyzing the Matlab simulation data of epileptic signals,the signal-to-noise ratio of the threshold function after denoising is higher than 21.232 and the root mean square error is lower than 3.473.The signal-to-noise ratio is increased by at least 9%compared to the soft and hard threshold functions,and the root mean square error is reduced by at least 19%.The algorithm not only effectively removes the noise in the epileptic signal,but also the signal-to-noise ratio and root mean square error data of the epileptic signal denoising are better than the soft and hard threshold function,which verifies the effectiveness of signal denoising.