心电信号降噪小波函数选取的定量研究

Quantitative study on the selection of wavelet functions for the de-noising of ECG signal

摘要:

为了定量地评估不同小波函数对心电(ECG)信号的降噪效果，建立了含噪声的ECG模型作为实验标准信号，采用正交小波变换和不同阈值方法来对该标准信号进行高频噪声消除实验，通过信噪比参数结合波形形态来衡量降噪效果。实验表明，当降噪后信噪比接近标准信号信噪比时，降噪效果最佳，得到既能保证信号失真度小又具有较高信噪比的降噪方案和适用于ECG信号小波分解和重构的小波函数，最后通过MIT-BIH数据库数据验证了利用该研究结果能够有效地消除ECG信号中的高频噪声。

Abstract:

To quantitatively evaluate the de-noising effects of different wavelet functions on the electrocardiogram(ECG) signal，a noisy ECG model was constructed as an experimental standard signal，which was processed by orthogonal wavelet transform and different thresholding methods to remove high-frequency noise. The de-noising effects were measured by Signal to Noise Ratio(SNR) and the shape of waveforms. It was demonstrated that when the SNR after denoising approached to that of the standard signal，the de-noising effect was optimal. Thus，the de-noising schemes with the less signal distortion and the higher SNR were obtained. Meanwhile，the wavelet function appropriate for the decomposition and reconstruction of ECG signal was determined. Verified by MIT-BIH database，the results of this study can effectively remove high-frequency noise in ECG signal.