# An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter
## 4 Results and Discussion
### Qualitative Evaluation  
The performance of the proposed de-noising algorithm is qualitatively analyzed by visual evaluation. Figure 3 shows the results of de-noising the No. 114 record in the MIT-BIH database. As can be seen from the figure, the signal that is de-noised by wavelet obviously reduces a lot of noise, while the signal smoothed further removes more noise.  
In Figure 4, we select 119 records from the MIT-BIH arrhythmia database, which contained some ectopic complexs (there exists a complex in the opposite direction of QRS complex before the appearance of QRS complex,called as ectopic complex). We add 20 dB of white Gaussian noise to it to destroy the record. It can be observed from the denoised signals that these ectopic complexes are still preserved in the reconstructed signals, which will not affect the analysis of ECG signals. Compared with the traditional wavelet de-noising, EMD and EMD soft threshold, the ECG signal de-noised by the proposed method is closer to the original ECG signal and smoother.  
Figures 5-7 demonstrate the de-noising effect of the proposed method for various noises. Figure 5 shows the de-noising results of signal after adding 50 HZ power line interference to No. 106 record of the MIT-BIH database. Figure 6 displays the results of adding 20 dB WGN to No. 113  
Figure 4: The de-noising results of adding 20 dB WGN to No. 119 record of the MIT-BIH database: (**a**) The original signal. (**b**) The signal after adding 20 dB WGN. (**c–f**) De-noised signals using wavelet soft threshold, EMD, EMD soft threshold, proposed method.  
Figure 3: The results of de-noising 114 records in the MIT-BIH database: (**a**) Green is the signal de-noised by wavelet transform (including bursts), and red is the sub-band segmented by the minimum of maximum and the maximum of minimum. (**b**) The comparison between the signal after wavelet de-noising and the signal after wavelet de-noising and smoothing. (**c**) The comparison between the original signal and the signal after wavelet de-noising and smoothing. (**d**) The signal after de-noising and the resulting noise.  
record of the MIT-BIH database. Figure 7 shows the de-noising results after adding EMG interference to No. 115 record of the MIT-BIH database. As can be seen from these figures, the de-noising effect is obvious and the distortion is smaller than that of the original signal. The results show that the proposed method can effectively de-noise all kinds of noises, and the de-noised signal retains the characteristics of the original signal. Moreover the signal is very similar to the original signal.  
The evaluation also extends to the synthetic ECG signal. We add 15 dB Gauss white noise to the synthetic signal and denoise it. As shown in Figure 8, it can be seen that the algorithm also has excellent denoising effect on the synthetic ECG signal.  
Figure 5: The de-noising results of signal added 50 HZ power line interference to the 106 record of the MIT-BIH database: **(a)** The original signal. **(b)** The signal after adding power line interference. **(c–f)** De-noised signals using wavelet soft threshold, EMD, EMD soft threshold, proposed method.  
Figure 6: The de-noising results of adding 20 dB WGN to No. 113 record of the MIT-BIH database: **(a)** The original signal. **(b)** The signal after adding 20 dB WGN. **(c–f)** De-noised signals using wavelet soft threshold, EMD, EMD soft threshold, proposed method.