## 4 Experimental Results
### Experimental Results and Comparison
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**DAE Model**} & \multirow{2}{*}{
\begin{tabular}{c} **Number of** \\ **Trainable** \\ **Parameters** \\ \end{tabular} } & \multirow{2}{*}{**MACs**} & \multicolumn{6}{c}{\(SNR_{in}\)} & \multicolumn{6}{c}{\(SNR_{in}\)} & \multicolumn{6}{c}{\(SNR_{in}\)} & \multicolumn{6}{c}{\(SNR_{in}\)} & \multicolumn{6}{c}{\(SNR_{in}\)} & \multicolumn{6}{c}{\(SNR_{in}\)} & \multicolumn{6}{c}{\(SNR_{in}\)} & \multicolumn{6}{c}{\(SNR_{in}\)} & \multicolumn{6}{c}{\(SNR_{in}\)} & \multicolumn{6}{c}{\(SNR_{in}\)} & \multicolumn{6}{c}{\(SNR_{in}\)} & \multicolumn{6}{c}{\(SNR_{in}\)} \\  & & \(-6\)**dB** & \(0\)**dB** & \(6\)**dB** & \(12\)**dB** & \(18\)**dB** & \(24\)**dB** & \(-6\)**dB** & \(0\)**dB** & \(6\)**dB** & \(12\)**dB** & \(18\)**dB** & \(24\)**dB** \\ \hline DNN & 1,399,712 & \(\mathbf{1.4}\)**M** & 18.83 & 13.72 & 8.49 & 2.62 & \(-2.94\) & \(-9.5\) & 88.15 & 79.13 & 76.70 & 73.00 & 73.65 & 73.38 \\ CNN & 1,116,478 & 13.27 M & 18.63 & 15.11 & 10.58 & 5.29 & \(-0.36\) & \(-7.08\) & 89.45 & 68.19 & 61.41 & 54.50 & 55.38 & 56.90 \\ FCN [22] & 78,444 & 25.08 M & 18.60 & 14.38 & 10.80 & 6.35 & 2.18 & \(-3.88\) & 93.53 & 73.29 & 60.21 & 49.00 & 42.01 & 39.29 \\ CNN-LSTM [23] & 10,920,532 & 46.69 M & 18.90 & 15.66 & 11.24 & 6.25 & 0.73 & \(-5.63\) & 86.92 & 65.23 & 54.68 & 42.70 & 47.44 & 47.38 \\ CPDAELow & **55,505** & 14.34 M & 18.85 & 16.12 & 12.44 & 8.01 & 4.31 & \(-0.72\) & 84.60 & 58.17 & 49.72 & 40.51 & 32.87 & 27.05 \\ CPDAELow & 194,609 & 56.96 M & 19.91 & 19.18 & 16.60 & 12.55 & 8.52 & 2.03 & 79.26 & 44.66 & 31.99 & 24.26 & 20.45 & 20.23 \\ CPDAEFull & 2,694,529 & 355.01 M & **23.68** & **27.75** & **24.99** & **21.38** & **16.92** & **8.15** & **51.20** & **18.10** & **12.81** & **9.28** & **8.65** & **8.66** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the Proposed Models with State-of-the-art Methods. Figure 13: Box plots for \(RMSE\) comparison of the denoising criteria of all of the evaluated methods under six \(SNR_{in}\) for the testing phase of NSTDB; the box plots include outliers (dot), minimum, interquartile range, median, maximum, and average (dotted line). Two noisy signals are displayed in Figure 16a,j, and the corresponding clean signals are displayed in Figure 16b,k. The ECG filtered with the CNN-DAE (Figure 16c,d) and DNN-DAE (Figure 16l,m) could eliminate most noise well, but the ECG signals were also destroyed. In the FCN-DAE, the reconstructed ECG retained most information of the ECG wave; however, one QRS segment was misjudged as noise (Figure 16e). The CNN-LSTM-DAE exhibited a similar result in Figure 16o. The proposed \(\text{CDPAE}_{\text{Lite}}\) removed the most noise, but the amplitude of the T wave was slightly decreased, as seen in Figure 16g,p. The proposed \(\text{CDPAE}_{\text{Regular}}\) retained the most significant ECG features. Moreover, the proposed \(\text{CDPAE}_{\text{Full}}\) retained the complete ECG features and produced the clearest ECG. In addition, we also tested the proposed DAE on self-recorded ECG, and the experimental results in Figure 17 demonstrate that the proposed models can extract a clear ECG signal from corrupted ECGs. Since these ECG and noise signals had never been seen in the training set, the reconstructed ECGs would have some distortion. However, it still shows that the proposed framework is able to restore important features such as the P wave, the QRS complex, and the T wave. Therefore, we believe that the generalizability of the proposed method exists and can stand the test of other datasets. Although the proposed \(\text{CDPAE}\) had outstanding performance, there were still some outliers in the statistical analysis. This result indicates some limitations existing in the proposed \(\text{CDPAE}\).