## 4 Experimental Results
### Experimental Results and Comparison
Figures 12 and 13 demonstrate the average \(PRD\) and \(RMSE\), respectively. Lower \(PRD\) and \(RMSE\) values imply that the reconstructed ECG is closer to a clean ECG. EM noise is the most difficult to remove because it has waves similar to a clean ECG. The models treated the ECG signal as noise in some cases, which resulted in higher \(PRD\) values. Under the -6 dB condition of \(SNR_{in}\), the CNN-LSTM-DAE approach exhibited the widest interquartile range (IQR), which indicates that the denoising performance was the most unstable. The FCN method had the lowest IQR, but its average \(PRD\) was higher than that of the other methods. Although the proposed CPDAE\({}_{\text{Lite}}\) did not exhibit any obvious difference in \(SNR_{imp}\), the reconstructed ECG was more similar to a clean ECG than those obtained with the other methods. However, the \(PRD\) and \(RMSE\) values of the proposed CPDAE\({}_{\text{Full}}\) were much lower than the values of the other methods in all situations. Figure 11: Box plots for \(SNR_{imp}\) 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). Figure 12: Box plots for \(PRD\) 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). Table 6 reports the total numbers of trainable parameters and MACs for various methods with the two criteria of average \(SNR_{imp}\) and average \(PRD\). The DNN-DAE, CNN-DAE, and CNN-LSTM-DAE used several fully connected layers, which necessitated the use of numerous parameters and resulted in ineffective processing of complex noise. For the comparison of MACs, the DNN-DAE consists of the fully connected layer so that it only costs 1.4 M MACs. The proposed CPDAEFull costs the highest MACs because the number of channels is higher than others. For the performance comparison of the \(SNR_{imp}\) and \(PRD\), the proposed CPDAELite uses fewer parameters and MACs than FCN-DAE and achieves better denoising performance. Here, CPDAEFull is the best version of the proposed models, and it exhibits outstanding denoising performance under different \(SNR_{in}\) values, although it would take 344.01 M MACs. To ensure the generalizability of the proposed method, EM noise was further added into a clean ECG in MIT-BIH Normal Sinus Rhythm Database (NSRDB, [52]), where the data include the ECG signals from 16 subjects. Twelve minutes of ECG signals were taken from each subject to be evaluated in experiments. Six levels of noisy ECG signals were mixed into ECG signals, i.e., \(-6\), \(0\), \(6\), \(12\), \(18\), and \(24\) dB. Finally, there were a total of 59,400 untrained noisy ECG data to be tested in the testing phase. To evaluate the various algorithms by \(SNR_{imp}\) and \(PRD\), a box plot was deployed as shown in Figures 14 and 15. The results show that the three proposed CPDAE models had superior noise suppression compared to the other algorithms.