## 5 Conclusions  
In this study, a CPDAE was proposed to effectively eliminate the electrode EM artifacts in a noisy ECG. The three proposed models of the CPDAE, namely CPDAE\({}_{\text{Lite}}\), CPDAE\({}_{\text{Regular}}\), and CPDAE\({}_{\text{Full}}\), can be implemented on devices and platforms with different computational capabilities. The purpose of designing CWAP is to reduce memory usage while feature transfers are needed. Therefore, it can be applied to any network that involves a shortcut layer or combines with an attention mechanism to selectively transfer features. The source code can be found in the Supplementary Materials. Considering that EM is the hardest noise to remove and causes PRD to be higher under the condition of \(-6\) dB \(SNR_{in}\), removing the EM signal without losing ECG features remains a challenge. To compare with state-of-the-art methods, the proposed models provided higher \(SNR\) with less computational complexity. The MAC and \(SNR\) results demonstrate that the proposed methods are suitable for future ECG instrumentation applications. However, among the limitations, it is worth noting that there were a small number of outliers in the boxplots of the three CPDAE models, which means the proposed method can not suppress the noise in some parts of all scenarios. Because the integrity of ECG segments in noisy ECGs cannot be ensured, the CPDAE misjudged ECG features as noise when the ECG features were not complete in noisy ECGs In addition, our studies can be extended to investigation of the following subjects: (1) The significant ECG features are kept completely or not; (2) Adding BW and MA noise to the evaluation; (3) The dataset should be re-checked because we found some ECGs containing noise in some fragments, which reduces the denoising performance during the training phase; (4) Using other loss function instead of MSE; (5) The information of RR intervals can be involved in the proposed CPDAE; (6) Considering that each ECG signal has highly similar and significant features, the generative adversarial networks (GAN) architecture can be a good solution to improve the quality of the reconstructed ECG; (7) Although the three proposed CPDAE models demonstrate very outstanding reconstructed quality, it is necessary to verify the usability of reconstructed ECGs via the doctor. **Supplementary Materials:** The following are available online at: [[https://github.com/[PERSON]/Channel-wise-Average-Pooling-and-1D-Pixel-Shuffle-Denoising-Autoencoder](https://github.com/[PERSON]/Channel-wise-Average-Pooling-and-1D-Pixel-Shuffle-Denoising-Autoencoder)]([https://github.com/[PERSON]/Channel-wise-Average-Pooling-and-1D-Pixel-Shuffle-Denoising-Autoencoder](https://github.com/[PERSON]/Channel-wise-Average-Pooling-and-1D-Pixel-Shuffle-Denoising-Autoencoder)) (accessed on 8 July 2022). **Author Contributions:** Conceptualization, [PERSON]-[PERSON] and [PERSON]; methodology, [PERSON]-[PERSON] and [PERSON]; software, [PERSON]-S.J. ; validation, [PERSON]-[PERSON] and [PERSON]-[PERSON]; formal analysis, [PERSON]-[PERSON] and [PERSON]; investigation, [PERSON]-[PERSON]; resources, [PERSON]; data curation, [PERSON]-[PERSON] and [PERSON]; writing--original draft preparation, [PERSON], [PERSON], [PERSON], and [PERSON]; writing--review and editing, [PERSON] and [PERSON], [PERSON]; visualization, [PERSON]; supervision, [PERSON] and [PERSON]; project administration, [PERSON], [PERSON], and [PERSON]; funding acquisition, [PERSON], [PERSON], and [PERSON]. All authors have read and agreed to the published version of the manuscript. Figure 18: Three scenarios for the limitations of the proposed CPDAE algorithm. (**a**) Boundary effect; (**b**) high-frequency interference; (**c**) strong high-frequency noise interference with boundary effect.