###### Abstract
[20; 23; 24; 32] added Gaussian noise into a clean ECG and then successfully eliminated this white noise. However, this approach is limited because the noisy ECGs used are not realistic. In [19; 21; 22; 25; 27; 28], the ratios of BW, EM, and MA artifacts in the noisy ECG were not mentioned, which indicates that the noisy ECGs may be different even if the same signal segments of noise and ECG are employed. Therefore, evaluating the denoising performance of each approach is not possible. As illustrated in Figure 1, the waves of (a)-(d) are different when the composition ratio of noise is adjusted, but the SNRs of these four situations are identical. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Advantage** & **Disadvantage** \\ \hline \multirow{4}{*}{Adaptive Filter} & (1) Simplest and easy to implement on embedded systems or digital signal processors. & (1) A noise signal as a reference signal is requested, and different noise sources would generate different weights, which cannot be shared. \\  [9; 10; 11] & (2) Compared with AI algorithms, it has less computational complexity. & (2) The system would fail to work if the noise of the external environment changes suddenly and the weight updates too slow. \\  & (3) The category can be time domain and frequency domain processing. & (1) It can extract the feature in the spatial domain and has an effectively computational ability. & (1) It is very hard to definite the value of the software and hardware thresholds for all scenarios. \\  & (2) It takes more computational complexity than adaptive filter approaches but gains better results. & (2) The selection of mother wavelet functions would generate different results. \\ \hline \multirow{4}{*}{EMD} & (1) Baseline wander can be easily removed by using the highest IMF. & (1) The IMFs of noise may contain some part of ECG feature that cannot be arbitrarily discarded. \\  & (2) The process of the EMD algorithm is simple and routine so it is not suitable for complex and varied noises. & (2) The EMD algorithm takes the amount of computing time for the routine process and cannot be real-time and online executed due to the data dependency of IMFs’ calculations. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of Advantages and Disadvantages of Various Strategies. To objectively evaluate the denoising performance, we used NSTDB_118e [-6, 0, 6, 12, 18, and 24 dB] and NSTDB_119e [-6, 0, 6, 12, 18, and 24 dB] from PhysioNet as noisy ECG signals [33]. The experimental results demonstrated that the proposed approach is highly capable of suppressing different intensities of noise. In this paper, a high-quality channel-wise average pooling and 1D pixel-shuffle DAE (CPDAE) is proposed. The proposed CPDAE deploys residual block, pixel shuffle, global average pooling, and skip connection to extract a clean ECG. Here, skip connection is widely applied in deep learning, which can remedy the lost information with dimensionality reduction in the encoder and combines the features in deep and shallow layers [34; 35; 36]. However, all of the features have to be recorded in every encoder layer on computation if the feature information is directly transmitted from encoder to decoder. This requires a great amount of memory for additional data storage in the architecture. Hence, channel-wise average pooling (CWAP) and point-wise convolution (PW Conv.) are proposed to mitigate the issue of excessive memory requirements caused by original skip-connection. CWAP is calculated by averaging the features in every channel so that the memory requirement is substantially reduced. To provide information of the encoder layer effectively, point-wise convolution (PW Conv.) is applied to restore the features into the same shape as the encoder features, and then, the features are merged into the decoder layer, which achieves memory saving and provides information received from the encoder layer to the decoder layer.