###### Abstract  
This paper presents a channel-wise average pooling and one dimension pixel-shuffle architecture for a denoising autoencoder (CFDAE) design that can be applied to efficiently remove electrode motion (EM) artifacts in an electrocardiogram (ECG) signal. The three advantages of the proposed design are as follows: (1) In the skip connection layer, less memory is needed to transfer the features extracted by the neural network; (2) Pixel shuffle and pixel unshuffle techniques with pointwise convolution are used to effectively reserve the key features generated from each layer in both the encoder and decoder; (3) Overall, fewer parameters are required to reconstruct the ECG signal. This paper describes three deep neural network models, namely \(\text{CPDAE}_{\text{Lite}}\), \(\text{CPDAE}_{\text{Regular}}\), and \(\text{CPDAE}_{\text{Full}}\), which support various computational capacity and hardware arrangements. The three proposed structures involve an encoder and decoder with six, seven, and eight layers, respectively. Furthermore, the \(\text{CPDAE}_{\text{Lite}}\), \(\text{CPDAE}_{\text{Regular}}\), and \(\text{CPDAE}_{\text{Full}}\) structures require fewer multiply-accumulate operations--355.01, 56.96, and 14.69 million, respectively--and less parameter usage--2.69 million, 149.7 thousand, and 55.5 thousand, respectively. To evaluate the denoising performance, the MIT-BIH noise stress test database containing six signal-to-noise ratios (SNRs) of noisy ECGs was employed. The results demonstrated that the proposed models had a higher improvement of SNR and lower percentage root-mean-square difference than other state-of-the-art methods under various conditions of SNR. elecrocardiogram (ECG); deep learning; denoising autoencoder; signal denoising Article  
Channel-Wise Average Pooling and 1D Pixel-Shuffle Denoising Autoencoder for Electrode Motion Artifact Removal in ECG  
Y.-S.-Y. [PERSON], S.-T.  
on the diagnosis of an ST segment in lead I [7]; MA noise is a high-frequency noise that occurs when muscle activates continuously, as when the patient feels cold or nervous [8]; EM artifacts are generated from the stretching of the Ag/AgCl electrode barrier layer in the skin [5]. Because EM artifacts are similar in appearance to ectopic beats, they are regarded as the most challenging noise to eliminate; PLI is a 50-Hz or 60-Hz artifact influenced by the ac in recording equipment [6]. Record ECGs are invariably accompanied by noise of diverse intensities, which affect the accuracy of the instrument and can affect the doctor's judgment. Therefore, a suitable denoising algorithm to reconstruct a clean ECG from a noisy ECG can aid in CVD diagnosis. Several denoising approaches based on different techniques have been developed; examples include adaptive filtering [9; 10; 11], the wavelet method [12; 13; 14], empirical mode decomposition (EMD) [15; 16; 17; 18], and denoising autoencoder (DAE) algorithms [19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. An adaptive filter updates its weight according to the error between the noisy ECG signal and the noise reference signal. In [9], various least-mean squares (LMS)-based adaptive filter algorithms were proposed. An adaptive filter uses a noise reference to only eliminate the noise in a noisy ECG and produce a clean ECG. The experimental results demonstrated that most noise was removed after 200 iterations. The QRS segment in an ECG is a complex wave, and it is difficult to denoise efficiently in this short segment because the LMS filter cannot update the weight coefficients immediately. In [10], the noisy ECG signal and the noise reference signal were transferred from the time domain into the frequency domain by using the fast Fourier transform, and then the LMS algorithm was used to calculate weights to the minimum mean-square error (_MSE_) between two input signals. The adaptive filter adjusts the magnitude in the frequency domain directly so that the filtered ECG approaches a clean ECG.