###### Abstract
In complexity computation, LMS-based approaches are simpler than other denoising techniques and can be easily implemented in digital circuits. In [11], a delayed error normalized LMS (DENLMS) adaptive filter was proposed and implemented on FPGAs to reduce Gaussian white noise in a wireless ECG monitoring system. WT is another popular technique for reducing noise. WT is a capable tool that divides a noisy signal into numerous frequency subbands with different wavelet coefficients; subsequently, the threshold function limits the frequency magnitude for the reconstruction of the clean ECG signal. In [12], a nonlocal WT (NLWT) that combines nonlocal means (NLM) and WT was proposed. In a noisy ECG, the NLWT separates samples into several similar blocks by using the reference block in the similarity data matrix (SDM) extraction stage. Subsequently, these similar blocks are transferred into the wavelet domain, and the threshold function only retains the ECG magnitude to reconstruct the clean ECG signal. EMD-based approaches decompose the noisy ECG signal into several intrinsic mode functions (IMFs) using a Hilbert-Huang transform (HHT, [29]), and the denoising ECG signal can be reconstructed by removing the noise IMFs. In [15], a real-time 2-stage motion noise (MN) artifact cancelation method based on EMD was proposed. The EMD isolates high-frequency components of the signal under the assumption that most of the MN artifacts are contained in the first stage, and the MN artifacts are eliminated by the high-pass filter in the first EMD (F-EMD) in the second stage. Although the aforementioned denoising approaches have been widely and successfully applied, they have several limitations. In adaptive filtering approaches, a noise reference signal for BW, EM, and MA must be measured, necessitating the use of additional electrodes. In the wavelet approaches, the software and hardware thresholds to satisfy various scenarios are challenging to define. In the EMD approach, because the frequencies of P waves and T waves in lead I, II, and III are similar to some of the noise, they may be classified as noise IMFs after HHT [29]. The detailed pros and cons for various strategies are listed as Table 1. A DAE algorithm for ECG biosignal noise removal was proposed in [19; 20; 21; 22; 23]. In [19; 20], the DAE was improved to extract the features of a clean ECG in the wavelet domain. Because these approaches use the DAE for adjusting the threshold, they have greater denoising performance than do wavelet methods. In [21], a deep neural network (DNN)-DAE outperformed an adaptive filtering approach, EMD, and wavelet approaches with a superior signal-to-noise ratio (\(SNR_{imp}\)). In [22], a fully convolutional denoising autoencoder (FCN-based DAE) surpassed a DNN-DAE and convolutional neural network (CNN)-DAE. However, the QRS segment was distorted because fully connected layers averaged out the neighboring samples in the QRS segment. Reference [23] reports that denoising performance was improved by adding long short-term memory (LSTM) to a DAE. The LSTM cell learns the time series orders of ECG waves, which enhances the reconstruction quality. However, LSTM involves the use of numerous parameters and exhibits high computing complexity. Therefore, a CUDA accelerator was recommended to be used during the influence stage. In the studied DAE network, denoising performance was evaluated by calculating the \(SNR_{imp}\), root-mean-square error (RMSE), and percentage root-mean-square difference (PRD) for a noisy ECG with different SNRs. In contrast to various datasets widely used in computer vision, few datasets are available for ECG signal enhancement. Therefore, most noisy ECG signals are mixed manually using one of the following two methods: (1) adding Gaussian noise into the recorded ECG from patients (MIT-BIH arrhythmia database [30], MITDB); (2) adding EM, BW, and MA noise recorded from physically active volunteers (MIT-BIH Noise Stress Test Database [31], NSTDB) into a clean ECG. Recently, refs.