In this paper, a novel denoising method for electrocardiogram (ECG) signal is proposed to improve performance and availability under multiple noise cases. The method is based on the framework of conditional generative adversarial network (CGAN), and we improved the CGAN framework for ECG denoising. The proposed framework consists of two networks: a generator that is composed of the optimized convolutional auto-encoder (CAE) and a discriminator that is composed of four convolution layers and one full connection layer. As the convolutional layers of CAE can preserve spatial locality and the neighborhood relations in the latent higher-level feature representations of ECG signal, and the skip connection facilitates the gradient propagation in the denoising training process, the trained denoising model has good performance and generalization ability. The extensive experimental results on MIT-BIH databases show that for single noise and mixed noises, the average signal-to-noise ratio (SNR) of denoised ECG signal is above 39 dB, and it is better than that of the state-of-the-art methods. Furthermore, the denoised classification results of four cardiac diseases show that the average accuracy increased above 32 $\%$ under multiple noises under SNR=0 dB. So, the proposed method can remove noise effectively as well as keep the details of the features of ECG signals.

Electrocardiogram (ECG) denoising is very important for heart disease diagnosis. The traditional ECG denoising models have problems such as single noise type and poor interpretability of deep neural networks. The innovation of the proposed method is to incorporate the precious achievements of traditional methods into the design of neural networks and to build a bridge between them. Therefore, a novel interpretable deep denoising framework based on sparse representation is proposed in this study, and the half quadratic splitting (HQS) algorithm is applied to decompose the denoising method into sparse representations as an iterative solution process. In addition, a new weight distribution (WD) module is designed to extract adaptive hyperparameters based on ECG correlation instead of empirical values and greatly improves the efficiency of hyperparameter selection. To demonstrate the fairness and effectiveness of the proposed method, four different denoising models with different data preprocessing techniques are used for comparison. The extensive experimental validation and simulation studies demonstrated that the proposed framework has an excellent performance in quantitative and visual evaluation.

The electrocardiogram (ECG) is widely used in medicine because it can provide basic information about different types of heart disease. However, ECG data are usually disturbed by various types of noise, which can lead to errors in diagnosis by doctors. To address this problem, this study proposes a method for denoising ECG based on disentangled autoencoders. A disentangled autoencoder is an improved autoencoder suitable for denoising ECG data. In our proposed method, we use a disentangled autoencoder model based on a fully convolutional neural network to effectively separate the clean ECG data from the noise. Unlike conventional autoencoders, we disentangle the features of the coding hidden layer to separate the signal-coding features from the noise-coding features. We performed simulation experiments on the MIT-BIH Arrhythmia Database and found that the algorithm had better noise reduction results when dealing with four different types of noise. In particular, using our method, the average improved signal-to-noise ratios for the three noises in the MIT-BIH Noise Stress Test Database were 27.45 db for baseline wander, 25.72 db for muscle artefacts, and 29.91 db for electrode motion artefacts. Compared to a denoising autoencoder based on a fully convolutional neural network (FCN), the signal-to-noise ratio was improved by an average of 12.57%. We can conclude that the model has scientific validity. At the same time, our noise reduction method can effectively remove noise while preserving the important information conveyed by the original signal.

By leveraging deep neural networks, recent face swapping techniques have performed admirably in generating faces that maintain consistent identities. Nevertheless, while these methods accurately transfer source identities, they often struggle to preserve important attributes (such as head poses, expressions, and gaze directions) in the target faces. As a consequence, the current research in this domain has not resulted in satisfactory performance. In this paper, we propose an efficient attribute-preserving framework, called AP-Swap, for short, for face swapping. Our approach incorporates two innovative modules designed specifically to preserve critical facial attributes. First, we propose a global residual attribute-preserving encoder (GRAPE), which adaptively extracts globally complete attribute features from target faces. Second, in addition to the regular network streams for the source and target facial images, we introduce a network stream that takes into account the facial landmarks of the target faces. This additional stream enables our landmark-guided feature entanglement module (LFEM), which efficiently preserves fine-grained facial attributes by conducting a landmark-based attribute-preserving (LBAP) operation. Through extensive quantitative and qualitative experiments, we demonstrate the superiority of AP-Swap over other state-ofthe-art methods in terms of facial attribute preservation and model efficiency, along with satisfactory identity consistency performance