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.

Cardiovascular disease has become one of the main diseases threatening human life and health. This disease is very common and troublesome, and the existing medical resources are scarce, so it is necessary to use a computer-aided automatic diagnosis to overcome these limitations. A computer-aided diagnostic system can automatically diagnose through an electrocardiogram (ECG) signal. This paper proposes a novel deep-learning method for ECG classification based on adversarial domain adaptation, which solves the problem of insufficient-labeled training samples, improves the phenomenon of different data distribution caused by individual differences, and enhances the classification accuracy of cross-domain ECG signals with different data distributions. The proposed method includes three modules: multi-scale feature extraction F, domain discrimination D, and classification C. The module F, constitutive of three different parallel convolution blocks, is constructed to increase the breadth of features extracted from this module. The module D is composed of three convolutional blocks and a fully connected layer, which is to solve the problem of low model layers and low-feature abstraction. In the module C, the time features and the deep-learning extraction features are concatenated on the fully connected layer to enhance feature diversity. The effectiveness of the proposed method is verified by experiments, and the classification accuracy of the experimental electrical signals reaches 92.3%.

Electrocardiogram (ECG) is an efficient and simple method for the diagnosis of cardiovascular diseases and has been widely used in clinical practice. Because of the shortage of professional cardiologists and the popularity of electrocardiograms, accurate and efficient arrhythmia detection has become a hot research topic. In this paper, we propose a new multi-task deep neural network, which includes a shared low-level feature extraction module (i.e., SE-ResNet) and a task-specific classification module. Contextual Transformer (CoT) block is introduced in the classification module to dynamically model the local and global information of ECG feature sequence. The proposed method was evaluated on public CPSC2018 and PTB-XL datasets and achieved an average F1 score of 0.827 on the CPSC2018 dataset and an average F1 score of 0.833 on the PTB-XL dataset.

The 12-lead electrocardiogram (ECG) is widely used in the clinical diagnosis of cardiovascular disease, and deep learning has become an effective approach to automatic ECG classification. Generally, current research simply regards the 12-lead ECG signal as an ordinary 2-D array and does not specifically consider the intrinsic relationship between different leads when building the neural networks. However, ECG classes, from the biomedical perspective, mainly show specific patterns on one or several leads rather than all 12 leads, which suggests that it would be more efficient to learn class-specific intrinsic features from corresponding leads. To make use of such domain knowledge, in this study, we present a multilabel 12-lead ECG classification method based on the leadwise grouping multibranch network. A simple yet effective leadwise grouping strategy is proposed to incorporate domain knowledge to ECG classification models. Meanwhile, a multibranch network is designed accordingly, and spatial and temporal features are extracted by a BranchNet for each branch, which corresponds to one lead group. Moreover, an extended focal loss is presented to solve the class imbalance problem for multilabel classification. The proposed method was evaluated on two large-scale real-world ECG databases and yielded values of 0.9599, 0.7920, 0.7490, 0.5537, 0.1282, and 1.5354 for the area under receiver operating characteristic curve (AUROC), area under precision–recall curve (AUPRC), F1, accuracy (Acc), one-error (OE), and coverage (Cove), respectively, on the PhysioNet/Computing in Cardiology Challenge 2020 (CinC2020) database and values of 0.9531, 0.8975, 0.8102, 0.7484, 0.1791, and 0.5392 on the Shandong Provincial Hospital (SPH) database. The results are better than the existing work and are reached with fewer parameters and lower computational cost, demonstrating the effectiveness of the proposed method and leadwise grouping strategy.

Objective: Due to the lack of fine-grained labels, current research can only evaluate the signal quality at a coarse scale. This paper proposes a weakly supervised fine-grained electrocardiogram (ECG) signal quality assessment method, which can produce continuous segment-level quality scores with only coarse labels. Methods: A novel network architecture, i.e. FGSQA-Net, is developed for signal quality assessment, which consists of a feature shrinking module and a feature aggregation module. Multiple feature shrinking blocks, which combine residual CNN block and max pooling layer, are stacked to produce a feature map corresponding to continuous segments along the spatial dimension. Segment-level quality scores are obtained by feature aggregation along the channel dimension. Results: The proposed method was evaluated on two real-world ECG databases and one synthetic dataset. Our method produced an average AUC value of 0.975, which outperforms the state-of-the-art beat-by-beat quality assessment method. The results are visualized for 12-lead and single-lead signals over a granularity from 0.64 to 1.7 seconds, demonstrating that high-quality and low-quality segments can be effectively distinguished at a fine scale. Conclusion: FGSQA-Net is flexible and effective for fine-grained quality assessment for various ECG recordings and is suitable for ECG monitoring using wearable devices. Significance: This is the first study on fine-grained ECG quality assessment using weak labels and can be generalized to similar tasks for other physiological signals.