Deep Neural Network Denoising Model Based on Sparse Representation Algorithm for ECG Signal

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.