基于双阶段特征提取网络的ECG降噪分类算法OA北大核心CSTPCD

Since clinically acquired standard 12-lead ECGs often contain noise,which could affects the accuracy of the ECG signal classification results,a noise reduction classification algorithm for ECGs based on a two-stage feature extraction network was proposed.Firsty,in the spatial feature extraction stage,spatial features were extracted from the input 12-lead standard ECG signal by a residual contraction network with a deeply coupled soft thresholding denoising method.Secondly,in the temporal feature extraction stage,temporal features were extracted from the ECG signal by a combination of a long and short-term memory network and an attentional mechanism.And ultimately,the extracted spatial and temporal features were fused through the fully-connected network layer to output the probabilistic predictive distributions for the nine categories.In order to verify the effect of the proposed algorithm,comparison experiments were conducted with other state-of-the-art classification algorithms of the same type on the CPSC2018 dataset,and the experimental results showed that the proposed classification algorithm could achieve an average F1 score of 0.848 when classifying the nine categories of ECG signals,which was a much better performance in terms of various indicators.In addition,the experiment proved that the proposed algorithm also could outperform other mainstream networks in noise-containing data,which fully demonstrated the noise reduction classification performance of the proposed algorithm for noise-containing ECG signals.And the algorithm can also be applied to other similar noise-containing physiological signals for analysis and processing.