An ECG Denoising and Classification Algorithm Based on Two-stage Feature Extraction Network

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An ECG Denoising and Classification Algorithm Based on Two-stage Feature Extraction Network

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[1]LIN Nan,TANG Kaipeng,NIU Yongpeng,et al.An ECG Denoising and Classification Algorithm Based on Two-stage Feature Extraction Network[J].Journal of Zhengzhou University (Engineering Science),2024,45(05):61-68.[doi:10.13705/j.issn.1671-6833.2024.05.005]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 05 Page number: 61-68 Column: Public date: 2024-08-08

Title:: An ECG Denoising and Classification Algorithm Based on Two-stage Feature Extraction Network

Author(s):: LIN Nan; TANG Kaipeng; NIU Yongpeng; XIE Lipeng; School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450003, China

Keywords:: ECG classification; ECG denoising; residual shrinkable network; soft thresholding; attention mechanism

CLC:: TP183TN911. 7R541. 7

DOI:: 10.13705/j.issn.1671-6833.2024.05.005

Abstract:: Since clinically acquired standard 12-lead ECGs often contain noise, which could affects the accuracy ofthe ECG signal classification results, a noise reduction classification algorithm for ECGs based on a two-stagefeature extraction network was proposed. Firsty, in the spatial feature extraction stage, spatial features wereextracted from the input 12-lead standard ECG signal by a residual contraction network with a deeply coupled softthresholding denoising method. Secondly, in the temporal feature extraction stage, temporal features were extractedfrom the ECG signal by a combination of a long and short-term memory network and an attentional mechanism. Andultimately, the extracted spatial and temporal features were fused through the fully-connected network layer tooutput the probabilistic predictive distributions for the nine categories. In order to verify the effect of the proposedalgorithm, comparison experiments were conducted with other state-of-the-art classification algorithms of the sametype on the CPSC2018 dataset, and the experimental results showed that the proposed classification algorithm couldachieve an average F1 score of 0. 848 when classifying the nine categories of ECG signals, which was a much betterperformance in terms of various indicators. In addition, the experiment proved that the proposed algorithm alsocould outperform other mainstream networks in noise-containing data, which fully demonstrated the noise reductionclassification performance of the proposed algorithm for noise-containing ECG signals. And the algorithm can alsobe applied to other similar noise-containing physiological signals for analysis and processing.

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Last Update: 2024-09-02