A dual-path interactive denoising autoencoder for removing mixed noise in multi-lead electrocardiogram signals

Denoising of dynamic electrocardiography (ECG) is a hotspot in ECG processing. Existing denoising methods mainly target single-lead signals or single noise sources, with limited research addressing mixed noise across multiple leads. Based on the time-frequency characteristics of common noise in ECG...

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Bibliographic Details
Published in:Computers & electrical engineering Vol. 123; p. 110177
Main Authors: Zhang, Xiuxin, Chen, Meng, Li, Yongjian, Gao, Jiahui, Sun, Yiheng, Liu, Feifei, Wei, Shoushui
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.04.2025
Subjects:
Attention mechanism
Bidirectional long short-term memory network (BiLSTM)
Convolutional neural network (CNN)
Dual-path
ECG denoising
Attention mechanism
Convolutional neural network (CNN)
Dual-path
ECG denoising
Bidirectional long short-term memory network (BiLSTM)
ISSN:0045-7906
Online Access:Get full text
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Summary:Denoising of dynamic electrocardiography (ECG) is a hotspot in ECG processing. Existing denoising methods mainly target single-lead signals or single noise sources, with limited research addressing mixed noise across multiple leads. Based on the time-frequency characteristics of common noise in ECG acquisition, this study introduces the Dual-Path Interactive Denoising Autoencoder (DP-IDAE). One pathway utilizes multi-scale convolutional kernels to extract local features, while the other employs bidirectional long short-term memory to capture global features. An interactive transmission mechanism facilitates information exchange between pathways. Experimental results demonstrate DP-IDAE's superior denoising performance across seven types of noise, including single and mixed noise composed of baseline wander (BW), muscle artifact (MA), and electrode motion (EM). In the complex noise environment of BW+EM+MA at -6 dB, theSNRimpand PRD still achieve 8.3 dB and 31.87 %, respectively. Additionally, the study investigates the relationship between denoising effects and signal similarity of different lead ECGs. It concludes that a higher similarity between lead signals leads to better denoising performance, especially for leads I, II, AVF, and V4-V6, where the denoising effect is more significant. From both time-domain and frequency-domain perspectives, DP-IDAE effectively removes local and global noise by leveraging the advantages of dual pathways.
ISSN:0045-7906
DOI:10.1016/j.compeleceng.2025.110177