An efficient ECG denoising method by fusing ECA-Net and CycleGAN

Peng Zhang; Mingfeng Jiang; Yang Li; Ling Xia; Zhefeng Wang; Yongquan Wu; Yaming Wang; Huaxiong Zhang; Peng Zhang; Mingfeng Jiang; Yang Li; Ling Xia; Zhefeng Wang; Yongquan Wu; Yaming Wang; Huaxiong Zhang

doi:10.3934/mbe.2023598

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 7: 13415-13433. doi: 10.3934/mbe.2023598

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Research article Special Issues

An efficient ECG denoising method by fusing ECA-Net and CycleGAN

1.
School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
2.
Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China
3.
Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Road, Chaoyang District, Beijing 100029, China
4.
Zhejiang Key Laboratory of DDIMCCP, Lishui University, Lishui, China

Academic Editor: Mette Olufsen

Received: 02 March 2023 Revised: 14 May 2023 Accepted: 23 May 2023 Published: 12 June 2023

For wearable electrocardiogram (ECG) acquisition, it was easy to infer motion artifices and other noises. In this paper, a novel end-to-end ECG denoising method was proposed, which was implemented by fusing the Efficient Channel Attention (ECA-Net) and the cycle consistent generative adversarial network (CycleGAN) method. The proposed denoising model was optimized by using the ECA-Net method to highlight the key features and introducing a new loss function to further extract the global and local ECG features. The original ECG signal came from the MIT-BIH Arrhythmia Database. Additionally, the noise signals used in this method consist of a combination of Gaussian white noise and noises sourced from the MIT-BIH Noise Stress Test Database, including EM (Electrode Motion Artifact), BW (Baseline Wander) and MA (Muscle Artifact), as well as mixed noises composed of EM+BW, EM+MA, BW+MA and EM+BW+MA. Moreover, corrupted ECG signals were generated by adding different levels of single and mixed noises to clean ECG signals. The experimental results show that the proposed method has better denoising performance and generalization ability with higher signal-to-noise ratio improvement (SNR_imp), as well as lower root-mean-square error (RMSE) and percentage-root-mean-square difference (PRD).
- ECG,
- ECA-Net,
- CycleGAN,
- signal denoising,
- motion artifact
Citation: Peng Zhang, Mingfeng Jiang, Yang Li, Ling Xia, Zhefeng Wang, Yongquan Wu, Yaming Wang, Huaxiong Zhang. An efficient ECG denoising method by fusing ECA-Net and CycleGAN[J]. Mathematical Biosciences and Engineering, 2023, 20(7): 13415-13433. doi: 10.3934/mbe.2023598

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Abstract

For wearable electrocardiogram (ECG) acquisition, it was easy to infer motion artifices and other noises. In this paper, a novel end-to-end ECG denoising method was proposed, which was implemented by fusing the Efficient Channel Attention (ECA-Net) and the cycle consistent generative adversarial network (CycleGAN) method. The proposed denoising model was optimized by using the ECA-Net method to highlight the key features and introducing a new loss function to further extract the global and local ECG features. The original ECG signal came from the MIT-BIH Arrhythmia Database. Additionally, the noise signals used in this method consist of a combination of Gaussian white noise and noises sourced from the MIT-BIH Noise Stress Test Database, including EM (Electrode Motion Artifact), BW (Baseline Wander) and MA (Muscle Artifact), as well as mixed noises composed of EM+BW, EM+MA, BW+MA and EM+BW+MA. Moreover, corrupted ECG signals were generated by adding different levels of single and mixed noises to clean ECG signals. The experimental results show that the proposed method has better denoising performance and generalization ability with higher signal-to-noise ratio improvement (SNR_imp), as well as lower root-mean-square error (RMSE) and percentage-root-mean-square difference (PRD).

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