Multi-lead model-based ECG signal denoising by guided filter
The electrocardiogram (ECG) denoising is of paramount importance for accurate disease diagnosis, but individual differences bring great difficulties for ECG denoising, especially for Dynamic Electrocardiography (DCG). In this paper, a multi-lead model-based ECG signal denoising method is proposed, i...
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| Vydáno v: | Engineering applications of artificial intelligence Ročník 79; s. 34 - 44 |
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| Jazyk: | angličtina |
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Elsevier Ltd
01.03.2019
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| ISSN: | 0952-1976, 1873-6769 |
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| Abstract | The electrocardiogram (ECG) denoising is of paramount importance for accurate disease diagnosis, but individual differences bring great difficulties for ECG denoising, especially for Dynamic Electrocardiography (DCG). In this paper, a multi-lead model-based ECG signal denoising method is proposed, in which a guided filter is inherently adapted to denoise ECG signal. For each person, a patient-specific statistical model will be constructed by sparse autoencoder (SAE) which can effectively preserve the detailed signal features. Thus, the guided signal producing by the statistical model can perform well in the guided filter. Especially, even the sudden morphological changes, the denoised ECG signals can still be conserved. The results on the 12-lead Arrhythmia Database and the MIT-BIH Arrhythmia Database demonstrate that the signal-to-noise ratio (SNR) improvement of the proposed method can reach as high as 21.54 dB, and the mean squared error (MSE) is less than 0.0401. Besides achievement of minimum signal distortion in comparisons with the major of the current denoising algorithms for complex noise environment, the proposed method demonstrate robustness in the complex interferences, especially in tracing the sudden morphological changes of ECG signals. Due to the remarkable superiority in preserving diagnostic and detail features of ECG signals, the proposed method can handle ECG signals with abnormal heart beats, and then can improve the accuracy detection of the disease. |
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| AbstractList | The electrocardiogram (ECG) denoising is of paramount importance for accurate disease diagnosis, but individual differences bring great difficulties for ECG denoising, especially for Dynamic Electrocardiography (DCG). In this paper, a multi-lead model-based ECG signal denoising method is proposed, in which a guided filter is inherently adapted to denoise ECG signal. For each person, a patient-specific statistical model will be constructed by sparse autoencoder (SAE) which can effectively preserve the detailed signal features. Thus, the guided signal producing by the statistical model can perform well in the guided filter. Especially, even the sudden morphological changes, the denoised ECG signals can still be conserved. The results on the 12-lead Arrhythmia Database and the MIT-BIH Arrhythmia Database demonstrate that the signal-to-noise ratio (SNR) improvement of the proposed method can reach as high as 21.54 dB, and the mean squared error (MSE) is less than 0.0401. Besides achievement of minimum signal distortion in comparisons with the major of the current denoising algorithms for complex noise environment, the proposed method demonstrate robustness in the complex interferences, especially in tracing the sudden morphological changes of ECG signals. Due to the remarkable superiority in preserving diagnostic and detail features of ECG signals, the proposed method can handle ECG signals with abnormal heart beats, and then can improve the accuracy detection of the disease. |
| Author | Hou, Zengguang Liu, Ming Xiong, Peng Du, Haiman Liu, Xiuling Zhang, Hong Hao, Huaqing Lin, Feng |
| Author_xml | – sequence: 1 givenname: Huaqing surname: Hao fullname: Hao, Huaqing organization: Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, China – sequence: 2 givenname: Ming surname: Liu fullname: Liu, Ming email: liuming@hbu.cn organization: Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, China – sequence: 3 givenname: Peng surname: Xiong fullname: Xiong, Peng organization: Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, China – sequence: 4 givenname: Haiman surname: Du fullname: Du, Haiman organization: Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, China – sequence: 5 givenname: Hong surname: Zhang fullname: Zhang, Hong organization: Affiliated Hospital of Hebei University, Baoding, China – sequence: 6 givenname: Feng surname: Lin fullname: Lin, Feng organization: School of Computer Science and Engineering, Nanyang Technological University, Singapore – sequence: 7 givenname: Zengguang surname: Hou fullname: Hou, Zengguang organization: Institute of Automation, Chinese Academy of Sciences, Beijing, China – sequence: 8 givenname: Xiuling surname: Liu fullname: Liu, Xiuling email: liuxiuling121@hotmail.com organization: Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, China |
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