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Title: Multi-lead model-based ECG signal denoising by guided filter
Authors: Hao, Huaqing
Liu, Ming
Xiong, Peng
Du, Haiman
Zhang, Hong
Lin, Feng
Hou, Zengguang
Liu, Xiuling
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Hao, H., Liu, M., Xiong, P., Du, H., Zhang, H., Lin, F., Hou, Z. & Liu, X. (2019). Multi-lead model-based ECG signal denoising by guided filter. Engineering Applications of Artificial Intelligence, 79, 34-44.
Journal: Engineering Applications of Artificial Intelligence
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.
ISSN: 0952-1976
DOI: 10.1016/j.engappai.2018.12.004
Rights: © 2018 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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