Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/152088
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dc.contributor.authorHao, Huaqingen_US
dc.contributor.authorLiu, Mingen_US
dc.contributor.authorXiong, Pengen_US
dc.contributor.authorDu, Haimanen_US
dc.contributor.authorZhang, Hongen_US
dc.contributor.authorLin, Fengen_US
dc.contributor.authorHou, Zengguangen_US
dc.contributor.authorLiu, Xiulingen_US
dc.date.accessioned2021-07-14T08:43:17Z-
dc.date.available2021-07-14T08:43:17Z-
dc.date.issued2019-
dc.identifier.citationHao, 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. https://dx.doi.org/10.1016/j.engappai.2018.12.004en_US
dc.identifier.issn0952-1976en_US
dc.identifier.urihttps://hdl.handle.net/10356/152088-
dc.description.abstractThe 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.en_US
dc.language.isoenen_US
dc.relation.ispartofEngineering Applications of Artificial Intelligenceen_US
dc.rights© 2018 Elsevier Ltd. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleMulti-lead model-based ECG signal denoising by guided filteren_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1016/j.engappai.2018.12.004-
dc.identifier.scopus2-s2.0-85059464663-
dc.identifier.volume79en_US
dc.identifier.spage34en_US
dc.identifier.epage44en_US
dc.subject.keywordsElectrocardiograph Denoisingen_US
dc.subject.keywordsMulti-lead Model-based Electrocardiograph Signalen_US
dc.description.acknowledgementThis research is partially supported by the National Natural Science Foundation of China (61673158, 61703133, 61473112), and the Natural Science Foundation of Hebei Province, China (F2016201186, F2017201222, F2018201070).en_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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