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Multi-lead model-based ECG signal denoising by guided filter

文献类型：期刊论文

作者Hao, Huaqing1; Liu, Ming1; Xiong, Peng1; Du, Haiman1; Zhang, Hong2; Lin, Feng3; Hou, Zengguang4; Liu, Xiuling1
刊名ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
出版日期2019-03-01
卷号79页码:34-44
关键词Electrocardiograph (ECG) denoising Multi-lead model-based ECG signal Guided filter Sparse autoencoder
ISSN号0952-1976
DOI10.1016/j.engappai.2018.12.004
通讯作者Liu, Xiuling(liuxiuling121@hotmail.com)
英文摘要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.
WOS关键词CARDIOVASCULAR-DISEASE ; DECOMPOSITION ; ALGORITHM ; FACE
资助项目National Natural Science Foundation of China[61673158] ; National Natural Science Foundation of China[61703133] ; National Natural Science Foundation of China[61473112] ; Natural Science Foundation of Hebei Province, China[F2016201186] ; Natural Science Foundation of Hebei Province, China[F2017201222] ; Natural Science Foundation of Hebei Province, China[F2018201070]
WOS研究方向Automation & Control Systems ; Computer Science ; Engineering
语种英语
WOS记录号WOS:000459524300004
出版者PERGAMON-ELSEVIER SCIENCE LTD
资助机构National Natural Science Foundation of China ; Natural Science Foundation of Hebei Province, China
源URL[http://ir.ia.ac.cn/handle/173211/25034]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
通讯作者Liu, Xiuling
作者单位1.Hebei Univ, Key Lab Digital Med Engn Hebei Prov, Coll Elect & Informat Engn, Baoding, Peoples R China
2.Hebei Univ, Affiliated Hosp, Baoding, Peoples R China
3.Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore, Singapore
4.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Hao, Huaqing,Liu, Ming,Xiong, Peng,et al. Multi-lead model-based ECG signal denoising by guided filter[J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,2019,79:34-44.
APA Hao, Huaqing.,Liu, Ming.,Xiong, Peng.,Du, Haiman.,Zhang, Hong.,...&Liu, Xiuling.(2019).Multi-lead model-based ECG signal denoising by guided filter.ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,79,34-44.
MLA Hao, Huaqing,et al."Multi-lead model-based ECG signal denoising by guided filter".ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 79(2019):34-44.

入库方式： OAI收割

来源：自动化研究所

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