DocumentCode :
2514931
Title :
Model-Based ECG Denoising Using Empirical Mode Decomposition
Author :
Lu, Yan ; Yan, Jingyu ; Yam, Yeung
Author_Institution :
Dept. of Mech. & Autom. Eng., Chinese Univ. of Hong Kong, Hong Kong, China
fYear :
2009
fDate :
1-4 Nov. 2009
Firstpage :
191
Lastpage :
196
Abstract :
In this paper, a novel scheme for electrocardiogram (ECG)denoising is presented based on ECG dynamic model and empirical mode decomposition (EMD). Firstly,we pre-filter the noisy ECG by making the model fit it in the MMSE sense, in order to preserve the important morphological features, especially the QRS complex. After that, the model is subtracted from the noisy ECG, and the residual signal is then decomposed using EMD and denoised by discarding the noise components from the decomposition results. Finally, the resultant ECG is obtained by combining the model and the denoised residue. Experiments conducted on both real and synthetic ECG data have demonstrated that the proposed method is a superior tool for ECG denoising.
Keywords :
bioelectric phenomena; electrocardiography; feature extraction; filtering theory; least mean squares methods; medical signal processing; signal denoising; ECG dynamic model; MMSE; QRS complex; electrocardiogram; empirical mode decomposition; model-based ECG denoising; morphological features; noisy ECG pre-filtering; residual signal; Automation; Bioelectric phenomena; Bioinformatics; Biomedical engineering; Electrocardiography; Frequency; Independent component analysis; Neural networks; Noise reduction; Signal analysis; Denoising; Electrocardiogram; Empirical mode decomposition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Bioinformatics and Biomedicine, 2009. BIBM '09. IEEE International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
978-0-7695-3885-3
Type :
conf
DOI :
10.1109/BIBM.2009.14
Filename :
5341814
Link To Document :
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