Gaussian noise filtering from ECG by Wiener filter and ensemble empirical mode decomposition

ASIAIR > College of Computer Science > Department of Photonics and Communication Engineering > Journal Article > Item 310904400/9562

Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/9562

Title:	Gaussian noise filtering from ECG by Wiener filter and ensemble empirical mode decomposition
Authors:	Kang-Ming Chang;Keng-Ming Chang;Shing-Hong Liu
Keywords:	ECG;Gaussian noise;Wiener filter;Ensemble empirical mode decomposition
Date:	2010-02
Issue Date:	2010-05-12 01:12:20 (UTC+0)
Publisher:	Asia University
Abstract:	Empirical mode decomposition (EMD) is a powerful algorithm that decomposes signals as a set of intrinsic mode function (IMF) based on the signal complexity. In this study, partial reconstruction of IMF acting as a filter was used for noise reduction in ECG. An improved algorithm, ensemble EMD (EEMD), was used for the first time to improve the noise-filtering performance, based on the mode-mixing reduction between near IMF scales. Both standard ECG templates derived from simulator and Arrhythmia ECG database were used as ECG signal, while Gaussian white noise was used as noise source. Mean square error (MSE) between the reconstructed ECG and original ECG was used as the filter performance indicator. FIR Wiener filter was also used to compare the filtering performance with EEMD. Experimental result showed that EEMD had better noise-filtering performance than EMD and FIR Wiener filter. The average MSE ratios of EEMD to EMD and FIR Wiener filter were 0.71 and 0.61, respectively. Thus, this study investigated an ECG noise-filtering procedure based on EEMD. Also, the optimal added noise power and trial number for EEMD was also examined.
Relation:	Journal of Signal Processing Systems
Appears in Collections:	[Department of Photonics and Communication Engineering] Journal Article

Files in This Item:

File	Size	Format
	0Kb	Unknown	582	View/Open

Loading...