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Gaussian Noise Filtering from ECG by Wiener Filter and Ensemble Empirical Mode Decomposition

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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.

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Acknowledgments

This work was partially supported by NSC (Taiwan, grant number 98-2221-E-468-009-).

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Authors and Affiliations

  1. Asia University, Taichung, Taiwan, Republic of China

    Kang-Ming Chang

  2. Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung, Taiwan, Republic of China

    Shing-Hong Liu

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  1. Kang-Ming Chang
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  2. Shing-Hong Liu
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Correspondence to Kang-Ming Chang.

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Chang, KM., Liu, SH. Gaussian Noise Filtering from ECG by Wiener Filter and Ensemble Empirical Mode Decomposition. J Sign Process Syst 64, 249–264 (2011). https://doi.org/10.1007/s11265-009-0447-z

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  • DOI: https://doi.org/10.1007/s11265-009-0447-z

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