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Genetic particle filter improved fuzzy-AEEMD for ECG signal de-noising

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  • Rajiv Kapoor
  • Rajesh Birok

Abstract

With the aid of ensemble empirical mode decomposition (EEMD), de-noising of the electrocardiogram (ECG) signal based on the genetic particle filter and fuzzy thresholding is proposed in this paper, which effectively eliminates noise from the ECG signal. This paper proposes a two-phase scheme for removing noise from ECG signal. In the first phase, noisy signal is decomposed into true intrinsic mode functions (IMFs) with the help of EEMD. Adaptive EEMD (AEEMD) is better than EMD because it removes the mode-mixing effect. In the second phase, IMFs which are corrupted by noise are obtained by using spectral flatness of each IMF and fuzzy thresholding. Corrupted IMFs are filtered using genetic particle filter to remove the noise. Finally, the signal is reconstructed with the processed IMFs to get the de-noised ECG. The proposed algorithm is analyzed for different databases and it gives better signal-to-noise ratio and root mean square error than other existing techniques.

Suggested Citation

  • Rajiv Kapoor & Rajesh Birok, 2021. "Genetic particle filter improved fuzzy-AEEMD for ECG signal de-noising," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 24(13), pages 1426-1436, October.
    • Handle: RePEc:taf:gcmbxx:v:24:y:2021:i:13:p:1426-1436
    DOI: 10.1080/10255842.2021.1892659
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