Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

ECG Filtering using Empirical Mode Decomposition Method

EMD 방법을 이용한 ECG 신호 필터링

  • Published : 2009.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Empirical mode decomposition (EMD) is new time-frequency analysis method to decompose the signal adaptively and efficiently. The key idea of EMD is to decompose the signal into a set of functions defined by the signal itself, named Intrinsic Mode Functions (IMFs), which preserve the inherent properties of the original signal. Since the decomposition is based on the local time scale of the signal, it is not only applicable to nonlinear and non-stationary processes but also useful in biomedical signals like electrocardiogram (ECG). Traditional low-pass filter uses fourier transform to analysis signal in frequency domain, but EMD is filtered to maintain signal properties in time domain. This paper performed signal decomposition and filtering for noisy ECGs using EMD method. The proposed method is presented and compared with traditional low-pass filter by two performance indices. Our results show effectiveness for enhancement of the noisy ECG waveforms.

EMD(Empirical mode decomposition) 방법은 시간-주파수 분석의 새로운 방법으로 적응적이며 효율적으로 신호를 분해한다. EMD는 신호 그 자체에 의해 정의된 IMFs(Intrinsic mode functions)로 명명되는 함수의 집합으로 분해되며, 분해된 IMFs는 원신호의 고유한 속성을 보존하므로 기저함수 및 필터로 사용될 수 있다. EMD 방법에 의한 분해는 신호의 지역적인 시간 스케일 특성에 기반을 두고 있으므로 비선형(non-linear) 비정상(non-stationary) 신호처리에 적합하며 ECG와 같은 생체 신호처리에 유용하다. 본 논문은 EMD 방법을 이용하여 ECG 신호를 분해하고 분해된 신호의 특성을 이용하여 잡음 제거 필터를 구현하였다. 전통적인 저주파 필터가 퓨리에 변환을 이용하여 주파수 영역에서 신호를 해석하는 것과 달리 EMD 방법은 시간 영역에서 필터링하여 신호의 속성을 유지한다. 영상 향상의 정도를 측정하기 위한 PRMD와 SSR 평가지수를 사용하여 제안된 기법과 전통적인 저주파 필터의 결과를 비교 제시하였다.

Keywords

References

  1. N.E.Huang, Z.Shen, S.R.Long, M.C.Wu, E.H.Shih, Q.Zheng, Yen. N.-C., C.C.Tung and H.H.Liu, 'The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,' Proc. Roy. Soc., London. A, vol. 454, pp. 903-995, 1998 https://doi.org/10.1098/rspa.1998.0193
  2. N.E.Huang, S.Zheng and R.L.Steven, 'A new view of nonlinear water waves: the Hilbert spectrum,' Annu. Rev. FluidMech., vol. 31, pp. 417-457, 1999 https://doi.org/10.1146/annurev.fluid.31.1.417
  3. B.Weng, M.Blanco-Velasco, and K.E.Barner, 'Baseline Wander Correction in ECG by the Empirical Mode Decomposition,' Proc. of the IEEE 32nd Annual Northeast Bioengineering Conference, pp. 135-136, 2006
  4. H.Liang, Qiu-Hua Lin, and J.D.Z.Chen, 'Application of the Empirical Mode Decomposition to the Analysis of Esophageal Manometric Data in Gastroesophageal Reflux Disease,' IEEE Transactions on biomedical engineering, vol. 52, no. 10, 2005
  5. H.Zhang and Q.Gai, 'Research on properties of empirical mode decompositionmethod,' Proc. of the 6th World Congress on Intelligent Control and Automation, vol. 2, pp. 10001-10004, 2006
  6. B.-U.Kohler, C.Hennig, R.Orglmeister, 'The principles of software QRS detection,' IEEE Engineering in Medicine and Biology Magazine, vol. 21, Issue 1, pp. 42-57, 2002 https://doi.org/10.1109/51.993193
  7. M.Okada, 'A digital filter for the QRS complex detection,' IEEE Trans. Biomed. Eng., vol. 26, pp. 700-703, Dec. 1979 https://doi.org/10.1109/TBME.1979.326461