Journal of the Institute of Electronics Engineers of Korea SC (전자공학회논문지SC)

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지

(A Design of Adaptive Neural Network Filter to Remove the Baseline Wander of ECG)

심전도 신호의 기저선 잡음 제거를 위한 적응 신경망 필터 설계

  • Lee, Geon-Gi (Dept.of Eletronics Engineering, Gyeongsang National University) ;
  • Kim, Yeong-Il (Dept.of Eletronics Engineering, Gyeongsang National University) ;
  • Lee, Ju-Won (Dept.of Eletronics Engineering, Gyeongsang National University) ;
  • Jo, Won-Rae (Pohang College)
  • Published : 2002.01.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, it is studied to remove the baseline wander and to minimize the distortion of ST segment in the noise filtering of ECG. In general, the standard filter and adaptive filter are used to remove the baseline wander of the ECG. But the standard filter is limited because the frequency of the baseline signal is variable and the apative filter is difficult to select the reference signal in case of using the adaptive filter. So we proposed a new method of the structure without reference signal using neural networks. To be convinced of the performance of this method, we used ECG data of MIT-BIHs. and obtained the result of the high performance,(-53.3[dB]) than standard filter(-16.3[dB]) and adaptive filter (-44.9[dB]).

본 논문은 심전도 신호의 잡음제거에 있어 ST 세그먼트의 왜곡을 최소화함과 동시에 기저선 변동 잡음을 제거하기 위한 연구이다. 일반적인 표준필터와 적응필터는 심전도신호의 기저선 변동잡음을 제거하기 위해 주로 사용된다. 그러나 표준필터는 기저선 잡음의 시변 특성 때문에 고정된 주파수 대역으로 잡음을 제거하기가 어렵고, 적응필터를 이용하여 필터링 할 경우에는 참조신호를 설정하기가 매우 어렵다. 따라서 본 연구에서는 시-지연신경망과 RBF 신경망을 이용하여 참조신호 없이 잡음을 제거하는 새로운 구조의 적응 필터를 제안하였다. 그리고 제안된 기법의 성능을 평가하기 위해 MIT-BIH 심전도데이터를 이용하였고, 실험결과에서 평균 잡음 제거비는 표준 필터가 -16.3[dB], 적응 필터가 -44.9[dB]이고 제안된 필터의 경우에는 -53.3[dB]로 나타나 다른 필터의 경우보다 우수한 잡음 제거 성능을 보였다.

Keywords

References

  1. Sahambi, J. S., Tandon, S. N. and Bhatt, R. K. P. 'Wavelet based ST-segment analysis', Med. Biol. Eng. & comput., 36, pp:568-572, 1998 https://doi.org/10.1007/BF02524425
  2. Gary, M. F., Thomas, C. J., et al. 'A coparison of the noise sensitivity of nine QRS Detection Algorithms', IEEE Trans. Biomed. Eng. 37(1), pp:85-98, 1990 https://doi.org/10.1109/10.43620
  3. Van Alste, J. A. and Schilder, T. S. 'Removal of baseline wander and power-line interference from the ECG by an efficient FIR filter with a reduced number of taps', IEEE trans. Biomed. Eng, BME-32(12), pp:1052-1060, 1985 https://doi.org/10.1109/TBME.1985.325514
  4. AAMI Standard and Recomkended Practice, Vol 22 : Biomedical Equipment, Part2
  5. Park, K. L., Lee, K. J. and Yoon, H. R. Application of a wavelet adaptive filter to minimise distortion of the ST-segment. Med. Biol. Eng. & comput., 36, pp:568-572, 1998 https://doi.org/10.1007/BF02524425
  6. Haykin, S. Adative Filter Theory-3rd ed., Prentice Hall, pp:855-874, 1996
  7. Widrow, B., Glover, J. R., Adaptive noise cancelling : Principles and applications. Proc. IEEE. 36 : 1692-1716, 1975 https://doi.org/10.1109/PROC.1975.10036
  8. Thakor, N.V. and Zhu, Y. S. Applications of adaptive filtering to ECG analysis : Noise cancellation and arrhythmia detection. IEEE Trans. Biomed. Eng. 38(8) : 785-794, 1991 https://doi.org/10.1109/10.83591
  9. Lin, C. T. and George Lee, C. S. A neuro-fuzzy synergism to intelligent system. Prentice-Hall, Inc. : 250-272, 1996
  10. Lin Lee. Neural Fuzzy System, Printice Hall, 1996