A Study on the Extraction of Basis Functions for ECG Signal Processing

심전도 신호 처리를 위한 기저함수 추출에 관한 연구

  • 박광리 (용인송담대 의료정보시스템과) ;
  • 이전 (연세대 보건과학대 의공학과) ;
  • 이병채 (용인송담대 의료정보시스템과) ;
  • 정기삼 (용인송담대 의료정보시스템과) ;
  • 윤형로 (연세대 보건과학대 의공학과) ;
  • 이경중 (연세대 보건과학대 의공학과)
  • Published : 2004.04.01

Abstract

This paper is about the extraction of basis function for ECG signal processing. In the first step, it is assumed that ECG signal consists of linearly mixed independent source signals. 12 channel ECG signals, which were sampled at 600sps, were used and the basis function, which can separate and detect source signals - QRS complex, P and T waves, - was found by applying the fast fixed point algorithm, which is one of learning algorithms in independent component analysis(ICA). The possibilities of significant point detection and classification of normal and abnormal ECG, using the basis function, were suggested. Finally, the proposed method showed that it could overcome the difficulty in separating specific frequency in ECG signal processing by wavelet transform. And, it was found that independent component analysis(ICA) could be applied to ECG signal processing for detection of significant points and classification of abnormal beats.

Keywords

References

  1. Thakor, N. V., Zhu, Y., 'Application of Adaptive Filtering to ECG Analysis: Noise Cancelation and Arrhythmia Detection', IEEE Trans. Biomed. Eng., vol. 38, no. 8, pp. 785-794, 1991 https://doi.org/10.1109/10.83591
  2. Jane, R., Laguna, P., Thakor, N.V., Caminal, P., 'Adaptive Baseline Wander Removal in the ECG: Comparative Analysis with Cubic Spline Technique', Computers in Cardiology, pp. 143-146, 1992 https://doi.org/10.1109/CIC.1992.269426
  3. Afonso, V.X. et al., 'Comparing Stress ECG Enhancement Algorithm', IEEE Eng. in Med. & Biol., pp. 37-44, 1996 https://doi.org/10.1109/51.499756
  4. Friesen, G.M. et al., 'A Comparison of the Noise Sensitivity of Mine QRS Detection Algorithm', IEEE Trans. Biomed. Eng., vol. 37, no. 1, pp. 85-98, 1990 https://doi.org/10.1109/10.43620
  5. Laguna, P., Thakor, N.V., Caminal, P., Jane, R., Yoon, H.R., 'New Algorithm for QT Interval Analysis in 24-Hour Holter ECG : Performance and Applications', Med. & Bio. Eng. & Comput, pp. 67-73, Jan. 1990 https://doi.org/10.1007/BF02441680
  6. Jurandir, N., Marcelo, C.B., 'Classification of Cardiac Arrhythmia Based on Principal Component Analysis and Feedforward Neural Networks', IEEE Conference, pp. 341-344, 1993 https://doi.org/10.1109/CIC.1993.378434
  7. Kosko, B., 'Fuzzy Enginnering', Prentice Hall, pp. 467-497, 1997
  8. Lee, T.W., 'Independent Component Analysis : Theory and Application', 1998
  9. Vigon, L., Ssaatchi, M.R., Mayhew, J.E.W., Fernandes, R., 'Qautitative Evaluation of Techniques for Ocular Artefact Filtering of EEG Waveforms', IEE Proc-Sci. Meas. Technol. vol. 147, no. 5, Sep. 2000
  10. 장길진, '독립성분분석을 이용한 강인한 화자식별', 한국과학기술원 전산학과 석사논문, 1999
  11. Horner, S., Crilly, P.B., 'A Review of Fetal ECG Detection and Enhancement Methodologies', IEEE, 1991 https://doi.org/10.1109/SSST.1991.138564
  12. Zarzoso, V., Nandi, A.K., 'Noninvasive Fetal Electrocardiogram Extraction: Blind Separation versus Adaptive Noise Cancellation', IEEE Trans. on Biomed. Eng., vol. 48, no. 1, Jan. 2001 https://doi.org/10.1109/10.900244
  13. Vigario, R.N., 'Extraction of Ocular Artefacts from EEG using Independent Component Analysis', Electroencephalography and Clinical Neurophysiology, vol. 103, no. 3, pp. 395-404, 1997 https://doi.org/10.1016/S0013-4694(97)00042-8
  14. Hyvarinen, A., Oja., E., 'A Fast Fixed-Point Algorithm for Independent Component Analysis', Neural Computation, vol. 9, no. 7, pp. 1483-1492, 1997 https://doi.org/10.1162/neco.1997.9.7.1483
  15. Mansour, A., Chritain, J., Ohnishi, N., 'Kurtosis : Definition and Properties', FUSION'98 International Conference, pp. 40-46, 1998