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Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation

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Abstract

This work aims at providing new insights on the electrocardiogram (ECG) segmentation problem using wavelets. The wavelet transform has been originally combined with a hidden Markov models (HMMs) framework in order to carry out beat segmentation and classification. A group of five continuous wavelet functions commonly used in ECG analysis has been implemented and compared using the same framework. All experiments were realized on the QT database, which is composed of a representative number of ambulatory recordings of several individuals and is supplied with manual labels made by a physician. Our main contribution relies on the consistent set of experiments performed. Moreover, the results obtained in terms of beat segmentation and premature ventricular beat (PVC) detection are comparable to others works reported in the literature, independently of the type of the wavelet. Finally, through an original concept of combining two wavelet functions in the segmentation stage, we achieve our best performances.

References

  1. 1.

    Li C, Zheng C, Tai C: Detection of ECG characteristic points using wavelet transforms. IEEE Transactions on Biomedical Engineering 1995,42(1):21–28. 10.1109/10.362922

  2. 2.

    Sahambi JS, Tandon SN, Bhatt RKP: Using wavelet transforms for ECG characterization. An on-line digital signal processing system. IEEE Engineering in Medicine and Biology Magazine 1997,16(1):77–83. 10.1109/51.566158

  3. 3.

    Senhadji L, Carrault G, Bellanger JJ, Passariello G: Comparing wavelet transforms for recognizing cardiac patterns. IEEE Engineering in Medicine and Biology Magazine 1995,14(2):167–173. 10.1109/51.376755

  4. 4.

    Chazal P, Reilly RB: A comparison of the use of different wavelet coefficients for the classification of the electrocardiogram. Proceedings of the 15th International Conference on Pattern Recognition (ICPR '00), September 2000, Barcelona, Spain 2: 255–258.

  5. 5.

    Andreão RV, Dorizzi B, Cortez PC, Mota JCM: Efficient ECG multi-level wavelet classification through neural network dimensionality reduction. Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing (NNSP '02), September 2002, Martigny, Switzerland 395–404.

  6. 6.

    Besar R, Eswaran C, Sahib S, Simpson RJ: On the choice of the wavelets for ECG data compression. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '00), June 2000, Istanbul, Turkey 6: 3614–3617.

  7. 7.

    Castro B, Kogan D, Geva AB: ECG feature extraction using optimal mother wavelet. Proceedings of the 21st IEEE Convention of the Electrical and ELectronic Engineers in Israel, April 2000, Tel-Aviv, Israel 346–350.

  8. 8.

    Ahmed SM, Al-Shrouf A, Abo-Zahhad M: ECG data compression using optimal non-orthogonal wavelet transform. Medical Engineering and Physics 2000,22(1):39–46. 10.1016/S1350-4533(00)00010-2

  9. 9.

    Addison PS: Wavelet transforms and the ECG: a review. Physiological Measurement 2005,26(5):R155–R199. 10.1088/0967-3334/26/5/R01

  10. 10.

    Andreão RV, Dorizzi B, Boudy J: ECG signal analysis through hidden Markov models. IEEE Transactions on Biomedical Engineering 2006,53(8):1541–1549. 10.1109/TBME.2006.877103

  11. 11.

    Mallat S: A Wavelet Tour of Signal Processing. Academic Press, San Diego, Calif, USA; 1998.

  12. 12.

    Bhatia P, Boudy J, Andreão RV: Wavelet transformation and pre-selection of mother wavelets for ECG signal processing. Proceedings of the 24th IASTED International Conference on Biomedical Engineering, February 2006, Innsbruck, Austria 390–395.

  13. 13.

    Torrence C, Compo GP: A practical guide to wavelet analysis. Bulletin of the American Meteorological Society 1998,79(1):61–78. 10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2

  14. 14.

    Rabiner LR, Juang B-H: Fundamentals of Speech Recognition. Prentice-Hall, Englewood Cliffs, NJ, USA; 1993.

  15. 15.

    Rabiner LR, Lee CH, Juang BH, Wilpon JG: HMM clustering for connected word recognition. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '89), May 1989, Glasgow, UK 1: 405–408.

  16. 16.

    Laguna P, Mark RG, Goldberger A, Moody GB: A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. Proceedings of the 24th Annual Meeting on Computers in Cardiology (CIC '97), September 1997, Lund, Sweden 673–676.

  17. 17.

    ANSI/AAMI : Testing and reporting performance results of cardiac rhythm and ST segment. ANSI/AAMI EC 57–293. Arlington: AAMI, 1998, 37 p

  18. 18.

    Martínez JP, Almeida R, Olmos S, Rocha AP, Laguna P: A wavelet-based ECG delineator: evaluation on standard databases. IEEE Transactions on Biomedical Engineering 2004,51(4):570–581. 10.1109/TBME.2003.821031

  19. 19.

    Coast DA, Stern RM, Cano GG, Briller SA: An approach to cardiac arrhythmia analysis using hidden Markov models. IEEE Transactions on Biomedical Engineering 1990,37(9):826–836. 10.1109/10.58593

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Correspondence to Rodrigo Varejão Andreão.

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Andreão, R.V., Boudy, J. Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation. EURASIP J. Adv. Signal Process. 2007, 056215 (2006) doi:10.1155/2007/56215

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Keywords

  • Markov Model
  • Hide Markov Model
  • Quantum Information
  • Wavelet Transform
  • Wavelet Function