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Journal of Medical Systems

, Volume 34, Issue 1, pp 71–81 | Cite as

Novel Approach to Fuzzy-Wavelet ECG Signal Analysis for a Mobile Device

  • Ching-En Tseng
  • Ching-Yu Peng
  • Ming-Wei Chang
  • Jia-Yush Yen
  • Chih-Kung Lee
  • Tse-Shih Huang
Original Paper

Abstract

This paper describes a signal processing technique for ECG signal analysis based upon the combination of wavelet analysis and fuzzy c-means clustering. The signal analysis technique is implemented into a biomedical signal diagnostic unit that is the carry on device for the Wireless Nano-Bios Diagnostic System (WNBDS) developed at National Taiwan University. The WNBDS integrates mobile devices and remote data base servers to conduct online monitoring and remote healthcare applications. The signal analysis and diagnostic algorithms in this paper are implemented in an embedded mobile device to conduct mobile biomedical signal diagnostics. At this stage, the Electrocardiogram (ECG or EKG) is analyzed for patient health monitoring. The ECG signal processing is based on the wavelet analysis, and the diagnosis is based on fuzzy clustering. The embedded system is realized with the Windows CE operating system.

Keywords

Discrete wavelet transform (DWT) Fuzzy C-means (FCM) ECG Windows CE Embedded system 

Notes

Acknowledgements

This work is supported by the Ministry of Economics, Taiwan ROC, under the Wireless Health Advanced Monitoring Bio-Diagnosis System, WHAM-BioS project.

References

  1. 1.
    Wu, S., Qian, Y., Gao, Z., and Lin, J., A novel method for beat-to-beat detection of ventricular late potentials. IEEE Transactions on Biomedical Engineering BME. 48:931–935, 2001.CrossRefGoogle Scholar
  2. 2.
    Kadambe, S., Murray, R., and Boudreaux-Bartels, G. F., Wavelet transform-based QRS complex detector. IEEE Transactions on Biomedical Engineering. 46:838–848, 1999. doi: 10.1109/10.771194.CrossRefGoogle Scholar
  3. 3.
    Addison, P. S., Watson, J. N., Glegg, G. R., Holzer, M., Sterz, F., and Robertson, C. E., Evaluating arrhythmias in ECG signals using wavelet transforms. IEEE Engineering in Medicine and Biology Magazine. 19:104–109, 2000. doi: 10.1109/51.870237.CrossRefGoogle Scholar
  4. 4.
    Li, C., Zheng, C., and Tai, C., Detection of ECG characteristic points usingwavelet transforms. IEEE Transactions on Biomedical Engineering. 42:21–28, 1995. doi: 10.1109/10.362922.CrossRefGoogle Scholar
  5. 5.
    Castro, B., Kogan, D., & Geva, A. B. (2000). ECG feature extraction using optimal mother wavelet. In Proc. 21st IEEE Convention of the Electrical and Electronic Engineers in Israel, pp. 346–350.Google Scholar
  6. 6.
    Chan, H. L., Siao, Y. C., Chen, S. W., Yu, S. F., Wavelet-based ECG compression by bit-field preserving and running length encoding. Computer Methods and Programs in Biomedicine. 90(1):1–8, April 2008.Google Scholar
  7. 7.
    Senhadji, L., Carrault, G., Bellanger, J. J., Passariello, G., Comparing wavelet transforms for recognizing cardiac patterns. IEEE Engineering in Medicine and Biology Magazine. 14:167–173, 1995. doi:  10.1109/51.376755.CrossRefGoogle Scholar
  8. 8.
    Lewicke, A., Sazonov, E., Corwin, M. J., Neuman, M., Schuckers, S., Sleep versus wake classification from heart rate variability using computational intelligence: consideration of rejection in classification models. IEEE Transactions on Biomedical Engineering. 55(1):108–118, Jan 2008.CrossRefGoogle Scholar
  9. 9.
    Loskutov, A., Mironyuk, O., Time series analysis of ECG: a possibility of the initial diagnostics. International Journal of Bifurcation and Chaor. 17(10):3709-3713, 2007.MATHCrossRefGoogle Scholar
  10. 10.
    Ubeyli, E. D., Usage of eigenvector methods in implementation of automated diagnostic systems for ECG beats. Digital Signal Processing. 18:33–48, 2008.CrossRefGoogle Scholar
  11. 11.
    Mehta, S. S., Lingayat, N. S., Combined entropy based method for detection of QRS complexes in 12-lead electrocardiogram using SVM. Computers in Biology and Medicine. 38:138–145, 2008.CrossRefGoogle Scholar
  12. 12.
    Polat, K., Akdemir, B., Gunes, S., Computer aided diagnosis of ECG data on the least square support vector machine. Digital Signal Processing. 18:25–32, 2008.CrossRefGoogle Scholar
  13. 13.
    Fira, C. M., Goras, L., An ECG signals compression method and its validation using NNs. IEEE Transactions on Biomedical Engineering. 55(4):1319–1326, Apr 2008.CrossRefGoogle Scholar
  14. 14.
    Jiang, W., Kong, S. G., Block-based neural networks for personalized ECG signal classification. IEEE Transactions on Neural Networks. 18(6):1750–1761, Nov 2007.CrossRefGoogle Scholar
  15. 15.
    Bezdek, J. C., Pattern recognition with fuzzy objective function algorithms. Plenum, New York, 1981.MATHGoogle Scholar
  16. 16.
    Mallat, S. G., A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 11:674–693, 1989. doi: 10.1109/34.192463.MATHCrossRefGoogle Scholar
  17. 17.
    Burrus, C. S., Gopinath, R. A., and Guo, H., Introduction to wavelet and wavelet transform—A primer. Prentice Hall, Upper Saddle River, NJ, 1998.Google Scholar
  18. 18.
    Daubechies, I., Ten lectures on wavelets. SIAM, Philadelphia, 1992.MATHGoogle Scholar
  19. 19.
    Dave, R. N., and Krishnapuram, R., Robust clustering methods: A unified view. IEEE Transactions on Fuzzy Systems. 5:2270–293, 1997. MAY.CrossRefGoogle Scholar
  20. 20.
    Al-Sultan, K. S., and Fedjki, C. A., A tabu search-based algorithm for the Fuzzy Clustering Problem. Pattern Recognition. 30:122023–2030, 1997. DEC.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Ching-En Tseng
    • 1
  • Ching-Yu Peng
    • 1
  • Ming-Wei Chang
    • 1
  • Jia-Yush Yen
    • 1
  • Chih-Kung Lee
    • 2
  • Tse-Shih Huang
    • 1
  1. 1.Department of Mechanical EngineeringNational Taiwan UniversityTaipeiRepublic of China
  2. 2.Institute of Applied MechanicsNational Taiwan UniversityTaipeiRepublic of China

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