Open Access

Arrhythmic Pulses Detection Using Lempel-Ziv Complexity Analysis

EURASIP Journal on Advances in Signal Processing20062006:018268

https://doi.org/10.1155/ASP/2006/18268

Received: 24 January 2005

Accepted: 12 September 2005

Published: 26 March 2006

Abstract

Computerized pulse analysis based on traditional Chinese medicine (TCM) is relatively new in the field of automatic physiological signal analysis and diagnosis. Considerable researches have been done on the automatic classification of pulse patterns according to their features of position and shape, but because arrhythmic pulses are difficult to identify, until now none has been done to automatically identify pulses by their rhythms. This paper proposes a novel approach to the detection of arrhythmic pulses using the Lempel-Ziv complexity analysis. Four parameters, one lemma, and two rules, which are the results of heuristic approach, are presented. This approach is applied on 140 clinic pulses for detecting seven pulse patterns, not only achieving a recognition accuracy of 97.1% as assessed by experts in TCM, but also correctly extracting the periodical unit of the intermittent pulse.

[1234567891011121314151617181920212223242526]

Authors’ Affiliations

(1)
Department of Computer Science and Engineering, School of Computer Sciences and Technology, Harbin Institute of Technology (HIT)
(2)
Department of Computing, The Hong Kong Polytechnic University, Hung Hom

References

  1. Hammer LI: Chinese Pulse Diagnosis: A Contemporary Approach. Eastland Press, Vista, Calif, USA; 2001.Google Scholar
  2. Laub JH: New non-invasive pulse wave recording instrument for the acupuncture clinic. American Journal of Acupuncture 1983, 11(3):255-258.MathSciNetGoogle Scholar
  3. Michael B, Michael M: Instrument-assisted pulse evaluation in acupuncture. American Journal of Acupuncture 1986, 14(3):255-259.MathSciNetGoogle Scholar
  4. Seng H: Objectifying of pulse-taking. Japanese Journal of Oriental Medicine 1977, 27(4):7.Google Scholar
  5. Wang K-Q, Xu L-S, Li Z, Zhang D, Li N, Wang S: Approximate entropy based pulse variability analysis. Proceedings of 16th IEEE Symposium on Computer-Based Medical Systems (CBMS '03), June 2003, New York, NY, USA 236-241.Google Scholar
  6. Wang WK, Hsu TL, Chiang Y, Lin Wang YY: Study on the pulse spectrum change before deep sleep and its possible relation to EEG. Chinese Journal of Medical and Biological Engineering 1992, 12: 107-115.Google Scholar
  7. Wei LY, Chow P: Frequency distribution of human pulse spectra. IEEE Transactions on Biomedical Engineering 1985, 32(3):245-246.View ArticleGoogle Scholar
  8. Lee H-L, Suzuki SJ, Adachi Y, Umeno M: Fuzzy theory in traditional Chinese pulse diagnosis. Proceedings of International Joint Conference on Neural Networks (IJCNN '93), October 1993, Nagoya, Japan 1: 774-777.Google Scholar
  9. Yoon Y-Z, Lee M-H, Soh K-S: Pulse type classification by varying contact pressure. IEEE Engineering in Medicine and Biology Magazine 2000, 19(6):106-110. 10.1109/51.887253View ArticleGoogle Scholar
  10. Stockman GK, Kanal LN, Kyle MC: Structural pattern recognition of Carotid pulse waves using a general waveform parsing system. Communications of the ACM 1976, 19(12):688-695. 10.1145/360373.360378View ArticleMATHGoogle Scholar
  11. Wang L, Wang K-Q, Xu L-S: Recognizing wrist pulse waveforms with improved dynamic time warping algorithm. Proceedings of the 3rd International Conference on Machine Learning and Cybernetics (ICMLC '04), August 2004, Shanghai, China 6: 3644-3649.Google Scholar
  12. Wang BH, Xiang JL: ANN recognition of TCM pulse states. Journal of Northwestern Polytechnic University 2002, 20(3):454-457.MathSciNetGoogle Scholar
  13. Huang SL, Sun MY: The Study of Chinese Pulse Image. Chinese People's Sanitation Press, Beijing, China; 1995.Google Scholar
  14. Zhen LS: Pulse Diagnosis. Paradigm Publications, Brookline, Mass, USA; 1985.Google Scholar
  15. Lempel A, Ziv J: On the complexity of finite sequences. IEEE Transactions on Information Theory 1976, 22(1):75-81. 10.1109/TIT.1976.1055501MathSciNetView ArticleMATHGoogle Scholar
  16. Ziv J: Coding theorems for individual sequences. IEEE Transactions on Information Theory 1978, 24(4):405-412. 10.1109/TIT.1978.1055911MathSciNetView ArticleMATHGoogle Scholar
  17. Nagarajan R: Quantifying physiological data with Lempel-Ziv complexity-certain issues. IEEE Transactions on Biomedical Engineering 2002, 49(11):1371-1373. 10.1109/TBME.2002.804582View ArticleGoogle Scholar
  18. Huang LY, Sun QX, Cheng JZ: Novel method of fast automated discrimination of sleep stages. Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, September 2003, Cancun, Mexico 3: 2273-2276.Google Scholar
  19. Zhang X-S, Roy RJ, Jensen EW: EEG complexity as a measure of depth of anesthesia for patients. IEEE Transactions on Biomedical Engineering 2001, 48(12):1424-1433. 10.1109/10.966601View ArticleGoogle Scholar
  20. Mund S: Ziv-Lempel complexity for periodic sequences and its cryptographic application. Advances in Cryptology—EUROCRYPT '91, April 1991, Brighton, UK 114-126.Google Scholar
  21. Wang K-Q, Xu L-S, Wang L, Li ZG, Li YZ: Pulse baseline wander removal using wavelet approximation. Proceedings of the 30th Annual Conference of Computers in Cardiology (CinC '03), September 2003, Thessaloniki, Chalkidiki, Greece 605-608.Google Scholar
  22. Navakatikyan MA, Barrett CJ, Head GA, Ricketts JH, Malpas SC: A real-time algorithm for the quantification of blood pressure waveforms. IEEE Transactions on Biomedical Engineering 2002, 49(7):662-670. 10.1109/TBME.2002.1010849View ArticleGoogle Scholar
  23. Gratze G, Fortin J, Holler A, et al.: A software package for non-invasive, real-time beat-to-beat monitoring of stroke volume, blood pressure, total peripheral resistance and for assessment of autonomic function. Computers in Biology and Medicine 1998, 28(2):121-142. 10.1016/S0010-4825(98)00005-5View ArticleGoogle Scholar
  24. Belani KG, Buckley JJ, Poliac MO: Accuracy of radial artery blood pressure determination with the Vasotrac. Canadian Journal of Anesthesia 1999, 46(5):488-496. 10.1007/BF03012951View ArticleGoogle Scholar
  25. Xu L-S, Zhang D, Wang K-Q: Wavelet-based cascaded adaptive filter for removing baseline drift in pulse waveforms. IEEE Transactions on Biomedical Engineering 2005, 52(11):1973-1975. 10.1109/TBME.2005.856296View ArticleGoogle Scholar
  26. Gusfield D, Stoye J: Linear time algorithms for finding and representing all the tandem repeats in a string. Journal of Computer and System Sciences 2004, 69(4):525-546. 10.1016/j.jcss.2004.03.004MathSciNetView ArticleMATHGoogle Scholar

Copyright

© Xu et al. 2006