Journal of Zhejiang University-SCIENCE A

, Volume 8, Issue 8, pp 1283–1289 | Cite as

Investigation on the automatic parameters extraction of pulse signals based on wavelet transform



This paper analyses a key problem in the quantification of pulse diagnosis. Due to the subjectivity and fuzziness of pulse diagnosis, quantitative methods are needed. To extract the parameters of pulse signals, the prerequisite is to detect the corners of pulse signals correctly. Up to now, the pulse parameters are mostly acquired by marking the pulse corners manually, which is an obstacle to modernize pulse diagnosis. Therefore, a new automatic parameters extraction approach for pulse signals using wavelet transform is presented. The results testified that the method we proposed is feasible and effective and can detect corners of pulse signals accurately, which can be expected to facilitate the modernization of pulse diagnosis.

Key words

Pulse signal Feature extraction Complex wavelet transform Quantitative diagnosis 

CLC number



Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Fei, Z.F., 2003. Contemporary Sphygmology in Traditional Chinese Medicine. People’s Medical Publishing House, Beijing, p.58–73 (in Chinese).Google Scholar
  2. Lee, H.L., Suzuki, S., Adachi, Y., Umeno, M., Shan, K., 1993. Fuzzy Theory in Traditional Chinese Pulse Diagnosis. Proc. Int. Joint Conf. on Neural Networks. Nagoya, Japan, 10:774–778.Google Scholar
  3. Lee, J.S., Sun, Y.N., Cen, C.H., 1995. Multiscale comer detection by using wavelet transform. IEEE Trans. on Image Processing, 4(1):100–104. [doi:10.1109/83.350810]CrossRefGoogle Scholar
  4. Mallat, S., Hwang, W.L., 1992. Singularity detection and processing with wavelet. IEEE Trans. on Inf. Theory, 38(2):617–643. [doi:10.1109/18.119727]MathSciNetCrossRefMATHGoogle Scholar
  5. Mallat, S., Zhong, S., 1992. Characterization of signals from multiscale edges. IEEE Trans. on Pattern Anal. Machine Intell., 14(7):710–732. [doi:10.1109/34.142909]CrossRefGoogle Scholar
  6. Quddus, A., Fahmy, M.M., 1999. An Improved Wavelet-based Corner Detection Technique. Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, 6:3213–3216.Google Scholar
  7. Sun, L.U., Tang, Y.Y., You, X.G., 2004. Corner Detection for Object Recognition by Using Wavelet Transform. Proc. 3rd Int. Conf. on Machine Learning and Cybernetics. Shanghai, p.26–29.Google Scholar
  8. Tu, C.L., Hwang, W.L., 2005. Analysis of singularities from modulus maxima of complex wavelets. IEEE Trans. on Inf. Theory, 51:1049–1062. [doi:10.1109/TIT.2004.842706]MathSciNetCrossRefMATHGoogle Scholar
  9. Wang, H.Y., Cheng, Y.Y., 2005. A Quantitative Model for Pulse Diagnosis in Traditional Chinese Medicine. 27th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society. Shanghai, China, p.5676–5679.Google Scholar
  10. Xu, L.S., Zhang, D., Wang, K.Q., Wang, L., 2006. Arrhythmic pulses detection using Lempel-Ziv complexity analysis. EURASIP J. Appl. Signal Processing, p.1–12. [doi:10.1155/ASP/2006/18268]Google Scholar
  11. Xu, L.S., Zhang, D., 2007. Baseline wander correction in pulse waveform using wavelet-based cascaded adaptive filter. Computers Biol. Med., 37(5):716–731. [doi:10.1016/j.compbiomed.2006.06.014]CrossRefGoogle Scholar
  12. Yoon, Y.Z., Lee, M.H., Soh, K.S., 2000. Pulse type classification by varying contact pressure. IEEE Eng. Med. Biol. Mag., 19:106–110. [doi:10.1109/51.887253]Google Scholar

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  1. 1.College of Computer Science & Information EngineeringZhejiang Gongshang UniversityHangzhouChina
  2. 2.Institute of VLSI DesignZhejiang UniversityHangzhouChina

Personalised recommendations