Advertisement

Algorithm Research of ECG Characteristic Points Detection Based on Wavelet Transforms

  • Li Wang
  • Zhihong Chen
  • Xin Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7390)

Abstract

This paper introduces the algorithm research of ECG characteristic points detection based on the wavelet transforms. The ECG signal is filtered by Mallat algorithm using the dyadic spline wavelets and then detected R wave on some proper scales by our algorithm against ECG including muscle contraction noise. The experiment results proved that the ECG characteristic value detection algorithm based on wavelet transforms is efficient and has important practical value in clinical diagnosis and physiological study. The future work is mainly on the better choice of comparison function to effectively adjust the relationship between speed and accuracy of detection.

Keywords

ECG signal QRS complex wave Detection Wavelet transforms 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Naregalkar, A., Naga, A., Vamsee, J., et al.: ECG Noise Removal and QRS Complex Detection Using UWT. In: 2010 International Conference on Electronics and Information Engineering (ICEIE), pp. 438–442 (2010)Google Scholar
  2. 2.
    Thahor, N.V., Webstor, J.G., Tompkins, W.J.: Estimation of QRS Complex Power Spectra for Design of a QRS Filter. IEEE Transactions on Biomedical Engineering 31(11), 702–705 (1984)CrossRefGoogle Scholar
  3. 3.
    Bushra, J., Olivier, L., Eric, F., Ouadi, B.: Detection of QRS Complex in ECG Signal Based on Classification Approach. In: Proceedings of 2010 IEEE 17th International Conference on Image Processing, September 26-29, IEEE Press, Hong Kong (2010)Google Scholar
  4. 4.
    Yang, F.: Wavelet Transforms Technology Project Analysis and Application. Science press, Beijing (2000)Google Scholar
  5. 5.
    Dib, N., Benali, R., Hadj, S., et al.: Delineation of The Complex QRS and The T-end Us-ing Wavelet Transform and Surface Indicator. In: 2011 7th International Workshop on Systems, Signal Processing and their Applications, pp. 83–86 (2011)Google Scholar
  6. 6.
    Shubha, K., Robin, M., Faye, G.: Boudreaux-Bartels: Wavelet Transform-Based QRS comples Detector. IEEE Transactions on Biomedical Engineering 46(7), 838–848 (1999)CrossRefGoogle Scholar
  7. 7.
    Dinh, N., Kumar, D., Pah, N., Burton, P.: Wavelet for QRS Detection. In: 2001 Proceedings of the 23rd Annual EMBS International Conference, pp. 1883–1886 (2001)Google Scholar
  8. 8.
    Martinez, J.P., Almeida, R., Olmos, S., Rocha, A.P., et al.: A Wavelet-Based ECG De-lineator: Evaluation on Standard Databases. IEEE Transactions on Biomedical Engineering 51(4), 570–581 (2004)CrossRefGoogle Scholar
  9. 9.
    Sahambi, J.S., Tandon, S.N., Bhatt, R.K.P.: Using Wavelet Transforms for ECG Characterization. IEEE Engineering in Medicine and Biology Magazine 16(1), 77–83 (1999)CrossRefGoogle Scholar
  10. 10.
    Li, C., Zhang, C., Tai, C.: Detection of ECG Characteristic Points Using Wavelet Trans-forms. IEEE Transactions on Biomedical Engineering 42(1), 21–28 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Li Wang
    • 1
    • 2
  • Zhihong Chen
    • 1
  • Xin Zhang
    • 2
  1. 1.College of Life Science and TechnologyTongji UniversityShanghaiChina
  2. 2.Shanghai Medical Instrumentation CollegeUniversity of Shanghai for Science and TechnologyShanghaiChina

Personalised recommendations