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Global Decision Making for Wavelet Based ECG Segmentation

  • Carl BöckEmail author
  • Michael Lunglmayr
  • Christoph Mahringer
  • Christoph Mörtl
  • Jens Meier
  • Mario Huemer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10672)

Abstract

In this work, we propose an improvement of an established single lead electrocardiogram (ECG) beat segmentation algorithm based on the wavelet transform. First, for a particular recording a reference beat is determined by averaging over a certain amount of beats. Subsequently, this beat is used to obtain recording specific thresholds and search windows needed for the segmentation of the whole recording. Since noise and artifacts significantly influence the segmentation process, we show that using the information provided by the reference beat positively impacts the results. Specifically, using this global information of the reference beat, the algorithm becomes more robust against transient noise and signal abnormalities. Consequently, the proposed approach leads to an ECG beat segmentation algorithm specifically suited for detecting subtle relative changes of characteristic time intervals and amplitude levels.

Keywords

ECG beat delineation ECG beat segmentation ECG characteristic points Wavelet transform 

References

  1. 1.
    Li, C., Zheng, C., Tai, C.: Detection of ECG characteristic points using wavelet transforms. IEEE Trans. Biomed. Eng. 42, 21–28 (1995)CrossRefGoogle Scholar
  2. 2.
    Martinez, J.P., Almeida, R., Rocha, A.P., Laguna, P.: A wavelet-based ECG delineator evaluation on standard databases. IEEE Trans. Biomed. Eng. 51, 570–581 (2004)CrossRefGoogle Scholar
  3. 3.
    Laguna, P., Mark, R.G., Goldberg, A., Moody, G.B.: A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. In: Proceedings of Computers in Cardiology, Lund, pp. 673–676 (1997)Google Scholar
  4. 4.
    Sahambi, J.S., Tandon, S.N., Bhatt, R.K.P.: Wavelet based ST-segment analysis. Med. Biol. Eng. Comput. 36, 568–572 (1998)CrossRefGoogle Scholar
  5. 5.
    Mallat, S.G., Peyre, G.: A Wavelet Tour of Signal Processing: The Sparse Way. Academic Press, Boston (2008)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Institute of Signal ProcessingJohannes Kepler University LinzLinzAustria
  2. 2.Department of Anesthesiology and Critical Care MedicineKepler University Hospital LinzLinzAustria

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