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Wavelet Analysis of ECG Signals

  • En-Bing LinEmail author
  • Megan Haske
  • Marilyn Smith
  • Darren Sowards
Chapter

Abstract

This study evaluated the effectiveness of different types of wavelets and thresholds to process electrocardiograms. An electrocardiogram, or ECG, shows the electrical activity in the heart and can be used to detect abnormalities. The first process used term-by-term thresholding to denoise ECGs. The second process denoised and compressed ECGs using global thresholding. The effectiveness was determined by using the signal-to-noise ratio (SNR) and the percentage root mean square difference (PRD).

Keywords

Discrete Wavelet Detail Coefficient Soft Thresholding Nonstationary Signal Hard Thresholding 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This research was conducted as part of the Central Michigan University LURE program during 2009–2011 and was supported by NSF-REU grant # 0606-36528. The authors are grateful for the support and would like to thank the anonymous referees’ helpful comments as well.

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Copyright information

© Springer New York 2013

Authors and Affiliations

  • En-Bing Lin
    • 1
    Email author
  • Megan Haske
    • 1
  • Marilyn Smith
    • 1
  • Darren Sowards
    • 1
  1. 1.Department of MathematicsCentral Michigan UniversityMt. PleasantUSA

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