Classification of cardiac arrhythmias using biorthogonal wavelet preprocessing and SVM

Berdakh Abibullaev, Won Seok Kang, Seung Hyun Lee, Jinung An

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In the current study we present a technique for the detection and classification of cardiac arrhythmias using biorthogonal wavelet functions and support vector machines (SVM). First, the wavelet transforms is applied to decompose the ECG signal into wavelet scales. Further, a soft thresholding technique is used to denoise and detect important cardiac events in the signal. Subsequently, we applied SVM classifier to discriminate the detected events into normal or pathological ones in the signal. Numeric computations demonstrate that the efficient wavelet pre-processing provides an accurate estimation of important physiological features of ECG and moreover it improves the SVM classification performance.

Original languageEnglish
Title of host publicationINC2010 - The International Conference on Networked Computing, Proceeding
Pages332-336
Number of pages5
Publication statusPublished - Jul 26 2010
Event6th International Conference on Networked Computing, INC2010 - Gyeongju, Korea, Republic of
Duration: May 11 2010May 13 2010

Publication series

NameINC2010 - The International Conference on Networked Computing, Proceeding

Other

Other6th International Conference on Networked Computing, INC2010
CountryKorea, Republic of
CityGyeongju
Period5/11/105/13/10

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Software

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  • Cite this

    Abibullaev, B., Kang, W. S., Lee, S. H., & An, J. (2010). Classification of cardiac arrhythmias using biorthogonal wavelet preprocessing and SVM. In INC2010 - The International Conference on Networked Computing, Proceeding (pp. 332-336). [5484804] (INC2010 - The International Conference on Networked Computing, Proceeding).