Adaptive ECG compression using support vector machine

Sándor M. Szilágyi, Laszlo Szilágyi, Zoltan Benyó

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

1 Citation (Scopus)

Abstract

An adaptive, support vector machine based ECG processing and compression method is presented in this study. The conventional pre-filtering algorithm is followed by a characteristic waves (QRS, T, P) localization. The regressive model parameters that describe the recognized waveformes are determined adaptively using general codebook information and patient specific data. The correct regocnition ratio of the QRS waves was above 99.9% using single channels from the MIT-BIH database files. The adaptive filter properly eliminates the perturbing noises such as 50/60 Hz power line or abrupt baseline shift or drift. The efficient signal coding algorithm can reduce the redundant data about 12 times. The good balance among proper signal quality for diagnosis and high compression rate is yielded by a support vector machine based system. The properly obtained wave locations and shapes, using a high compression rate, can form a solid base to improve the diagnosis performance in clinical environment.

Original languageEnglish
Title of host publicationProgress in Pattern Recognition, Image Analysis and Applications - 12th Iberoamerican Congress on Pattern Recognition, CIARP 2007, Proceedings
PublisherSpringer Verlag
Pages594-603
Number of pages10
ISBN (Print)9783540767244
Publication statusPublished - Jan 1 2007
Event12th Iberoamerican Congress on Pattern Recognition, CIARP 2007 - Vina del Mar-Valparaiso, Chile
Duration: Nov 13 2007Nov 16 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4756 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other12th Iberoamerican Congress on Pattern Recognition, CIARP 2007
CountryChile
CityVina del Mar-Valparaiso
Period11/13/0711/16/07

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Keywords

  • Adaptive estimation
  • QRS clustering
  • Signal compression
  • Support vector machine

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Szilágyi, S. M., Szilágyi, L., & Benyó, Z. (2007). Adaptive ECG compression using support vector machine. In Progress in Pattern Recognition, Image Analysis and Applications - 12th Iberoamerican Congress on Pattern Recognition, CIARP 2007, Proceedings (pp. 594-603). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4756 LNCS). Springer Verlag.