Support vector machine-based ECG compression

S. M. Szilágyi, L. Szilágyi, Z. Benyó

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)


This paper presents an adaptive, support vector machine-based ECG signal processing and compression method. After a conventional pre-filtering step, the characteristic waves (QRS, P, T) from the ECG signal are localized. The following step contains a regressive model for waveform description in terms of model parameters. The gained information allows an iterative filtering in permanent concordance with the aimed processing manner. The structure of the algorithm allows real-time adaptation to the heart's state. Using these methods for one channel of the MIT-BIH database, the detection rate of QRS complexes is above 99.9%. The negative influence of various noise types, like 50/60 Hz power line, abrupt baseline shift or drift, and low sampling rate was almost completely eliminated. The vector support machine system allow a good balance between compressing and diagnostic performance and the obtained results can form a solid base for better data storage in clinical environment.

Original languageEnglish
Title of host publicationAnalysis and Design of Intelligent Systems using Soft Computing Techniques
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783540724315
Publication statusPublished - Jan 1 2007

Publication series

NameAdvances in Soft Computing
ISSN (Print)1615-3871
ISSN (Electronic)1860-0794

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computational Mechanics
  • Computer Science Applications

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    Szilágyi, S. M., Szilágyi, L., & Benyó, Z. (2007). Support vector machine-based ECG compression. In Analysis and Design of Intelligent Systems using Soft Computing Techniques (pp. 737-745). (Advances in Soft Computing; Vol. 41). Springer Verlag.