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Adaptive ECG Compression Using Support Vector Machine

  • Sándor M. Szilágyi
  • László Szilágyi
  • Zoltán Benyó
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)

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.

Keywords

signal compression QRS clustering support vector machine adaptive estimation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Sándor M. Szilágyi
    • 1
  • László Szilágyi
    • 1
    • 2
  • Zoltán Benyó
    • 2
  1. 1.Sapientia - Hungarian Science University of Transylvania, Faculty of Technical and Human Science, Târgu-MureşRomania
  2. 2.Budapest University of Technology and Economics, Dept. of Control Engineering and Information Technology, BudapestHungary

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