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  • Research Article
  • Open Access

Modeling of Electrocardiogram Signals Using Predefined Signature and Envelope Vector Sets

EURASIP Journal on Advances in Signal Processing20072007:012071

  • Received: 28 April 2006
  • Accepted: 24 November 2006
  • Published:


A novel method is proposed to model ECG signals by means of "predefined signature and envelope vector sets (PSEVS)." On a frame basis, an ECG signal is reconstructed by multiplying three model parameters, namely, predefined signature vector ," "predefined envelope vector ," and frame-scaling coefficient (FSC). All the PSVs and PEVs are labeled and stored in their respective sets to describe the signal in the reconstruction process. In this case, an ECG signal frame is modeled by means of the members of these sets labeled with indices and and the frame-scaling coefficient, in the least mean square sense. The proposed method is assessed through the use of percentage root-mean-square difference (PRD) and visual inspection measures. Assessment results reveal that the proposed method provides significant data compression ratio (CR) with low-level PRD values while preserving diagnostic information. This fact significantly reduces the bandwidth of communication in telediagnosis operations.


  • Visual Inspection
  • Quantum Information
  • Compression Ratio
  • Assessment Result
  • Diagnostic Information

Authors’ Affiliations

Department of Electronics Engineering, Engineering Faculty, IŞIK University, Kumbaba Mevkii, Sile, Istanbul, 34980, Turkey
Speech Technology and Research (STAR) Laboratory, Information and Computing Sciences Division, SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025, USA
Department of Electrical-Electronics Engineering, College of Engineering, Istanbul University, Avcılar, Istanbul, 34230, Turkey
Department of Physical Electronics, Graduate School of Science and Technology, Tokyo Institute of Technology, Ookayama Campus, 2-12-1, Ookayama, Meguro-Ku, Tokyo 152-8552, Japan


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© Hakan Gürkan et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.