Modeling of Electrocardiogram Signals Using Predefined Signature and Envelope Vector Sets
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Research Article Modeling of Electrocardiogram Signals Using Predefined Signature and Envelope Vector Sets 1 Umit ¨ ¨ 1, 2 and B. Sıddık Yarman3, 4 ¨ Guz, Hakan Gurkan, 1 Department
of Electronics Engineering, Engineering Faculty, IS¸IK University, Kumbaba Mevkii, 34980 Sile, Istanbul, Turkey Technology and Research (STAR) Laboratory, Information and Computing Sciences Division, SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025, USA 3 Department of Electrical-Electronics Engineering, College of Engineering, Istanbul University, 34230 Avcılar, Istanbul, Turkey 4 Department of Physical Electronics, Graduate School of Science and Technology, Tokyo Institute of Technology, Ookayama Campus, 2-12-1 Ookayama, Meguro-Ku 152-8552, Tokyo, Japan 2 Speech
Received 28 April 2006; Accepted 24 November 2006 Recommended by Maurice Cohen 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 (PSV)R ,” “predefined envelope vector (PEV)K ,” 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 R and K 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. Copyright © 2007 Hakan G¨urkan et al. 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.
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INTRODUCTION
An electrocardiogram (ECG) signal which is a graphical display of the electrical activity of the heart is an essential biological signal for the monitoring and diagnosis of heart diseases. ECG signals which are recorded with digital equipment are most widely used in applications such as monitoring, cardiac diagnosis, event analysis, real-time transmission over telephone networks, patient databases, or long-term recording. The amount of ECG data grows depending upon sampling rate, sampling precision, number of lead, and recording time. Obviously, continuous generation of huge amount of ECG data requires high storage capacity and also wide transmission band for the remote monitoring activities. While retaining all clinically significant features including P-waves, QRS complexes, and T-waves, compression of the ECG signals is essential in the biomedical engineering [1–3]. Various methods have been developed for modeling and comp
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