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Modeling of Electrocardiogram Signals Using Predefined Signature and Envelope Vector Sets


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.


  1. 1.

    Hudson DL, Cohen ME: Intelligent analysis of biosignals. Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society (EMBS '05), September 2005, Shanghai, China 323–326.

    Google Scholar 

  2. 2.

    Jalaleddine SMS, Hutchens CG, Strattan RD, Coberly WA: ECG data compression techniques - a unified approach. IEEE Transactions on Biomedical Engineering 1990,37(4):329-343. 10.1109/10.52340

    Article  Google Scholar 

  3. 3.

    Cârdenas-Barrera JL, Lorenzo-Ginori JV: Mean-shape vector quantizer for ECG signal compression. IEEE Transactions on Biomedical Engineering 1999,46(1):62-70. 10.1109/10.736756

    Article  Google Scholar 

  4. 4.

    Jalaleddine SMS, Hutchens CG: SAIES - a new ECG data compression algorithm. Journal of Clinical Engineering 1990,15(1):45-51.

    Article  Google Scholar 

  5. 5.

    Miaou S-G, Larn J-H: Adaptive vector quantisation for electrocardiogram signal compression using overlapped and linearly shifted codevectors. Medical and Biological Engineering and Computing 2000,38(5):547-552. 10.1007/BF02345751

    Article  Google Scholar 

  6. 6.

    Sun C-C, Tai S-C: Beat-based ECG compression using gain-shape vector quantization. IEEE Transactions on Biomedical Engineering 2005,52(11):1882-1888. 10.1109/TBME.2005.856270

    Article  Google Scholar 

  7. 7.

    Skretting K, Engan K, Husøy JH: ECG compression using signal dependent frames and matching pursuit. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '05), March 2005, Philadelphia, Pa, USA 4: 585–588.

    Google Scholar 

  8. 8.

    de Lima Filho EB, da Silva EAB, de Carvalho MB, da Silva Júnior WS, Koiller J: Electrocardiographic signal compression using multiscale recurrent patterns. IEEE Transactions on Circuits and Systems I: Regular Papers 2005,52(12):2739-2753.

    Article  Google Scholar 

  9. 9.

    Lee H, Buckley KM: ECG data compression using cut and align beats approach and 2-D transforms. IEEE Transactions on Biomedical Engineering 1999,46(5):556-564. 10.1109/10.759056

    Article  Google Scholar 

  10. 10.

    Wei J-J, Chang C-J, Chou N-K, Jan G-J: ECG data compression using truncated singular value decomposition. IEEE Transactions on Information Technology in Biomedicine 2001,5(4):290-299. 10.1109/4233.966104

    Article  Google Scholar 

  11. 11.

    Miaou S-G, Yen H-L, Lin C-L: Wavelet-based ECG compression using dynamic vector quantization with tree codevectors in single codebook. IEEE Transactions on Biomedical Engineering 2002,49(7):671-680. 10.1109/TBME.2002.1010850

    Article  Google Scholar 

  12. 12.

    Chou H-H, Chen Y-J, Shiau Y-C, Kuo T-S: An effective and efficient compression algorithm for ECG signals with irregular periods. IEEE Transactions on Biomedical Engineering 2006,53(6):1198-1205. 10.1109/TBME.2005.863961

    Article  Google Scholar 

  13. 13.

    Tai S-C, Sun C-C, Yan W-C: A 2-D ECG compression method based on wavelet transform and modified SPIHT. IEEE Transactions on Biomedical Engineering 2005,52(6):999-1008. 10.1109/TBME.2005.846727

    Article  Google Scholar 

  14. 14.

    Miaou S-G, Chao S-N: Wavelet-based lossy-to-lossless ECG compression in a unified vector quantization framework. IEEE Transactions on Biomedical Engineering 2005,52(3):539-543. 10.1109/TBME.2004.842791

    Article  Google Scholar 

  15. 15.

    Kim BS, Yoo SK, Lee MH: Wavelet-based low-delay ECG compression algorithm for continuous ECG transmission. IEEE Transactions on Information Technology in Biomedicine 2006,10(1):77-83. 10.1109/TITB.2005.856854

    Article  Google Scholar 

  16. 16.

    Zigel Y, Cohen A, Katz A: ECG signal compression using analysis by synthesis coding. IEEE Transactions on Biomedical Engineering 2000,47(10):1308-1316. 10.1109/10.871403

    Article  Google Scholar 

  17. 17.

    Güz Ü, Gürkan H, Yarman BS: A novel method to represent the speech signals by using language and speaker independent predefined functions sets. Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS '04), May 2004, Vancouver, BC, Canada 3: 457–460.

    Google Scholar 

  18. 18.

    Yarman BS, Güz Ü, Gürkan H: On the comparative results of "SYMPES: a new method of speech modeling". AEU - International Journal of Electronics and Communications 2006,60(6):421-427. 10.1016/j.aeue.2005.08.003

    Article  Google Scholar 

  19. 19.

    Moody GB: The MIT-BIH Arrhythmia Database CD-ROM. 2nd edition. Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass, USA; 1992.

    Google Scholar 

  20. 20.

    Karaş A: Elektriksel İşaretlerin Temel Tanım Fonksiyonlarıyla Karakterizasyonu, Ph.D. thesis. Department of Electrical and Electronic Engineering, Institute of Science, Istanbul University, Istanbul, Turkey; 1997.

    Google Scholar 

  21. 21.

    Zigel Y, Cohen A, Katz A: The weighted diagnostic distortion (WDD) measure for ECG signal compression. IEEE Transactions on Biomedical Engineering 2000,47(11):1422-1430. 10.1109/TBME.2000.880093

    Article  Google Scholar 

  22. 22.

    Blanco-Velasco M, Cruz-Roldán F, Godino-Llorente JI, Blanco-Velasco J, Armiens-Aparicio C, López-Ferreras F: On the use of PRD and CR parameters for ECG compression. Medical Engineering and Physics 2005,27(9):798-802. 10.1016/j.medengphy.2005.02.007

    Article  Google Scholar 

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Correspondence to Hakan Gürkan.

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Gürkan, H., Güz, Ü. & Yarman, B.S. Modeling of Electrocardiogram Signals Using Predefined Signature and Envelope Vector Sets. EURASIP J. Adv. Signal Process. 2007, 012071 (2007).

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