Circuits, Systems, and Signal Processing

, Volume 38, Issue 1, pp 329–355 | Cite as

IIR Filter Architectures with Truncation Error Feedback for ECG Signal Processing

  • Gustavo Ott
  • Eduardo A. C. Costa
  • Sérgio J. M. Almeida
  • Mateus B. FonsecaEmail author


This work proposes fixed-point hardware architectures for two IIR filters, focusing on design specifications for ECG signal processing, using the truncation error feedback (TEF) to attenuate errors caused by truncation operations inside these recursive structures. The TEF is represented by modulo operation followed by a unit-delay operator and multiplication by a coefficient. In this work, the proposed TEF core consists of a hardware structure based on delay, right-shift and modulo operations. The TEF approach is applied to sequential and parallel IIR filter architectures with fixed and adaptive coefficients. The first structure comprises a first-order high-pass filter applied to attenuate low frequencies of the electrocardiogram (ECG) signal. The second one is a second-order infinite impulse response (IIR) adaptive notch filter used to attenuate power line interference signals. All dedicated architectures were described and simulated using VHDL and synthesized in Cadence environment using the 45 nm Nangate Open Cell Library to verify the results of the area, delay and power metrics. A simulated ECG signal was used as input to check the functionality of the filters. Our results indicate that the TEF approach was useful for both high-pass filter (HPF) and adaptive notch filter (ANF), and it can be a significant strategy to meet design specifications and dynamic performance of fixed-point digital filters. For the synthesis analysis, both HPF and ANF sequential filters had lower power and cell area figures but presented higher normalized power per sample and delay. In summation, the TEF approach enabled the use of fixed-point filters for ECG filtering without degrading their dynamic performance or increasing noise caused by truncation.


IIR filters Fixed-point arithmetic ECG Biomedical signal processing 


  1. 1.
    L. Aksoy, E.A.C. Costa, P. Flores, J. Monteiro, Multiple tunable constant multiplications: algorithms and applications, in IEEE/ACM International Conference on Computer-Aided Design, 2012 (IEEE, 2012), pp. 473–479Google Scholar
  2. 2.
    L. Aksoy, E.A.C. Costa, P. Flores, J.C. Monteiro, Finding the optimal tradeoff between area and delay in multiple constant multiplications. Microprocess. Microsyst. 35(8), 729–741 (2011)CrossRefGoogle Scholar
  3. 3.
    B. Bomar, Finite wordlength effects, in The Digital Signal Processing Handbook, Chapter 3, ed. by Vijay K. Madisetti (CRC Press, Boca Raton, 1998), pp. 3-1–3-18Google Scholar
  4. 4.
    Cadence Design Systems, Inc. Cadence Encounter RTL Compiler v. 8.10 (2016)Google Scholar
  5. 5.
    J. Carr, J. Brown, Introduction to Biomedical Equipment Technology, Chapter 8 (Prentice Hall, Englewood Cliffs, 2000), pp. 197–233Google Scholar
  6. 6.
    J. Dattorro, The implementation of recursive digital filters for high-fidelity audio. J. Audio Eng. Soc. 36(11), 851–878 (1988)Google Scholar
  7. 7.
    K. Gaikwad, M. Chavan, Design and implementation of digital Butterworth IIR filter using Xilinx system generator for noise reduction in ECG signal. Int. J. Signal Process. 2, 86–90 (2017)Google Scholar
  8. 8.
    S. Gawande, S. Bhujbal, High speed IIR notch filter using pipelined technique. Int. J. Adv. Res. Electr. Electron. Instrum. Eng. 6, 501–508 (2017)Google Scholar
  9. 9.
    D. Grover, J. Deller Jr., Digital Signal Processing and the Microcontroller, Chapter 7 (Prentice Hall, Englewood Cliffs, 1998)Google Scholar
  10. 10.
    W. Higgins, D. Munson, Noise reduction strategies for digital filters: error spectrum shaping versus the optimal linear state-space formulation. IEEE Trans. Acoust. Speech Signal Process. 30(6), 963–973 (1982)CrossRefGoogle Scholar
  11. 11.
    Hinamoto T, A. Doi, W.-S. Lu, Roundoff noise minimization in state-space discrete-time systems using joint optimization of high-order error feedback and realization. IEEE Trans. Circuits and Syst. I Regul. Pap. 61(12), 3460–3468 (2014)CrossRefGoogle Scholar
  12. 12.
    T.I. Laakso, I.O. Hartimo, Noise reduction in recursive digital filters using high-order error feedback. IEEE Trans. Signal Process. 40(5), 1096–1107 (1992)CrossRefzbMATHGoogle Scholar
  13. 13.
    T.I. Laakso, J. Ranta, S.J. Ovaska, Design and implementation of efficient IIR notch filters with quantization error feedback. IEEE Trans. Instrum. Meas. 43(3), 449–456 (1994)CrossRefGoogle Scholar
  14. 14.
    NanGate, Inc. Nangate 45 nm Open Cell Library (2008)Google Scholar
  15. 15.
    S. Ohno, Y. Wakasa, M. Nagata, Optimal error feedback filters for uniform quantizers at remote sensors, in 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 2015), pp. 3866–3870Google Scholar
  16. 16.
    G. Ott, E. Costa, S. Almeida, M. Fonseca, Exploiting architectural solutions for IIR filter architecture with truncation error feedback, in 2016 IEEE 7th Latin American Symposium on Circuits and Systems (LASCAS) (IEEE, 2016), pp. 375–378Google Scholar
  17. 17.
    D. Prutchi, M. Norris, Design and Development of Medical Electronic Instrumentation: A Practical Perspective of the Design, Construction, and Test of Medical Devices, Chapter 2 (Wiley, New York, 2005), pp. 41–96CrossRefGoogle Scholar
  18. 18.
    A. Rahmatillah, Ataulkarim, IIR digital filter design for powerline noise cancellation of ECG signal using Arduino platform. J. Phys. Conf. Ser. 853, 1–9 (2017)CrossRefGoogle Scholar
  19. 19.
    N. Singhi, S. Ayub, P. Saini, Design of digital IIR filter for noise reduction in ECG signal, in 2013 5th International Conference on Computational Intelligence and Communication Networks (IEEE, 2013), pp. 171–176Google Scholar
  20. 20.
    L. Tan, J. Jiang, Digital Signal Processing: Fundamentals and Applications (Elsevier, Amsterdam, 2007)Google Scholar
  21. 21.
    X. Tan, H. Zhang, A novel adaptive IIR notch filter for frequency estimation and tracking, in 2010 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), vol. 5 (IEEE, 2010), pp. 259–263Google Scholar
  22. 22.
    R. Tarik, S. Ohno, M. Nagahara, Synthesis of IIR error feedback filters for modulators using approximation, in 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA) (IEEE, 2016), pp. 1–5Google Scholar
  23. 23.
    M. Tavares, Development of an ECG and cardiac variability monitor for use in operating room and ICU. Technical report, Biomedical Engineering Laboratory, Catholic University of Pelotas. FAPERGS (2003) (Unpublished) Google Scholar
  24. 24.
    N.M. Verulkar, P.H. Zope, S.R. Suralkar, Filtering techniques for reduction of power line interference in electrocardiogram signals. Int. J. Eng. Res. Technol. 1, 1–7 (2012)CrossRefGoogle Scholar
  25. 25.
    Y. Voronenko, M. Püschel, Multiplierless multiple constant multiplication. ACM Trans. Algorithms (TALG) 3(2), 1–38 (2007)MathSciNetzbMATHGoogle Scholar
  26. 26.
    Z. Wang, C.-W. Pan, Y. Song, C. Sechen, High-throughput digital IIR filter design. J. Algorithms Optim. 2, 15–27 (2014)Google Scholar
  27. 27.
    J. Zhou, G. Li, Plain gradient based direct frequency estimation using second-order constrained adaptive IIR notch filter. Electron. Lett. 40(5), 351–352 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Gustavo Ott
    • 1
  • Eduardo A. C. Costa
    • 1
  • Sérgio J. M. Almeida
    • 1
  • Mateus B. Fonseca
    • 2
    Email author
  1. 1.Graduate Program on Electronic Engineering and ComputingCatholic University of Pelotas (UCPel)PelotasBrazil
  2. 2.Engineering CenterFederal University of Pelotas (UFPel)PelotasBrazil

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