Low Power Bio-Medical DSP

  • Hyejung Kim
  • Hoi-Jun Yoo
Part of the Integrated Circuits and Systems book series (ICIR)


Many micro-watt power processors have been proposed to improve the processing efficiency for the possible application to Bio Signal Processing [1–5]. Figure 6.1 denotes the energy of recent low power (energy) processors, indicating the trend of the processor’s energy efficiency. The first group is the general purposed processor [1–3, 5]. They have developed for low power operation. Yet, they still require the long operating time, which is the important factor of the energy consumption. Thus, the application specific processor rather than general purpose processor has been developed [4]. Even though it consumes more power than the general purposed processors, the operating time can be reduced remarkably due to the dedicated hardware and instructions. Thus, if the application is clearly defined such as the Bio Signal Processing, it becomes very attractive to improve the energy efficiency.


Compression Ratio Infinite Impulse Response Finite Impulse Response Filter Huffman Code General Purpose Processor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Zhai B, Nazhandali L, Olson J, Reeves A, Minuth M, Helfand R, Pant S, Blaauw D, Austin T (2006) A 2.60pJ/Inst subthreshold sensor processor for optimal energy efficiency. IEEE Proceedings of Symposium of VLSI, Jun 2006Google Scholar
  2. 2.
    De Nil M, Yseboodt L, Bouwens F, Hulzink J, Berekovic M, Huisken J, van Meerbergen J (2007) Ultra low power asip design for wireless sensor node. IEEE Proceedings of ICECSGoogle Scholar
  3. 3.
    Seok M, Hanson S, Lin Y-S, Foo Z, Kim D, Lee Y, Liu N, Sylvester D, Blaauw D (2008) The Phoenix processor: A 30 pW platform for sensor applications. IEEE Proceedings of Symposium of VLSI, Jun 2008Google Scholar
  4. 4.
    Ickes N, Finchelstien D, Chandrakasan AP (2008) A 10-pJ/instruction, 4-MIPS Micropower DSP for sensor application. IEEE Proceedings of ASSCC, Nov 2008Google Scholar
  5. 5.
    Jocke SC,. Bolus1 JF, Wooters SN, Jurik AD, Weaver AC, Blalock TN, Calhoun BH (2009) A 2.6-μW Sub-threshold mixed-signal ECG SoC. IEEE Proceedings of VLSI, Jun 2009Google Scholar
  6. 6.
    de Chazal P, Palreddy S, Tompkins WJ (2004) Automatic classification of hearbeats using ECG morphology and heartbeat interval features. IEEE Trans Biomed Eng 51(7):1196–1206, Jul 2004CrossRefGoogle Scholar
  7. 7.
    Kim BS, Yoo SK, Lee MH (2006) Wavelet-based low-delay ecg compression algorithm for continuous ECG transmission. IEEE Trans Infor Tech Biomed 10(1), Jan 2006Google Scholar
  8. 8.
    Istepanian RSH, Petrosian AA (2000) Optimal Zonal Wavelet-based ECG Data Compression for mobile telecardiology system. IEEE Trans Infor Tech Biomed 4(3), Sep 2000Google Scholar
  9. 9.
    Kim H, Kim Y, Yoo HJ (2008) A low cost quadratic level ecg compression algorithm and its hardware optimization for body sensor network system. IEEE Proceedings of EMBC, Aug 2008Google Scholar
  10. 10.
    Arzeno NM, Deng Z-De, Poon C-S (2008) Analysis of first-derivative based qrs detection algorithms. IEEE Trans Biomed Eng 55(2):478–484, Feb 2008CrossRefGoogle Scholar
  11. 11.
    Zigel Y, Cohen A, Katz A (2000) The Weighted diagnostic distortion (WDD) Measure for ECG signal compression. IEEE Trans Biomed Eng 47(11), Nov 2000Google Scholar
  12. 12.
    Welch TA (1984) A technique for high-performance data compression Computer 17(6):8–19, Jun 1984Google Scholar
  13. 13.
    Health informatics. Standard communication protocol. Computer-assisted electrocardiography. British-Adopted European Standard BS EN 1064:2005Google Scholar
  14. 14.
    Dipersio DA, Barr RC (1985) Evaluation of the fan method of adaptvie sampling on human electrocardiograms. Med Bio Eng Comp 401–410, Sep1985Google Scholar
  15. 15.
    Abenstein JP, Tompkins WJ (1982) A new data reduction algorithm for real time ECG analysis. IEEE Trans Biomed Eng 29(1):43–48, Apr1982CrossRefGoogle Scholar
  16. 16.
    Cox JR, Nolle FM, Fozzard HA, Oliver GC (1968) AZTEC, A preprocessing program for real time ecg rhythm analysis. IEEE Trans Biomed Eng 15(4):128–129, Apr 1968CrossRefGoogle Scholar
  17. 17.
    Mueller WC (1978) Arrhythmia detection program for an ambulatory ecg monitor. Biomed Sci Instrument 14:81–85Google Scholar
  18. 18.
    Hilton ML (1997) Wavelet and wavelet packet compression of electrocardiograms. IEEE Trans Biomed Eng 44(5) May1997Google Scholar
  19. 19.
    Djohan A, et al. (1995) ECG compression using discrete symmetric wavelet transform. IEEE Proceedings of EMBCGoogle Scholar
  20. 20.
    Tai S-C, Sun C-C, Yan W-C (2005) A 2-D ECG Compression method based on wavelet transform and modified SPIHT. IEEE Trans Biomed Eng 52(6):999–1008, Jun 2005CrossRefGoogle Scholar
  21. 21.
    Manikandan MS, et al. (2005) ECG signal compression using discrete sinc interpolation. IEEE Proceedings of ICISIP, Dec 2005Google Scholar
  22. 22.
    Manikandan MS, Dandapat S (2005) ECG signal compression using discrete sinc interpolation. IEEE Proceedings of ICISIP, Dec 2005Google Scholar
  23. 23.
    Olmos S, MillAn M, Garcia J, Laguna P (1996) ECG data compression with the Karhunen-Loeve transform. Comput Cardiol 8–11:253–256, Sep 1996Google Scholar
  24. 24.
  25. 25.
    Fira CM, Goras L (2008) An ECG signals compression method and its validation using NNs. IEEE Trans Biomed Eng 55(4):1319–1326, Apr 2008CrossRefGoogle Scholar
  26. 26.
    Reddy DC (2005) Biomedical signal processing—principles and techniques. McGraw Hill. ISBN 007-124774-2Google Scholar
  27. 27.
    Nazhandali L, et al. (2005) A second-generation sensor network processor with application-driven memory optimization and out-of-order execution. IEEE Proceedings of CASES, pp. 249–256, Sep 2005Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Ultra Low Power and Extreme Electronics Group IMECLeuvenBelgium
  2. 2.Department of Electrical EngineeringKorea Advanced Institute of Science and TechnologyDaejeonRepublic of Korea

Personalised recommendations