iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://unpaywall.org/10.1007/S11265-011-0615-9
Analysis and Design of On-sensor ECG Processors for Realtime Detection of Cardiac Anomalies Including VF, VT, and PVC | Journal of Signal Processing Systems Skip to main content
Springer Nature Link
Log in

Analysis and Design of On-sensor ECG Processors for Realtime Detection of Cardiac Anomalies Including VF, VT, and PVC

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

Cardiovascular disease remains the main cause of death, and great efforts are spent on the design of ECG (electrocardiogram) body sensors these years. Essential components such as analog frontend and wireless transceivers have been integrated on a compact IC with micro-Watt power consumption. To provide timely warning against the fatal vascular signs, based on the Chaotic Phase Space Differential (CPSD) algorithm, heterogeneous VLSI processors are implemented and integrated to extract the abnormal ECG characteristics for VF (Ventricular Fibrillation), VT (Ventricular Tachycardia) and PVC (Premature Ventricular Contraction). The on-sensor processing reduces 98.0% power of wireless data transmission for raw ECG signals. The application specific processor is designed to accelerate CPSD algorithm with 1.7μW power while the OpenRISC is integrated to provide the system flexibility. The architecture is realized on the FPGA platform to demonstrate the detection of the abnormal ECG signals in realtime.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7

Similar content being viewed by others

References

  1. de Vreede-Swagemakers, J. J., et al. (1997). Out-of-hospital cardiac arrest in the 1990’s: A population-based study in the Maastricht area on incidence, characteristics and survival. Journal of the American College of Cardiology, 30, 1500–1505.

    Article  Google Scholar 

  2. Abidi, S. S. R. (2001). An intelligent tele-healthcare environment offering person-centric and wellness-maintenance services. Journal of Medical Systems, 25(3), 147–65.

    Article  Google Scholar 

  3. Yang, G. Z. (2006). Body sensor networks. Springer-Verlag.

  4. Liu, C. S., et al. (2010). The differential method of phase space matrix for AF/VF discrimination application. Medical Engineering & Physics, 32(5), 444–453.

    Article  Google Scholar 

  5. Chan, P. K., et al. (2004). A CMOS chopper-stabilized differential difference amplifier for biomedical integrated circuits. In The 2004 47th midwest symposium on circuits and systems, 2004. MWSCAS ’04. (Vol. 3, pp. iii–33–6).

  6. Lim, T., et al. (2004). A low-power and low-offset CMOS front-end amplifier for portable EEG acquisition system. In IEEE international workshop on biomedical circuits and systems, 2004 (pp. S1/1–17–20).

  7. Wu, H., et al. (2005). A low-voltage low-noise CMOS instrumentation amplifier for portable medical monitoring systems. In The 3rd international IEEE-NEWCAS conference, 2005 (pp. 295–298).

  8. Ng, K. A., et al. (2005). A CMOS analog front-end IC for portable EEG/ECG monitoring applications. IEEE Transactions on Circuits and Systems I: Regular Papers, 52(11), 2335–2347.

    Article  Google Scholar 

  9. Yazicioglu, R. F., et al. (2006). A 60 spl μW 60 nV/Hz readout front-end for portable biopotential acquisition systems. In IEEE international solid-state circuits conference, 2006. ISSCC 2006. Digest of Technical Papers (pp. 109–118).

  10. Yazicioglu, R. F., et al. (2006). Low-power low-noise 8-channel EEG front-end ASIC for ambulatory acquisition systems. In Proceedings of the 32nd European solid-state circuits conference, 2006. ESSCIRC 2006 (pp. 247–250).

  11. Denison, T., et al. (2007). A 2.2 μW 94nV/\(\sqrt{}\)Hz, chopper-stabilized instrumentation amplifier for EEG detection in chronic implants. In IEEE international solid-state circuits conference, 2007. ISSCC 2007. Digest of Technical Papers (pp. 162–594).

  12. Yates, D. C., et al. (2006). An ultra low power low noise chopper amplifier for wireless BEG. In 49th IEEE international midwest symposium on circuits and systems, 2006. MWSCAS ’06. (Vol. 2, pp. 449–452).

  13. Yates, D., et al. (2007). A low-voltage low-power front-end for wearable EEG systems. In 29th annual international conference of the IEEE engineering in medicine and biology society, 2007. EMBS 2007 (pp. 5282–5285).

  14. Wattanapanitch, W., et al. (2007). An energy-efficient micropower neural recording amplifier. IEEE Transactions on Biomedical Circuits and Systems, 1(2), 136–147.

    Article  Google Scholar 

  15. Yazicioglu, R. F., et al. (2008) A 200 μW eight-channel acquisition ASIC for ambulatory EEG systems. In IEEE international solid-state circuits conference, 2008. ISSCC 2008. Digest of Technical Papers (pp. 164–603).

  16. Kim, H. J., et al. (2008). A low energy bio sensor node processor for continuous healthcare monitoring system. In Proc. IEEE Asian solid-state circuits conference (pp. 317–320).

  17. Tolbert, J. R., et al. (2010). A low power system with adaptive data compression for wireless monitoring of physiological signals and its application to wireless electroencephalography. In 11th international symposium on quality electronic design (ISQED), 2010 (pp. 333–341).

  18. Tolbert, J. R., et al. (2009). An accuracy aware low power wireless eeg unit with information content based adaptive data compression. In Engineering in medicine and biology society, 2009. EMBC 2009. Annual International Conference of the IEEE (pp. 5417–5420).

  19. Bonfanti, A., et al. (2010). A multi-channel low-power system-on-chip for single-unit recording and narrowband wireless transmission of neural signal. In Engineering in medicine and biology society (EMBC), 2010 annual international conference of the IEEE (pp. 1555–1560).

  20. Wong, A. C., et al. (2008). A 1V, micropower system-on-chip for vital-sign monitoring in wireless body sensor networks. In ISSCC dig. tech. papers (pp. 138–602).

  21. Jocke, S. C., et al. (2009). A 2.6 μW sub-threshold mixed-signal ECG SoC. In IEEE symposium on VLSI circuits (pp. 60–61).

  22. Liu, C. S., et al. (2009). Chaotic phase space differential (CPSD) algorithm for real-time detection of VF, VT, and PVC ECG signals. In 4th European conference of the international federation for medical and biological engineering (Vol. 22, pp. 18–21). Springer, Berlin, Heidelberg.

    Chapter  Google Scholar 

  23. Zheng, Z. J., et al. (2001). Sudden cardiac death in the United States, 1989 to 1998. Circulation, 104, 2158–2163.

    Article  Google Scholar 

  24. Massachusetts Institute of Technology (1999). MIT-BIH arrhythmia database. Available online: http://www.physionet.org/physiobank/database/mitdb.

  25. Goldberger, A. L., et al. (2000). Physiobank, physiotoolkit, and physionet: Components of a new research resource for complex physiologic signals. Circulation, 101(23), e215–20.

    Google Scholar 

  26. Massachusetts Institute of Technology (1999). CU database. Available online: http://www.physionet.org/physiobank/database/cudb.

  27. Nolle, F. M., et al. (1986). CREI-GARD, a new concept in computerized arrhythmia monitoring systems. Computers in Cardiology, 13, 515–518.

    Google Scholar 

  28. Massachusetts Institute of Technology (1999). SCDH database. Online: http://www.physionet.org/pn3/sddb.

  29. Goldberger, A. L., et al. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 101(23), e215–e220.

    Google Scholar 

  30. Asl, B. M., et al. (2008) Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal. Artificial Intelligence in Medicine, 44, 51–64.

    Article  Google Scholar 

  31. U.S. Food and Drug Administration (1999). CFR - code of federal regulations title 21. http://www.accessdata.fsda.gov/cripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?fr=320.38.

Download references

Author information

Authors and Affiliations

  1. Graduate Institute of Electronics Engineering, National Taiwan University, Taipei, Taiwan

    Hong-Hui Chen, Cheng-Yi Chiang, Tung-Chien Chen, Yu-Jie Huang, Shey-Shi Lu & Liang-Gee Chen

  2. Graduate Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan

    Chien-Sheng Liu & Chii-Wann Lin

Authors
  1. Hong-Hui Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cheng-Yi Chiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tung-Chien Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chien-Sheng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yu-Jie Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shey-Shi Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chii-Wann Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Liang-Gee Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hong-Hui Chen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, HH., Chiang, CY., Chen, TC. et al. Analysis and Design of On-sensor ECG Processors for Realtime Detection of Cardiac Anomalies Including VF, VT, and PVC. J Sign Process Syst 65, 275–285 (2011). https://doi.org/10.1007/s11265-011-0615-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-011-0615-9

Keywords

Search

Navigation