Skip to main content

Part of the book series: Analog Circuits and Signal Processing ((ACSP))

  • 442 Accesses

Abstract

The chapter discusses the performance of the presented system. The high-level simulation results are presented, and then a detailed comparison with the published work is given. The chapter is concluded by presenting the obtained results from the first chip tapeout of the introduced system.

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

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Bibliography

  1. A.L. Goldberger, L.A. Amaral, L. Glass, J.M. Hausdorff, P.C. Ivanov, R.G. Mark, J.E. Mietus, G.B. Moody, C.-K. Peng, H.E. Stanley, Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)

    Article  Google Scholar 

  2. P.S. Hamilton, W.J. Tompkins, Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database. IEEE Trans. Biomed. Eng. 33(12), 1157–1165 (1986)

    Article  Google Scholar 

  3. J.P. Martínez, R. Almeida, S. Olmos, A.P. Rocha, P. Laguna, A wavelet-based ECG delineator: Evaluation on standard databases. IEEE Trans. Biomed. Eng. 51(4), 570–581 (2004)

    Article  Google Scholar 

  4. A. Cost, G.G. Cano, QRS detection based on hidden markov modeling, in Engineering in Medicine and Biology Society, 1989. Images of the Twenty-First Century., Proceedings of the Annual International Conference of the IEEE Engineering in (IEEE, 1989), p. 34–35

    Google Scholar 

  5. E.B. Mazomenos, D. Biswas, A. Acharyya, T. Chen, K. Maharatna, J. Rosengarten, J. Morgan, N. Curzen, A low-complexity ECG feature extraction algorithm for mobile healthcare applications. IEEE J. Biomed. Health Inform. 17(2), 459–469 (2013)

    Article  Google Scholar 

  6. E. Mazomenos, T. Chen, A. Acharyya, A. Bhattacharya, J. Rosengarten, K. Maharatna, A time-domain morphology and gradient based algorithm for ECG feature extraction, in Industrial Technology (ICIT), 2012 IEEE International Conference on (IEEE, 2012), p. 117–122

    Google Scholar 

  7. R.V. Andreão, B. Dorizzi, J. Boudy, ECG signal analysis through hidden Markov models. IEEE Trans. Biomed. Eng. 53(8), 1541–1549 (2006)

    Article  Google Scholar 

  8. Y. Sun, K.L. Chan, S.M. Krishnan, Characteristic wave detection in ECG signal using morphological transform. BMC Cardiovasc. Disord. 5(1), 28 (2005)

    Article  Google Scholar 

  9. P. Laguna, R.G. Mark, A. Goldberg, G.B. Moody, A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG, in Computers in Cardiology 1997 (IEEE, Piscataway, 1997), pp. 673–676

    Google Scholar 

  10. P. De Chazal, M. O’Dwyer, R.B. Reilly, Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 51(7), 1196–1206 (2004)

    Article  Google Scholar 

  11. A. Amann, R. Tratnig, K. Unterkofler, Detecting ventricular fibrillation by time-delay methods. IEEE Trans. Biomed. Eng. 54(1), 174–177 (2007)

    Article  Google Scholar 

  12. F. Alonso-Atienza, E. Morgado, L. Fernandez-Martinez, A. García-Alberola, J. Rojo-Alvarez, Detection of life-threatening arrhythmias using feature selection and support vector machines. I.E.E.E. Trans. Biomed. Eng. 61(3), 832–840 (2014)

    Article  Google Scholar 

  13. Q. Li, C. Rajagopalan, G. Clifford, Ventricular fibrillation and tachycardia classification using machine learning method. I.E.E.E. Trans. Biomed. Eng. 61(6), 1607–1613 (2013)

    Google Scholar 

  14. O. Sayadi, M.B. Shamsollahi, G.D. Clifford, Robust detection of premature ventricular contractions using a wave-based bayesian framework. IEEE Trans. Biomed. Eng. 57(2), 353–362 (2010)

    Article  Google Scholar 

  15. S. Lee, J. Hong, K. Lin, C. Hsieh, M. Liang, S. Chien, Low-power wireless ECG acquisition and classification system for body sensor networks. IEEE J. Biomed. Health Inform. 19(1), 236–246 (2015)

    Article  Google Scholar 

  16. Y.-P. Chen, D. Jeon, Y. Lee, Y. Kim, Z. Foo, I. Lee, N.B. Langhals, G. Kruger, H. Oral, O. Berenfeld, et al., An injectable 64 nw ECG mixed-signal SoC in 65 nm for arrhythmia monitoring. IEEE J. Solid State Circuits 50(1), 375–390 (2015)

    Article  Google Scholar 

  17. H. Kim, R.F. Yazicioglu, T. Torfs, P. Merken, H.-J. Yoo, C. Van Hoof, A low power ECG signal processor for ambulatory arrhythmia monitoring system, in VLSI Circuits (VLSIC), 2010 IEEE Symposium on (IEEE, 2010), p. 19–20

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Cite this chapter

Saleh, H., Bayasi, N., Mohammad, B., Ismail, M. (2018). Performance and Results. In: Self-powered SoC Platform for Analysis and Prediction of Cardiac Arrhythmias . Analog Circuits and Signal Processing. Springer, Cham. https://doi.org/10.1007/978-3-319-63973-4_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-63973-4_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63972-7

  • Online ISBN: 978-3-319-63973-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics