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Self-powered SoC Platform for Analysis and Prediction of Cardiac Arrhythmias

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

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Abstract

The chapter starts by highlighting the severity of cardiovascular disease problem; then it shed the light on the most relevant published research in the area of cardiovascular disease diagnostic. ECG filtering is reviewed, followed by ECG feature extraction technique overview, and ECG feature classification methods are briefly introduced. The chapter concludes by a review of some of the relevant published work on hardware implementation for ECG signal processing systems.

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Bibliography

  1. I. Kuon, J. Rose, Measuring the gap between FPGAS and ASICS. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 26(2), 203–215 (2007)

    Article  Google Scholar 

  2. A.S. Go, D. Mozaffarian, V.L. Roger, E.J. Benjamin, J.D. Berry, M.J. Blaha, S. Dai, E.S. Ford, C.S. Fox, S. Franco, et al., Heart disease and stroke statistics–2014 update: a report from the American Heart Association. Circulation 129(3), e28 (2014)

    Article  Google Scholar 

  3. M. Mneimneh, E. Yaz, M. Johnson, R. Povinelli, An adaptive Kalman filter for removing baseline wandering in ECG signals, in Computers in Cardiology, 2006 (IEEE, 2006), p. 253–256

    Google Scholar 

  4. X. Hu, Z. Xiao, N. Zhang, Removal of baseline wander from ECG signal based on a statistical weighted moving average filter. J. Zhejiang Univ. Sci. C 12(5), 397–403 (2011)

    Article  Google Scholar 

  5. B.-S. Lin, B.-S. Lin, W.-C. Lee, F.-C. Chong, Y.-D. Lin, Removing residual power-line interference using WHT adaptive filter, in Engineering in Medicine and Biology, 2002. 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, 2002. Proceedings of the Second Joint, vol. 1 (IEEE, 2002), p. 155–156

    Google Scholar 

  6. Z. Zhidong, M. Chan, A novel cancellation method of powerline interference in ECG signal based on EMD and adaptive filter, in 2008 11th IEEE International Conference on Communication Technology (2008), p. 517–520

    Google Scholar 

  7. H. Bharath, K. Prabhu, A new LMS based adaptive interference canceller for ECG power line removal, in Biomedical Engineering (ICoBE), 2012 International Conference on (IEEE, 2012), p. 68–73

    Google Scholar 

  8. Z.-D. Zhao, Y.-Q. Chen, A new method for removal of baseline wander and power line interference in ECG signals, in Machine Learning and Cybernetics, 2006 International Conference on (IEEE, 2006), p. 4342–4347

    Google Scholar 

  9. J. Mateo, C. Sánchez, A. Tortes, R. Cervigon, J. Rieta, Neural network based canceller for powerline interference in ECG signals, in Computers in Cardiology, 2008, (IEEE, 2008), pp. 1073–1076

    Google Scholar 

  10. K. Chan, Y. Zhang, Adaptive reduction of motion artifact from photo-plethysmographic recordings using a variable step-size LMS filter, in Sensors, 2002. Proceedings of IEEE, vol. 2 (IEEE, 2002), p. 1343–1346

    Google Scholar 

  11. S. Seyedtabaii, L. Seyedtabaii, Kalman filter based adaptive reduction of motion artifact from photoplethysmographic signal, in Proceedings of World Academy of Science, Engineering and Technology, vol. 27 (Citeseer, 2008)

    Google Scholar 

  12. 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 

  13. A.H. Khandoker, M.H. Imam, J.P. Couderc, M. Palaniswami, J.F. Jelinek, QT variability index changes with severity of cardiovascular autonomic neuropathy. IEEE Trans. Inf. Technol. Biomed. 16(5), 900–906 (2012)

    Article  Google Scholar 

  14. 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 

  15. S. Barro, R. Ruiz, D. Cabello, J. Mira, Algorithmic sequential decision-making in the frequency domain for life threatening ventricular arrhythmias and imitative artefacts: a diagnostic system. J. Biomed. Eng. 11(4), 320–328 (1989)

    Article  Google Scholar 

  16. R.J. Oweis, E.W. Abdulhay, Seizure classification in EEG signals utilizing Hilbert-Huang transform. Biomed. Eng. Online 10(1), 38 (2011)

    Article  Google Scholar 

  17. I. Murthy, G. Prasad, Analysis of ECG from pole-zero models. IEEE Trans. Biomed. Eng. 39(7), 741–751 (1992)

    Article  Google Scholar 

  18. S.S. Mehta, N.S. Lingayat, Detection of P and T-Waves in Electrocardiogram (2008)

    Google Scholar 

  19. X.-S. Zhang, Y.-S. Zhu, N.V. Thakor, Z.-Z. Wang, Detecting ventricular tachycardia and fibrillation by complexity measure. IEEE Trans. Biomed. Eng. 46(5), 548–555 (1999)

    Article  Google Scholar 

  20. H. Li, W. Han, C. Hu, M.-H. Meng, Detecting ventricular fibrillation by fast algorithm of dynamic sample entropy, in Robotics and Biomimetics (ROBIO), 2009 IEEE International Conference on (IEEE, 2009), p. 1105–1110

    Google Scholar 

  21. S. Caswell Schuckers, Approximate entropy as a measure of morphologic variability for ventricular tachycardia and fibrillation, in Computers in Cardiology 1998 (IEEE, Long Beach, 1998), p. 265–268

    Google Scholar 

  22. 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 

  23. I. Jekova, Shock advisory tool: detection of life-threatening cardiac arrhythmias and shock success prediction by means of a common parameter set. Biomed. Signal Process. Control 2(1), 25–33 (2007)

    Article  Google Scholar 

  24. Y. Ma, G. Guo, Support Vector Machines Applications (Springer, 2014)

    Google Scholar 

  25. K. Polat, B. Akdemir, S. Güneş, Computer aided diagnosis of ECG data on the least square support vector machine. Digital Signal Process. 18(1), 25–32 (2008)

    Article  Google Scholar 

  26. E.D. Übeyli, Support vector machines for detection of electrocardiographic changes in partial epileptic patients. Eng. Appl. Artif. Intell. 21(8), 1196–1203 (2008)

    Article  Google Scholar 

  27. B.M. Asl, S.K. Setarehdan, M. Mohebbi, Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal. Artif. Intell. Med. 44(1), 51–64 (2008)

    Article  Google Scholar 

  28. J.S. Yadav, M. Yadav, A. Jain, Artificial neural network. Int. J. Sci. Res. Educ 1(06), 108–117 (2014)

    Google Scholar 

  29. S.M. Jadhav, S.L. Nalbalwar, A.A. Ghatol, Generalized feedforward neural network based cardiac arrhythmia classification from ECG signal data, in Advanced Information Management and Service (IMS), 2010 6th International Conference on (IEEE, 2010), p. 351–356

    Google Scholar 

  30. B. Anuradha, V.V. Reddy, ANN for classification of cardiac arrhythmias. ARPN J. Eng. Appl. Sci. 3(3), 1–6 (2008)

    Google Scholar 

  31. E.D. Übeyli, Combining recurrent neural networks with eigenvector methods for classification of ECG beats. Digital Signal Process. 19(2), 320–329 (2009)

    Article  Google Scholar 

  32. S.R. Eddy, What is a hidden Markov model? Nat. Biotechnol. 22(10), 1315–1316 (2004)

    Article  Google Scholar 

  33. W. Cheng, K. Chan, Classification of electrocardiogram using hidden markov models, in Engineering in Medicine and Biology Society, 1998. Proceedings of the 20th Annual International Conference of the IEEE (IEEE, 1998), p. 143–146

    Google Scholar 

  34. D.M. Witten, R. Tibshirani, Penalized classification using fisher’s linear discriminant. J. R. Stat. Soc. Ser. B Stat. Methodol. 73(5), 753–772 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  35. J.-S. Wang, W.-C. Chiang, Y.-T. Yang, Y.-L. Hsu, An effective ECG arrhythmia classification algorithm, in Bio-Inspired Computing and Applications (Springer, 2012), p. 545–550

    Google Scholar 

  36. M. Elgendi, M. Jonkman, F. De Boer, Premature atrial complexes detection using the fisher linear discriminant, in Cognitive Informatics, 2008. ICCI 2008. 7th IEEE International Conference on (IEEE, 2008), p. 83–88

    Google Scholar 

  37. Y.-C. Yeh, W.-J. Wang, C.W. Chiou, Cardiac arrhythmia diagnosis method using linear discriminant analysis on ECG signals. Measurement 42(5), 778–789 (2009)

    Article  Google Scholar 

  38. S. Kharya, S. Agrawal, S. Soni, Naive Bayes classifiers: a probabilistic detection model for breast cancer. Int. J. Comput. Appl. 92(10), 26–31 (2014)

    Google Scholar 

  39. J. Huang, J. Lu, C.X. Ling, Comparing naive bayes, decision trees, and svm with auc and accuracy, in Data Mining, 2003. ICDM 2003. Third IEEE International Conference on (IEEE, 2003), p. 553–556

    Google Scholar 

  40. M. Wiggins, A. Saad, B. Litt, G. Vachtsevanos, Evolving a Bayesian classifier for ECG-based age classification in medical applications. Appl. Soft Comput. 8(1), 599–608 (2008)

    Article  Google Scholar 

  41. A.M. Alturki, A.M. Al-Ghamdi, K. Daqrouq, R. Al-Hmouz, Application of ECG arrhythmia classification by means of Bayesian theorem. J. Appl. Sci. 14(2), 165–170 (2014)

    Article  Google Scholar 

  42. L.A. Zadeh, Fuzzy sets. Inf. Control. 8(3), 338–353 (1965)

    Article  MATH  Google Scholar 

  43. M.G. Voskoglou, Case-based reasoning: a recent theory for problem-solving and learning in computers and people, in The Open Knowledge Society. A Computer Science and Information Systems Manifesto (Springer, Berlin, Heidelberg, 2008), p. 314–319

    Google Scholar 

  44. A. Sengur, I. Turkoglu, A hybrid method based on artificial immune system and fuzzy k-NN algorithm for diagnosis of heart valve diseases. Expert Syst. Appl. 35(3), 1011–1020 (2008)

    Article  Google Scholar 

  45. T.-F. Chiu. C-W. Chu, J.-L. Wu, A hybrid case-based reasoning approach for the electrocardiogram diagnosis, in Proceeding of the 7th World Multiconference on Systemics (IEEE, 2003), p. 93–98

    Google Scholar 

  46. B.-Y. Shiu, S.-W. Wang, Y.-S. Chu, T.-H. Tsai, Low-power low-noise ECG acquisition system with dsp for heart disease identification, in Biomedical Circuits and Systems Conference (BioCAS), 2013 IEEE (IEEE, 2013), p. 21–24

    Google Scholar 

  47. 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 

  48. H. Kim, R.F. Yazicioglu, P. Merken, C. Van Hoof, H.-J. Yoo, ECG signal compression and classification algorithm with quad level vector for ECG holter system. IEEE Trans. Inf. Technol. Biomed. 14(1), 93–100 (2010)

    Article  Google Scholar 

  49. M. Nambakhsh, V. Tavakoli, N. Sahba et al., FPGA-core defibrillator using wavelet-fuzzy ECG arrhythmia classification, in Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE (IEEE, 2008), p. 2673–2676

    Google Scholar 

  50. 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 

  51. 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 

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Authors and Affiliations

  1. Department of Electronic Engineering, Khalifa University of Science, Technology and Research, Abu Dhabi, United Arab Emirates

    Hani Saleh & Baker Mohammad

  2. Department of Electrical and Computer Engineering, Khalifa University of Science, Technology and Research, Abu Dhabi, United Arab Emirates

    Nourhan Bayasi

  3. Department of Electrical and Computer Engineering Department, Khalifa University of Science, Technology and Research, Abu Dhabi, United Arab Emirates

    Mohammed Ismail

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  1. Hani Saleh
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  4. Mohammed Ismail
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Saleh, H., Bayasi, N., Mohammad, B., Ismail, M. (2018). Literature Review. 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_2

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  • DOI: https://doi.org/10.1007/978-3-319-63973-4_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63972-7

  • Online ISBN: 978-3-319-63973-4

  • eBook Packages: EngineeringEngineering (R0)

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