Advertisement

Signal Processing Methods for Identification of Sudden Cardiac Death

  • Reeta DeviEmail author
  • Hitender Kumar Tyagi
  • Dinesh Kumar
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 958)

Abstract

Sudden cardiac death (SCD) is defined as sudden natural death occurring within few minutes to an hour from the onset of symptoms due to known or unknown cardiac cause. An early stage prediction or identification of SCD has become a major challenge among the medical fraternity to save the life of SCD affected person. For prediction of sudden cardiac death, three distinct kinds of markers viz. markers of structural heart disease, markers of electrical instability and markers of abnormal autonomic balance have been devised. Based on these markers, many signal processing techniques like signal averaged electrocardiography, extraction of longer QRS duration, identification of QT-dispersion, and feature extraction from T-wave alternans, heart rate variability (HRV), and heart rate turbulence (HRT) with data mining, statistical and machine learning algorithms are fused together to validate the SCD prediction accuracy. But despite significant advances in the engineering research and medical science, there is no standard technique adopted to identify the SCD at an early stage which limits the fusion of any method into a medical product due to its own limitations. This paper is therefore, designed to discuss different signal processing methods based on these three markers in order to predict sudden cardiac death at an early and alarming stage. The contents embodied in this paper would benefit the community of the research groups designing signal processing algorithms for early prediction of SCD which will help the clinicians to save the precious life of the SCD affected patients.

Keywords

SCD Signal processing Structural heart disease Electrical instability Autonomic system 

Notes

Acknowledgments

This present work is carried out with the help of library and research resource facilities provided by Kurukshetra University, Kurukshetra, Haryana, India.

Disclosure

The authors do not have any conflict of interest to declare.

Statement

Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. 1.
    Khor, G.L.: Cardiovascular epidemiology in Asia-Pacific region. Asia Pac. J. Clin. Nutr. 10, 76–80 (2001)CrossRefGoogle Scholar
  2. 2.
    Stecker, E.C., et al.: Public health burden of sudden cardiac death in the United States. Circ. Arrhythm. Electrophysiol. 7, 212e7 (2014)CrossRefGoogle Scholar
  3. 3.
    Ladich, E., Virmani, R., Burke, A.: Sudden cardiac death not related to coronary atherosclerosis. Toxicol. Pathol. 34, 52 (2006)CrossRefGoogle Scholar
  4. 4.
    Chug, S.S.: Early identification of risk factors for sudden cardiac death. Nat. Rev. Cardiol. 7, 318–326 (2010)CrossRefGoogle Scholar
  5. 5.
    Hua, W., et al.: Preventive effectiveness of implantable cardioverter defibrillator in reducing sudden cardiac death in the Chinese population: a multicenter trial of ICD therapy versus non-ICD therapy. J. Cardiovasc. Electrophysiol. 23, S5–S9 (2012)CrossRefGoogle Scholar
  6. 6.
    Fam, J.M., Ching, C.K.: Review on non-invasive risk stratification of sudden cardiac death. Proc. Singapore Healthc. 20(4), 263–278 (2011)CrossRefGoogle Scholar
  7. 7.
    Sanders, G.D., Hlatky, M.A., Owens, D.K.: Cost- effectiveness of implantable cardioverter-defibrillators. N. Engl. J. Med. 353, 1471–1480 (2005)CrossRefGoogle Scholar
  8. 8.
    Goldberger, A.L., et al.: PhysioBank, PhysioToolkit and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101, e215–e220 (2000)Google Scholar
  9. 9.
    Yunfeng, W., Rangayyan, R.M., Zhou, Y., Ng, S.-C.: Filtering electrocardiographic signals using an unbiased and normalized adaptive noise reduction system. Med. Eng. Phys. 31(1), 17–26 (2009)CrossRefGoogle Scholar
  10. 10.
  11. 11.
    Goldberger, J.J., et al.: American Heart Association/American College of Cardiology Foundation/Heart Rhythm Society scientific statement on noninvasive risk stratification techniques for identifying patients at risk for sudden cardiac death: a scientific statement from the American Heart Association Council on Clinical Cardiology Committee on Electrocardiography and Arrhythmias and Council on Epidemiology and Prevention. Circulation 118(14), 1497–1518 (2008)CrossRefGoogle Scholar
  12. 12.
    Foley, T.A., et al.: Measuring left ventricular ejection fraction-techniques and potential pitfalls. Eur. Cardiol. 8(2), 108–114 (2012)CrossRefGoogle Scholar
  13. 13.
    John, R.M., et al.: Ventricular arrhythmias and sudden cardiac death. Lancet 380(9852), 1520–1529 (2012)CrossRefGoogle Scholar
  14. 14.
    Baldzizhar, A., Manuylova, E., Marchenko, R., Kryvalap, Y., Carey, M.G.: Ventricular tachycardias: characteristics and management. Crit. Care Nurs. Clin. North Am. 28(3), 317–329 (2016)CrossRefGoogle Scholar
  15. 15.
    Zareba, W., Moss, A.J., Le, C.S.: Dispersion of ventricular repolarization and arrhythmic cardiac death in coronary artery disease. Am. J. Cardiol. 74, 550–553 (1994)CrossRefGoogle Scholar
  16. 16.
    Ng, G.A.: Treating patients with ventricular ectopic beats. Heart 92(11), 1707–1712 (2006)CrossRefGoogle Scholar
  17. 17.
    Kurl, S., et al.: Duration of QRS complex in resting electrocardiogram is a predictor of sudden cardiac death in men. Circulation 125(21), 12588–12594 (2012)CrossRefGoogle Scholar
  18. 18.
    Verrier, R.L., et al.: Microvolt T-wave alternans: physiological basis, methods of measurement, and clinical utility—consensus guideline by International Society for Holter and Noninvasive Electrocardiology. J. Am. Coll. Cardiol. 58(13), 1309–1324 (2011)CrossRefGoogle Scholar
  19. 19.
    El-Menyar, A., Asaad, N.: T-wave alternans and sudden cardiac death. Crit. Pathways Cardiol. 7, 21–28 (2008)CrossRefGoogle Scholar
  20. 20.
    Pham, Q., Quan, K.J., Rosenbaum, D.S.: T-wave alternans: marker, mechanism, and methodology for predicting sudden cardiac death. J. Electrocardiol. 36(1), 75–81 (2003)CrossRefGoogle Scholar
  21. 21.
    Stein, P.K., Sanghavi, D., Sotoodehnia, N., et al.: Association of Holter-based measures including T-wave alternans with risk of sudden cardiac death in the community-dwelling elderly: the Cardiovascular Health Study. J. Electrocardiol. 43, 251–259 (2010)CrossRefGoogle Scholar
  22. 22.
    Hilfiker, G., Schoenenberger, A.W., Erne, P., Kobza, R.: Utility of electrophysiological studies to predict arrhythmic events. World J. Cardiol. 7, 344–350 (2015)CrossRefGoogle Scholar
  23. 23.
    Malik, M., Batchvarov, V.N.: Measurement, interpretation and clinical potential of QT dispersion. J. Am. Coll. Cardiol. 36(6), 1749–1766 (2000)CrossRefGoogle Scholar
  24. 24.
    Spargias, K.S., Lindsay, S.J., et al.: QT dispersion as a predictor of long-term mortality in patients with acute myocardial infarction and clinical evidence of heart failure. Eur. Heart J. 20, 1158–1165 (1999)CrossRefGoogle Scholar
  25. 25.
    Maron, B.J., Doerer, J.J., Haas, T.S., Tierney, D.M., Mueller, F.O.: Sudden deaths in young competitive athletes: analysis of 1866 deaths in the United States, 1980–2006. Circulation 119(8), 1085–1092 (2009)CrossRefGoogle Scholar
  26. 26.
    Acharya, U.R., Fujita, H., Sudarshan, V.K., Ghista, D.N., Lim, W.J.E., Koh, J.E.W.: Automated prediction of sudden cardiac death risk using Kolmogorov complexity and recurrence quantification analysis features extracted from HRV signals. In: SMC 2015 Hong Kong, pp. 1110–1115. IEEE (2015)Google Scholar
  27. 27.
    Ebrahimzadeh, E., Pooyan, M.: Early detection of sudden cardiac death by using classical linear techniques and time-frequency methods on electrocardiogram signals. J. Biomed. Sci. Eng. 4, 699–706 (2011)CrossRefGoogle Scholar
  28. 28.
    Ebrahimzadeh, E., Mohammad, P., Ahmad, B.: A novel approach to predict sudden cardiac death (SCD) using non-linear and time-frequency analysis from HRV signals. PLoS ONE 9(2), e81896 (2014)CrossRefGoogle Scholar
  29. 29.
    Fujita, H., et al.: Sudden cardiac death (SCD) prediction based on non-linear heart rate variability features and SCD index. Appl. Soft Comput. 43(510), 519 (2016)Google Scholar
  30. 30.
    Murukesan, L., Murugappan, M., Omar, I., Khatun, S., Murugappan, S.: Time domain features based sudden cardiac arrest prediction using machine learning algorithms. J. Med. Imaging Health Inf. 5, 1267–1271 (2015)CrossRefGoogle Scholar
  31. 31.
    Murukesan, L., Murugappan, M., Iqbal, M., Saravanan, M.: Machine learning approach for sudden cardiac arrest prediction based on optimal heart rate variability features. J. Med. Imaging Health Inf. 4, 1–12 (2014)CrossRefGoogle Scholar
  32. 32.
    Devi, R., Tyagi, H.K., Kumar, D.: Early stage prediction of sudden cardiac death. In: Proceedings of the IEEE International Conference WiSPNET 2017 Held on 22–24 March 2017. SSN College of Engineering and Technology, Chennai (2017)Google Scholar
  33. 33.
    Devi, R., Tyagi, H.K., Kumar, D.: Early stage prediction of sudden cardiac death using linear and non-linear features of heart rate variability. Int. J. Electron. Electr. Comput. Syst. 6(9), 742–754 (2017)Google Scholar
  34. 34.
    Devi, R., Tyagi, H.K., Kumar, D.: Heart rate variability analysis for early stage prediction of sudden cardiac death. World Acad. Sci. Eng. Technol. Int. J. Electr. Comput. Energ. Electron. Commun. Eng. 10(3) (2016). PISSN: 2010-376X, EISSN: 2010-3778Google Scholar
  35. 35.
    Patil, S., et al.: Intelligent and effective heart attack prediction system using data mining and artificial neural network. Eur. J. Sci. Res. 31(4), 642–656 (2009)Google Scholar
  36. 36.
    Jilani, T., et al.: Acute coronary syndrome prediction using data mining techniques - an application. Int. J. Inf. Math. Sci. 5(4), 295–299 (2009)MathSciNetGoogle Scholar
  37. 37.
    Lammert, M.E., et al.: Electrocardiographic predictors of out-of hospital sudden cardiac arrest in patients with coronary artery disease. Am. J. Cardiol. 109(9), 1278–1282 (2012)CrossRefGoogle Scholar
  38. 38.
    Billman, G.E., Schwartz, P.J., Stone, H.L.: Baroreceptor reflex control of heart rate: a predictor of sudden cardiac death. Circulation 66(4), 874–880 (1982)CrossRefGoogle Scholar
  39. 39.
    Bauer, A., et al.: Heart rate turbulence: standards of measurement, physiological interpretation, and clinical use: International Society for Holter and Noninvasive Electrophysiology consensus. J. Am. Coll. Cardiol. 52(17), 1353–1365 (2008)CrossRefGoogle Scholar
  40. 40.
    Schmidt, G., Malik, M., Barthel, P., et al.: Heart-rate turbulence after ventricular premature beats as a predictor of mortality after acute myocardial infarction. Lancet 353, 1390–1396 (1999)CrossRefGoogle Scholar
  41. 41.
    Baur, A., Zurn, C.S., Schmidt, G.: Heart rate turbulence to guide treatment for prevention of sudden death. J. Cardiovasc. Pharmacol. 55(6), 531–538 (2010)CrossRefGoogle Scholar
  42. 42.
    Francis, J., Watanabe, M.A., Schmidt, G.: Heart rate turbulence: a new predictor for risk of sudden cardiac death. Ann. Noninvasive Electrocardiol. 10(1), 102–109 (2005)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Reeta Devi
    • 1
    Email author
  • Hitender Kumar Tyagi
    • 2
  • Dinesh Kumar
    • 3
  1. 1.Department of Electronics and Communication Engineering, University Institute of Engineering and TechnologyKurukshetra UniversityKurukshetraIndia
  2. 2.Department of Electronics, University CollegeKurukshetra UniversityKurukshetraIndia
  3. 3.YMCA University of Science and TechnologyFaridabadIndia

Personalised recommendations