New Diagnostic Tool for Patients Suffering from Noncommunicable Diseases (NCDs)

  • Wojciech Oleksy
  • Zbigniew Budzianowski
  • Ewaryst Tkacz
  • Małgorzata Garbacik
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 721)


Electrocardiography, technique, which is an essential tool in the diagnosis of heart disease, as well as other organs, is used by doctors for over 100 years. It is used to measure electrical activity of the heart as a function of time and present it in digital or analogue form. Whilst the standard 12 lead ECG is the basic clinical method of heart diagnosis it has its drawbacks. Measuring all 12 leads is often difficult and impractical, most of all it restricts patient movement. In 1988, Gordon Dower developed a system of quasi-orthogonal lead called EASI, which uses only 5 electrodes in order to register standard 12 lead ECG signals. The main goal of this work is to present a new tool using machine learning algorithms which transforms electrocardiographic signals (ECG) performed by EASI into a standard 12-channel ECG, which therefore could be an ideal tool for diagnose of NCDs.


EASI ECG Regression Machine learning 


  1. 1.
    WHO Europe – The European Health Report 2012: Charting The Way To Well-Being.
  2. 2.
  3. 3.
    Redley, B.: EASI ECG monitoring vs traditional 12-lead ECG. A Review of the Literature (2005)Google Scholar
  4. 4.
    Efron, B., Johnstone, I., Robert, T.: Least angle regression. Ann. Stat. 32, 407–499 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Hastie, T., Tibshirani, R., Jerome, F.: The elements of statistical learning. Data Mining, Inference, and Prediction. Springer, New York (2013)zbMATHGoogle Scholar
  6. 6.
    Rousseeuw, P.J.: Least median of squares regression. J. Am. Stat. Assoc. 79(388), 871–880 (1984)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Wang, Y., Witten, I.H.: Pace Regression Working Paper Series (1999)Google Scholar
  8. 8.
    Leo, B.: Bagging predictors. Mach. Learn. 24, 123–140 (1996)zbMATHGoogle Scholar
  9. 9.
    Friedman, J.H.: Stochastic gradient boosting. Comput. Stat. Data Anal. 38(4), 367–378 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall, Upper Saddle River (1998)zbMATHGoogle Scholar
  11. 11.
    Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, New York (1995)zbMATHGoogle Scholar
  12. 12.
    Frank, E., Witten, I.H.: Data Mining. Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Massachusetts (2005)zbMATHGoogle Scholar
  13. 13.
    Smola, A.J., Schlkopf, B.: A tutorial on support vector regression. Stat. Comput. 14, 199–222 (2004)MathSciNetCrossRefGoogle Scholar
  14. 14.
  15. 15.
    Oleksy, W., Tkacz, E.: Investigation of a transfer function between standard 12-Lead ECG And EASI ECG. Anal. Biomed. Signals Images 20, 322–327 (2010)Google Scholar
  16. 16.
    Feild, D.Q., Feldman, C.L., Milan, H.B.: Improved EASI coefficients: their derivation values, and performance. J. Electrocardiol. 35, 22–33 (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Wojciech Oleksy
    • 1
  • Zbigniew Budzianowski
    • 1
  • Ewaryst Tkacz
    • 1
  • Małgorzata Garbacik
    • 1
  1. 1.Department of Biosensors and Processing of Biomedical Signals, Faculty of Biomedical EngineeringSilesian University of TechnologyGliwicePoland

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