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Patient Specific Congestive Heart Failure Detection From Raw ECG signal

Yakup Kutlu*, Apdullah Yayık, Esen Yıldırım, Mustafa Yeniad, Serdar Yıldırım

DOI: 10.28978/nesciences.286250

Abstract

In this study; in order to diagnose congestive heart failure (CHF) patients, non-linear second-order difference plot (SODP) obtained from raw 256 Hz sampled frequency and windowed record with different time of ECG records are used. All of the data rows are labelled with their belongings to classify much more realistically. SODPs are divided into different radius of quadrant regions and numbers of the points fall in the quadrants are computed in order to extract feature vectors. Fisher's linear discriminant, Naive Bayes, Radial basis function, and artificial neural network are used as classifier. The results are considered in two step validation methods as general k-fold cross-validation and patient based cross-validation. As a result, it is shown that using neural network classifier with features obtained from SODP, the constructed system could distinguish normal and CHF patients with 100% accuracy rate.

Keywords

Congestive heart failure, ECG, Second-Order Difference Plot, classification, patient based cross-validation

 

Volume 1, No 2, 33-43, 2016

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References
  • A.L. Goldberger and coworkers, 2000. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220. Available at: http://circ.ahajournals.org/cgi/content/full/101/23/e215.
  • Bishop, C.M. & others, 1995. Neural networks for pattern recognition.
  • Braunwald E, Zipes DP, Libby P, B.R., 2004. Braunwald’s Heart Disease A Textbook of Cardiovascular Medicine 7th Editio., Philadelphia, USA: Saunders.
  • D., U.E., 2009. Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents. Computer Methods and Programs in Biomedicine., pp.313–321.
  • Dabanloo, N.J. et al., 2010. Application of Novel Mapping for Heart Rate Phase Space and Its Role in Cardiac Arrhythmia Diagnosis. Computing in Cardiology, pp.209–212.
  • Duda, R.O. & Hart, P.E., Pattern Classification.pdf.
  • Engin, M., 2007. Spectral and wavelet based assessment of congestive heart failure patients. Computers in Biology and Medicine.
  • Işler Y, K.M., Konjestif Kalp Yetmezligi Teshisi için Kalp Hızı Degiskenligi Analizinde Dalgacık Entropisinin Etkisi. In IEEE 14th Signal Processing and Communications Applications Conference. pp. 1–4.
  • Işler, Y. & Kuntalp, M., 2007. Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure. Computers in biology and medicine, 37(10), pp.1502–10. Available at: http://www.ncbi.nlm.nih.gov/pubmed/17359959 [Accessed June 22, 2013].
  • Kamath, C., 2012a. A new approach to detect congestive heart failure using sequential spectrum of electrocardiogram signals. Medical engineering & physics, 34(10), pp.1503–9. Available at: http://www.ncbi.nlm.nih.gov/pubmed/22459502 [Accessed December 8, 2013].
  • Kamath, C., 2012b. A new approach to detect congestive heart failure using Teager energy nonlinear scatter plot of R-R interval series. Medical engineering & physics, 34(7), pp.841–8. Available at: http://www.ncbi.nlm.nih.gov/pubmed/22032833 [Accessed June 22, 2013].
  • Kannathal, N. et al., 2006. Cardiac state diagnosis using adaptive neuro-fuzzy technique. Medical Engineering & Physics, 28(8), pp.809–815.
  • Karmakar, C.K. et al., 2009. Novel feature for quantifying temporal variability of Poincare Plot: a case study. In Computers in Cardiology, 2009. pp. 53–56.
  • Maurice E.Cohen, D.L.H. and P.C.D., 1996. Applying Continuous chaotic Modeling to Cardiac Signal Analysis. Engineering in Medicine and Biology, (2), pp.97–102.
  • Suri, J. et al., 2007. Advances in cardiac signal processing, Springer.
  • Thuraisingham, R.A., 2010. A Classification System to Detect Congestive Heart Failure Using Second-Order Difference Plot of RR Intervals. Cardiology research and practice, 2009, p.807379. Available at: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2842886&tool=pmcentrez&rendertype=abstract [Accessed June 22, 2013].
  • Topol, E.J. & Califf, R.M., 2007. Textbook of cardiovascular medicine, Lippincott Williams & Wilkins.
  • Yayla, B., 2010. Kalp yetmezli̇kli̇ hastalarda adrenomedulli̇n ve probnp düzeyleri̇ni̇n ekokardi̇yografi̇ i̇le i̇li̇şki̇si̇. İstanbul Göztepe Araştırma ve Geliştirme hastanesi.