Comparison of the Effects of Cross-validation Methods on Determining Performances of Classifiers Used in Diagnosing Congestive Heart Failure

[1] U.S. National Library of Medicine. Heart failure (Medical Encyclopedia). http://www.nlm.nih.gov.Search in Google Scholar

[2] Flavell, C., Stevenson, L.W. (2001). Take Heart with Heart Failure. Circulation, 104, 89.10.1161/hc4301.09913611684641Search in Google Scholar

[3] Wilbur, J., James, P. (2005). Diagnosis and management of heart failure in the outpatient setting. Primary Care, 32, 1115-1129.10.1016/j.pop.2005.09.00516326230Search in Google Scholar

[4] American Heart Association (2006). Heart Disease and Stroke Statistics-2006 Update: A Report From the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation, 113, 85-151.Search in Google Scholar

[5] Isler, 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), 1502-1510.10.1016/j.compbiomed.2007.01.01217359959Search in Google Scholar

[6] Isler, Y., Kuntalp, M. (2010). Heart Rate Normalization in the Analysis of Heart Rate Variability in Congestive Heart Failure. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, 224(3), 453-463.10.1243/09544119JEIM64220408490Search in Google Scholar

[7] Yu, S.N., Lee, M.Y. (2012). Bispectral analysis and genetic algorithm for congestive heart failure recognition based on heart rate variability. Computers in Biology and Medicine, 42, 816-825.10.1016/j.compbiomed.2012.06.00522809682Search in Google Scholar

[8] Yu, S.N., Lee, M.Y. (2012). Conditional mutual information-based feature selection for congestive heart failure recognition using heart rate variability. Computer Methods and Programs in Biomedicine, 108, 299-309.10.1016/j.cmpb.2011.12.01522261219Search in Google Scholar

[9] Jovic, A., Bogunovic, N. (2011). Electrocardiogram analysis using a combination of statistical, geometric, and nonlinear heart rate variability features. Artificial Intelligence in Medicine, 51, 175-186.10.1016/j.artmed.2010.09.00520980134Search in Google Scholar

[10] Pecchia, L., Melillo, P., Sansone, M., Bracale, M. (2011). Discrimination power of short-term heart rate variability measures for CHF assessment. IEEE Transactions on Information Technology in Biomedicine, 15(1), 40-46.10.1109/TITB.2010.209164721075731Search in Google Scholar

[11] Narin, A., Isler, Y., Ozer, M. (2014). Investigating the performance improvement of HRV Indices in CHF using feature selection methods based on backward elimination and statistical significance. Computers in Biology and Medicine, 45, 72-79.10.1016/j.compbiomed.2013.11.01624480166Search in Google Scholar

[12] Narin, A., Isler, Y. (2012). Effect of Principal Component Analysis on Diagnosing Congestive Heart Failure Patients using Heart Rate Records. In IEEE 20th Signal Processing and Communications Applications Conference (SIU2012), 18-20 April 2012, Fethiye / Mugla.10.1109/SIU.2012.6204735Search in Google Scholar

[13] Isler, Y., Selver, M.A., Kuntalp, M. (2005). Effects of Detrending in Heart Rate Variability Analysis. In II. Muhendislik Bilimleri Genc Arastirmacilar Kongresi MBGAK’2005, 17-19 October 2005, Istanbul, 213-219.Search in Google Scholar

[14] Duda, R.O., Hart, P.E., Stork, D.G. (2000). Pattern Classification, 2nd edition, New York. Wiley.Search in Google Scholar

[15] Goldberger, A.L., et.al. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation, 101(23), e215-e220.10.1161/01.CIR.101.23.e215Search in Google Scholar

[16] Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology. (1996). Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Circulation, 93, 1043-1065.Search in Google Scholar

[17] Lomb, N.R. (1976). Least-squares frequency analysis of unequally spaced data. Astrophysics and Space Science, 39, 447-462.10.1007/BF00648343Search in Google Scholar

[18] Quiraga, R.Q., Rosso, O.A., Basar, E., Schurmann, M. (2001). Wavelet entropy in event-related potentials: A new method shows ordering of EEG oscillations. Biological Cybernetics, 84(4), 291-299.10.1007/s004220000212Search in Google Scholar

[19] Woo, M.A., Stevenson, W.G., Moser, D.K., Trelease, R.B., Harper, R.H. (1992). Patterns of beat-to-beat heart rate variability in advanced heart failure. American Heart Journal, 123, 704-710.10.1016/0002-8703(92)90510-3Search in Google Scholar

[20] Kamen, P.W., Krum, H., Tonkin, A.M. (1996). Poincare plot of heart rate variability allows quantitative display of parasympathetic nervous activity. Clinical Science, 92, 201-208.10.1042/cs09102018795444Search in Google Scholar

[21] Kamen, P.W. (1996). Heart rate variability. Australian Family Physician, 25, 1087-1094.Search in Google Scholar

[22] Brennan, M., Palaniswami, M., Kamen, P. (2001). Do existing measures of poincare plot geometry reflect nonlinear features of heart rate variability? IEEE Transactions on Biomedical Engineering, 48(11), 1342-1347.10.1109/10.95933011686633Search in Google Scholar

[23] Huikuri, H.V., Makikallio, T.H., Peng, C.K., Goldberger, A.L., Hintze, U., Moller, M. (2000). Fractal correlation properties of R-R interval dynamics and mortality in patients with depressed left ventricular function after an acute myocardial infarction. Circulation, 101, 47-53.10.1161/01.CIR.101.1.4710618303Search in Google Scholar

[24] Acharya, U.R., Kannathal, N., Seng, O.W., Ping, L.Y., Chua, T. (2004). Heart rate analysis in normal subjects of various age groups. BioMedical Engineering OnLine, 3(24).10.1186/1475-925X-3-2449327815260880Search in Google Scholar

[25] Caminal, P., Vallverdu, M., Giraldo, B., Benito, S., Vazquez, G., Voss, A. (2005). Optimized symbolic dynamics approach for the analysis of the respiratory pattern. IEEE Transactions on Biomedical Engineering, 52(11), 1832-1839.10.1109/TBME.2005.85629316285386Search in Google Scholar

[26] Xu, J.H., Liu, Z.R., Liu, R. (1994). The measures of sequence complexity for EEG studies. Chaos, 4(11), 2111-2119.Search in Google Scholar

[27] Richman, J.S., Randall, M.J. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology - Heart and Circulatory Physiology, 278, H2039-H2049.10.1152/ajpheart.2000.278.6.H203910843903Search in Google Scholar

[28] Akgul, A. (2003). Tibbi Arastirmalarda Istatistiksel Analiz Teknikleri: SPSS Uygulamalari (Statistical Analysis Techniques in Medical Researches: SPSS Experiments). 2nd edition, Ankara, Turkey, Seckin Yayincilik.Search in Google Scholar

[29] Kohavi, R. (1995). A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. In 14th International Joint Conference on Artificial Intelligence (IJCAI), 20-25 August 1995, Montreal, Quebec, Canada. Search in Google Scholar