Evaluating and comparing performance of feature combinations of heart rate variability measures for cardiac rhythm classification

Biomedical Signal Processing and Control

Volumn 7, Issue 3, 2012, Pages 245-255

  Jovic, Alan ^a   Bogunovic, Nikola ^a  

^a UNIVERSITY OF ZAGREB   (Croatia)

Author keywords

Boosted C4.5; Heart rate variability; Linear features; Nonlinear features; Random forest; Support vector machines

Indexed keywords

DECISION TREES; DISEASES; RANDOM FORESTS; SUPPORT VECTOR MACHINES; TIME DOMAIN ANALYSIS;

ARRHYTHMIA CLASSIFICATION; AUTOMATIC CLASSIFICATION; BOOSTED C4.5; CLASSIFICATION ACCURACY; CLASSIFICATION ALGORITHM; HEART RATE VARIABILITY; LINEAR FEATURE; NONLINEAR FEATURES;

HEART;

ACCURACY; ALGORITHM; COMPARATIVE STUDY; CONFERENCE PAPER; CONTROLLED STUDY; DISEASE CLASSIFICATION; EVALUATION; HEART ARRHYTHMIA; HEART RATE VARIABILITY; HEART RHYTHM; PRIORITY JOURNAL; RANDOM FOREST; SUPPORT VECTOR MACHINE;

ALGORITHMS; CLASSIFICATION; DISEASES; FORESTRY; MATHEMATICAL MODELS; PROBLEM SOLVING;

EID: 84860234891     PISSN: 17468094     EISSN: 17468108     Source Type: Journal    
DOI: 10.1016/j.bspc.2011.10.001     Document Type: Conference Paper

References (53)

1. J. Pan, and W.J. Tompkins Real time QRS detector algorithm IEEE Trans. Biomed. Eng. 32 1985 230 236
2. M. Adnane, Z. Jiang, and S. Choi Development of QRS detection algorithm designed for wearable cardiorespiratory system Comp. Meth. Prog. Biomed. 93 2009 20 31
3. R. Kitney, D. Linkens, A. Selman, and A. McDonald The interaction between heart rate and respiration. Part II - Nonlinear analysis based on computer modeling Automedica 4 1982 141 153
4. M.C. Teich, S.B. Lowen, B.M. Jost, K. Vibe-Rheymer, and C. Heneghan Heart-rate variability: measures and models M. Akay, Nonlinear Biomedical Signal Processing, vol. II, Dynamic Analysis and Modeling 2001 IEEE Press New York 159 213
5. N. Iyengar, C.K. Peng, A.L. Goldberger, and L.A. Lipsitz Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics Am. J. Physiol. 271 1996 1078 1084
6. Task Force of The European Society of Cardiology and The North American Society of Pacing and Electrophysiology Heart rate variability guidelines: standards of measurement, physiological interpretation, and clinical use Eur. Heart J. 17 1996 354 381
7. M.G. Tsipouras, and D.I. Fotiadis Automatic arrhythmia detection based on time and time-frequency analysis of heart rate variability Comp. Meth. Prog. Biomed. 74 2 2004 95 108 (Pubitemid 38315050)
8. S.W. Chen A wavelet-based heart rate variability analysis for the study of nonsustained ventricular tachycardia IEEE Trans. Biomed. Eng. 49 7 2002 736 742
9. O. Faust, R.U. Acharya, S.M. Krishnan, and L.C. Min Analysis of cardiac signals using spatial filling index and time-frequency domain Biomed. Eng. Online 3 2004 30
10. D. Cysarz, H. Bettermann, and P. van Leeuwen Entropies of short binary sequences in heart period dynamics Am. J. Physiol. Heart Circ. Physiol. 278 2000 H2163 H2172
11. A.L. Goldberger Non-linear dynamics for clinicians: chaos theory fractals, and complexity at the bedside Lancet 11 1996 1312 1314 (Pubitemid 26137801)
12. P.C. Ivanov, L.A. Amaral, A.L. Goldberger, S. Havlin, M.G. Rosenblum, Z.R. Struzik, and H.E. Stanley Multifractality in human heartbeat dynamics Nature 399 1999 461 465 (Pubitemid 29258850)
13. C. Braun, P. Kowallik, A. Freking, D. Hadeler, K.D. Kniffki, and M. Meesmann Demonstration of nonlinear components in heart rate variability of healthy persons Am. J. Physiol. Heart Circ. Physiol. 275 5 1998 1577 1584
14. K.C. Chua, V. Chandran, R.U. Acharya, and L.C. Min Cardiac health diagnosis using higher order spectra and support vector machine Open Med. Inform. J. 3 2009 1 8
15. C.K. Karmakar, A.H. Khandoker, J. Gubbi, and M. Palaniswani Complex Correlation Measure: a novel descriptor for Poincare plot Biomed. Eng. Online 8 2009 17
16. G. Kudaiberdieva, B. Görenek, and B. Timuralp Heart rate variability as a predictor of sudden cardiac death Anadolu Kardiyol Derg. 7 2007 68 70 (Pubitemid 47028976)
17. B.M. Asl, S.K. Setarehdan, and M. Mohebbi Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal Artif. Intell. Med. 44 1 2008 51 64
18. F. Yaghouby, A. Ayatollahi, and R. Soleimani Classification of cardiac abnormalities using reduced features of heart rate variability signal World Appl. Sci. J. 6 11 2009 1547 1554
19. A. Jovic, and N. Bogunovic Electrocardiogram analysis using a combination of statistical, geometric, and nonlinear heart rate variability features Artif. Intell. Med. 51 2011 175 186
20. R.U. Acharya, P.S. Bhat, S.S. Iyengar, A. Raod, and S. Dua Classification of heart rate data using artificial neural network and fuzzy equivalence relation Pattern Recogn. 36 2003 61 68
21. F. Yaghouby, A. Ayatollahi, R. Bahramali, M. Yaghouby, and A.H. Alavi Towards automatic detection of atrial fibrillation: a hybrid computational approach Comput. Biol. Med. 40 11-12 2010 919 930
22. M.H. Kadbi, J. Hashemi, H.R. Mohseni, and A. Maghsoudi Classification of ECG arrhythmias based on statistical and time-frequency features S.G. Fabri, Proc IET 3rd Int Conf on Advances in Medical, Signal and Information Processing MEDSIP 2006 July 17-19, 2006, Glasgow, Scotland, UK 2006 Curran Associates Red Hook, NY, USA 1 4
23. T.P. Exarchos, M.G. Tsipouras, C.P. Exarchos, C. Papaloukas, D.I. Fotiadis, and L.K. Michalis A methodology for the automated creation of fuzzy expert systems for ischaemic and arrhythmic beat classification based on a set of rules obtained by a decision tree Artif. Intell. Med. 40 3 2007 187 200 (Pubitemid 47048715)
24. J. Kim, H.S. Shin, K. Shin, and M. Lee Robust algorithm for arrhythmia classification in ECG using extreme learning machine Biomed. Eng. Online 8 2009 31
25. L. Pecchia, P. Melillo, M. Sansone, and M. Bracale Discrimination power of short-term heart rate variability measures for CHF assessment IEEE Trans. Inf. Technol. Biomed. 15 1 2011 40 46
26. V. Vapnik The Nature of Statistical Learning Theory 1995 Springer-Verlag New York
27. J.R. Quinlan Bagging, Boosting, and C4.5 AAAI, Proc 13th National Conf Artif Intell August 4-8, 1996, Portland, OR, USA 1996 MIT Press Cambridge, MA, USA 725 730
28. L. Breiman Random forests Mach. Learn. 45 2001 5 32 (Pubitemid 32933532)
29. T.B. Garcia, and N.E. Holtz 12-Lead ECG: The Art of Interpretation 2001 Jones and Bartlett Publishers Sudbury, MA, USA
30. G. Karraz, and G. Magenes Automatic classification of heartbeats using neural network classifier based on a Bayesian framework A. Laine, K.H. Chon, Conf Proc IEEE Eng Med Biol Soc 1 August 30-September 3, 2006, New York 2006 IEEE Press New York 4016 4019 (Pubitemid 46520791)
31. PhysioBank: Physiologic signal archives for biomedical research, http://www.physionet.org (accessed 17.09.11).
32. G.B. Moody, and R.G. Mark The impact of the MIT-BIH Arrhythmia Database IEEE Eng. Med. Biol. Mag. 20 3 2001 45 50 (Pubitemid 32606302)
33. T.P. Hutchinson Statistics and graphs for heart rate variability: pNN50 or pNN20? Physiol. Meas. 24 3 2003 N9 N14
34. R.U. Acharya, K.P. Joseph, N. Kannathal, C.M. Lim, and J.S. Suri Heart rate variability: a review Med. Biol. Eng. Comput. 44 2006 1031 1051 (Pubitemid 47090818)
35. S. de Waele, and P.M.T. Broersen The Burg algorithm for segments IEEE Trans. Signal Process. 48 10 2000 2876 2880
36. A. Kitlas, E. Oczeretko, M. Kowalewski, M. Borowska, and M. Urban Nonlinear dynamics methods in the analysis of the heart rate variability Roczniki Akademii Medycznej w Białymstoku, Annales Academiae Medicae Bialostocensis 50 Suppl. 2 2005 46 47
37. S.M. Pincus, and A.L. Goldberger Physiological time-series analysis: what does regularity quantify? Am. J. Physiol. 266 Heart Circ. Physiol. 35 1994 H1643 H1656
38. M. Costa, A.L. Goldberger, and C.K. Peng Multiscale entropy analysis of biological signals Phys. Rev. E 71 2005 021906
39. Y. Özbay, R. Ceylan, and B. Karlik A fuzzy clustering neural network architecture for classification of ECG arrhythmias Comput. Biol. Med. 36 2006 376 388 (Pubitemid 43247768)
40. K.H. Chon, C.G. Scully, and S. Lu Approximate entropy for all signals IEEE Eng. Med. Biol. Mag. 28 6 2009 18 23
41. J.R. Quinlan C4.5: Programs for Machine Learning 1993 Morgan Kaufmann Publishers San Francisco, CA, USA
42. J. Platt Fast training of support vector machines using sequential minimal optimization B. Schöllkopf, C.J. Burges, A.J. Smola, Advances in Kernel Methods - Support Vector Learning 1998 MIT Press Cambridge, MA, USA 185 208
43. Y. Freund, and R.E. Schapire A decision-theoretic generalization of on-line learning and an application to boosting P.M.B. Vitányi, Proc 2nd Eur Conf Comput Learn Theory EuroCOLT '95 March 13-15, 1995, Barcelona, Spain 1995 Springer-Verlag London, UK 23 37
44. E. Alpaydin Introduction to Machine Learning 2004 MIT Press Cambridge, MA, USA
45. S. Pramanik, U.N. Chowdhury, B.K. Pramanik, and N. Huda A comparative study of bagging, boosting and C4.5: the recent improvements in decision tree learning algorithm Asian J. Inf. Technol. 9 6 2010 300 306
46. A. Bureau, J. Dupuis, B. Hayward, K. Falls, and P. Van Eerdewegh Mapping complex traits using random forests BMC Genet. 4 Suppl. 1 2003 S64
47. A. Jovic, and N. Bogunovic HRVFrame: Java-based framework for feature extraction from cardiac rhythm Lect. Notes Artif. Intell. 6747 2011 96 100
48. A. Jovic, and N. Bogunovic Feature set extension for heart rate variability analysis by using non-linear, statistical and geometric measures V. Luzar-Stiffler, I. Jarec, Z. Bekic, Proc 31st Int Conf Information Technology Interfaces ITI 2009 June 22-25, 2009, Cavtat/Dubrovnik, Croatia 2009 SRCE Zagreb, Croatia 35 40
49. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I.H. Witten The WEKA Data Mining Software: an update SIGKDD Explor. Newsl. 11 1 2009 10 18
50. M.P. Tulppo, T.H. Makikallio, T.E. Takala, T. Seppanen, and H.V. Huikuri Quantitative beat-to-beat analysis of heart rate dynamics during exercise Am. J. Physiol. 71 1996 H244 H252
51. M.A. Hall, and G. Holmes Benchmarking attribute selection techniques for discrete class data mining IEEE Trans. Knowl. Data Eng. 15 3 2003 1437 1447
52. G. Wu, and E. Chang Class-boundary alignment for imbalanced dataset learning T. Fawcett, N. Mishra, Proc ICML 2003 Workshop on Learning from Imbalanced Data Sets II August 21-24, 2003, Washington, DC 2003 AAAI Press 49 56
53. K.J. Cios, and G.W. Moore Uniqueness of medical data mining Artif. Intell. Med. 26 1-2 2002 1 24 (Pubitemid 35299395)