Electrocardiogram analysis using a combination of statistical, geometric, and nonlinear heart rate variability features

Artificial Intelligence in Medicine

Volumn 51, Issue 3, 2011, Pages 175-186

  Jovic, Alan ^a   Bogunovic, Nikola ^a  

^a UNIVERSITY OF ZAGREB   (Croatia)

Author keywords

C4.5 decision tree; Heart disorder classification; Heart rate variability; Nonlinear analysis; Random forest; Support vector machines

Indexed keywords

APPROXIMATE ENTROPY; ARTIFICIAL NEURAL NETWORK; C4.5 DECISION TREE; CARDIAC DISORDERS; CLASSIFICATION ACCURACY; CLASSIFICATION ALGORITHM; CLASSIFICATION METHODS; CLASSIFICATION RESULTS; CLASSIFICATION RULES; CLUSTERING RESULTS; CONGESTIVE HEART FAILURES; CORRELATION DIMENSIONS; ELECTROCARDIOGRAM ANALYSIS; EXPECTATION MAXIMIZATION; FEATURE VECTORS; HEART RATE VARIABILITY; K-MEANS; MACHINE LEARNING ALGORITHMS; MULTI-CLASS CLASSIFICATION; MULTICLASS CLASSIFICATION PROBLEMS; NONLINEAR FEATURES; ONLINE DATABASE; PATIENT RECORD; RANDOM FORESTS; RR INTERVALS; SINGLE PERIOD;

BAYESIAN NETWORKS; CLUSTERING ALGORITHMS; DECISION TREES; DISTRIBUTED PARAMETER NETWORKS; ELECTROCARDIOGRAPHY; ELECTROCHROMIC DEVICES; EXTRACTION; HEART; INFERENCE ENGINES; INTELLIGENT NETWORKS; LEARNING ALGORITHMS; NEURAL NETWORKS; NONLINEAR ANALYSIS; SPEECH RECOGNITION; SUPPORT VECTOR MACHINES; VECTORS;

CLASSIFICATION (OF INFORMATION);

ACCURACY; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYESIAN LEARNING; CONGESTIVE HEART FAILURE; CONTROLLED STUDY; DISEASE CLASSIFICATION; ELECTROCARDIOGRAPHY; GEOMETRY; HEART ARRHYTHMIA; HEART RATE VARIABILITY; HEART RHYTHM; HEART SUPRAVENTRICULAR ARRHYTHMIA; HUMAN; MAJOR CLINICAL STUDY; NONLINEAR HEART RATE VARIABILITY; PRIORITY JOURNAL; RANDOM FOREST; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS; SUPPORT VECTOR MACHINE;

ALGORITHMS; DATA INTERPRETATION, STATISTICAL; ELECTROCARDIOGRAPHY; HEART; HEART RATE; HUMANS; NONLINEAR DYNAMICS;

EID: 79954595949     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artmed.2010.09.005     Document Type: Article

References (49)

1. Kitney R.I., Linkens D., Selman A., McDonald A. The interaction between heart rate and respiration: part II - nonlinear analysis based on computer modelling. Automedica 1982, 4:141-153.
2. 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, European Heart Journal 1996;17:354-81.
3. Goldberger A.L. Non-linear dynamics for clinicians: chaos theory, fractals, and complexity at the bedside. Lancet 1996, 11:1312-1314.
4. Goldberger A.L. Fractal variability versus pathologic periodicity: complexity loss and stereotype in disease. Perspectives in Biology and Medicine 1997, 40:543-561.
5. Ivanov P.C., Amaral L.A., Goldberger A.L., Havlin S., Rosenblum M.G., Struzik Z.R., et al. Multifractality in human heartbeat dynamics. Nature 1999, 399(6735):461-465.
6. Jovic A., Bogunovic N. Feature extraction for ECG time-series mining based on chaos theory. Proceedings of the 29th international conference on information technology interfaces 2007, 63-68. SRCE, Zagreb, Croatia. V. Luzar-Stiffler, V. Hljuz Dobric (Eds.).
7. Witten I.H., Frank E. Data mining: Practical machine learning tools and techniques 2005, Morgan Kaufmann, San Francisco CA, USA.
8. Iyengar N., Peng C.K., Goldberger A.L., Lipsitz L.A. Age-related alterations in the fractal scaling of cardiac interbeat interval dynamics. American Journal of Physiology 1996, 271:1078-1084.
9. Braun C., Kowallik P., Freking A., Hadeler D., Kniffki K.D., Meesmann M. Demonstration of nonlinear components in heart rate variability of healthy persons. American Journal of Physiology - Heart and Circulatory Physiology 1998, 275(5):1577-1584.
10. Acharya R.U., Joseph K.P., Kannathal N., Lim C.M., Suri J.S. Heart rate variability: a review. Medical & Biological Engineering & Computing 2006, 44:1031-1051.
11. Chattipakorn N., Incharoen T., Kanlop N., Chattipakorn S. Heart rate variability in myocardial infarction and heart failure: review. International Journal of Cardiology 2007, 120:289-296.
12. Tsipouras M.G., Fotiadis D.I. Automatic arrhythmia detection based on time and time-frequency analysis of heart rate variability. Computer Methods and Programs in Biomedicine 2004, 74:95-108.
13. Asl B.M., Setarehdan S.K., Mohebbi M. Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal. Artificial Intelligence in Medicine 2008, 44(1):51-64.
14. Kadbi M.H., Hashemi J., Mohseni H.R., Maghsoudi A. Classification of ECG arrhythmias based on statistical and time-frequency features. Proceedings of IET 3rd International Conference on advances in medical, signal and information processing MEDSIP 2006 2006, 1-4. Curran Associates, Red Hook NY, USA. S.G. Fabri (Ed.).
15. Ubeyli E.D. Recurrent neural networks with composite features for detection of electrocardiographic changes in partial epileptic patients. Computers in Biology and Medicine 2008, 38:401-410.
16. Pincus S.M., Goldberger A.L. Physiological time-series analysis: what does regularity quantify?. American Journal of Physiology 1994, 266(4 Pt 2):H1643-H1656.
17. Richman J.S., Moorman J.R. Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology 2000, 278(6):H2039-H2049.
18. Ho K.K.L., Moody G.B., Peng C.K., Mietus J.E., Larson M.G., Levy D., et al. Predicting survival in heart failure case and control subjects by use of fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics. Circulation 1997, 96(3):842-848.
19. Seker R., Saliu S., Birand A., Kudaiberdieva G. Validity test for a set of nonlinear measures for short data length with reference to short-term heart rate variability signal. Journal of Systems Integration 2000, 10:41-53.
20. Costa M., Goldberger A.L., Peng C.K. Multiscale entropy analysis of complex physiologic time series. Physical Review Letters 2002, 89(6):068102.
21. Angelini L., Maestri R., Marinazzo D., Nitti L., Pellicoro M., Pinna G.D., et al. Multiscale analysis of short term heart beat interval, arterial blood pressure, and instantaneous lung volume time series. Artificial Intelligence in Medicine 2007, 41(3):237-250.
22. Acharya R.U., Kumar A., Bhat P.S., Lim C.M., Iyengar S.S., Kannathal N., et al. Classification of cardiac abnormalities using heart rate signals. Medical & Biological Engineering & Computing 2004, 42:288-293.
23. Acharya R.U., Lim C.M., Joseph P. Heart rate variability analysis using correlation dimension and detrended fluctuation analysis. ITBM-RBM Innovation et technologie en biologie et médecine 2002, 23(6):333-339.
24. Peng C.K., Havlin S., Stanley H.E., Goldberger A.L. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 1995, 5(1):82-87.
25. Faust O., Acharya R.U., Krishnan S.M., Min L.C. Analysis of cardiac signals using spatial filling index and time-frequency domain. BioMedical Engineering OnLine 2004, 3:30.
26. Cohen M.E., Hudson D.L., Deedwania P.C. Measurement of variability in Holter Tape R-R intervals for patients with congestive heart failure. Proceedings of the 16th annual international conference of the ieee engineering in medicine and biology society 1994, 127-128. IEEE Press, Baltimore MD, USA. N.F. Sheppard, M. Eden, G. Kantor (Eds.).
27. Manis G., Nikolopoulos S., Alexandridi A., Davos C. Assessment of the classification capability of prediction and approximation methods for HRV analysis. Computers in Biology and Medicine 2007, 37:642-654.
28. Advanced methods and tools for ECG data analysis 2006, Artech House, Norwood MA, USA. G.D. Clifford, F. Azuaje, P.E. McSharry (Eds.).
29. PhysioBank: Physiologic signal archives for biomedical research, http://www.physionet.org/.
30. Acharya R.U., Bhat P.S., Iyengar S.S., Raod A., Dua S. Classification of heart rate data using artificial neural network and fuzzy equivalence relation. Pattern Recognition 2003, 36:61-68.
31. Bezerianos A., Papadimitriou S., Alexopoulos D. Radial basis function neural networks for the characterization of heart rate variability dynamics. Artificial Intelligence in Medicine 1999, 15(3):215-234.
32. Burges J.C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 1998, 2:121-167.
33. McCauley J.L. Chaos, dynamics, fractals: an algorithmic approach to deterministic chaos (Cambridge Nonlinear Science Series) 1994, Cambridge University Press, Cambridge, United Kingdom.
34. Alvarez D., Hornero R., Garcia M., del Campo F., Zamarron C. Improving diagnostic ability of blood oxygen saturation from overnight pulse oximetry in obstructive sleep apnea detection by means of central tendency measure. Artificial Intelligence in Medicine 2007, 41(1):13-24.
35. Schuster H.G. Deterministic chaos: an introduction 1984, Physik-Verlag GmbH, Weinheim, Germany.
36. Ding M., Grebogi C., Ott E., Sauer T., Yorke J.A. Plateau onset for correlation dimension: When does it occur?. Physical Review Letters 1993, 70:3872-3875.
37. Grassberger P., Procaccia I. Measuring the strangeness of strange attractors. Physica D: Nonlinear Phenomena 1983, 9(1-2):189-208.
38. Hornero R., Abasolo D., Jimeno N., Sanchez C.I., Poza J., Aboy M. Variability, regularity, and complexity of time series generated by schizophrenic patients and control subjects. IEEE Transactions on Biomedical Engineering 2006, 53(2):210-218.
39. Mietus J.E., Peng C.K., Henry I., Goldsmith R.L., Goldberger A.L. The pNNx files: re-examining a widely used heart rate variability measure. Heart 2002, 88:378-380.
40. Hutchinson T.P. Statistics and graphs for heart rate variability: pNN50 or pNN20?. Physiological Measurement 2003, 24(3):N9-N14.
41. Hartigan J.A., Wong M.A. A k-means clustering algorithm. Applied Statistics 1979, 28(1):100-108.
42. Quinlan J.R. C4.5: Programs for machine learning 1993, Morgan Kaufmann Publishers, San Francisco CA, USA.
43. Friedman N., Geiger D., Goldszmidt M. Bayesian network classifiers. Machine Learning 1997, 29(2-3):131-163.
44. Haykin S. Neural networks: a comprehensive foundation 1999, Prentice Hall, Upper Saddle River NJ, USA.
45. Vapnik V. The nature of statistical learning theory 1995, Springer-Verlag, New York, USA.
46. Platt J. Fast training of support vector machines using sequential minimal optimization. Advances in kernel methods-support vector learning 1998, 185-208. MIT Press, Cambridge MA, USA. B. Schöllkopf, C.J. Burges, A.J. Smola (Eds.).
47. Breiman L. Random forests. Machine Learning 2001, 45:5-32.
48. Darrington J.M., Hool L.C. A new methodology for assessment of the performance of heartbeat classification systems. BMC Medical Informatics and Decision Making 2008, 8:7.
49. Kachigan S.K. Multivariate statistical analysis: a conceptual introduction 1991, Radius Press, New York, USA.