Cardiac decision making using higher order spectra

Biomedical Signal Processing and Control

Volumn 8, Issue 2, 2013, Pages 193-203

  Martis, Roshan Joy ^a   Acharya, U Rajendra ^b,d   Mandana, K M ^c   Ray, A K ^a   Chakraborty, Chandan ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY   (India)

^b DEPARTMENT OF ELECTRONICS AND COMPUTER ENGINEERING   (Singapore)

^c Fortis Hospitals   (India)

^d UNIVERSITY OF MALAYA   (Malaysia)

Author keywords

Bispectrum; Classifier; Electrocardiogram; Higher Order Statistics; Principal component analysis (PCA); Support Vector Machine

Indexed keywords

CLASSIFIERS; DECISION MAKING; DIMENSIONALITY REDUCTION; ELECTROCARDIOGRAPHY; FEEDFORWARD NEURAL NETWORKS; HEART; IMAGE SEGMENTATION; MULTILAYER NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS; SUPPORT VECTOR MACHINES;

ATRIAL PREMATURE CONTRACTION; BISPECTRUM; COMPUTER-ASSISTED METHODS; HIGHERORDER STATISTIC (HOS); LEAST SQUARE SUPPORT VECTOR MACHINES; MEAN AND STANDARD DEVIATIONS; RADIAL BASIS FUNCTION(RBF); VENTRICULAR PREMATURE CONTRACTIONS;

HIGHER ORDER STATISTICS;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATION; BISPECTRUM; ELECTROCARDIOGRAM; HEART ARRHYTHMIA; HEART DISEASE; HEART LEFT BUNDLE BRANCH BLOCK; HEART RIGHT BUNDLE BRANCH BLOCK; LEAST SQUARE SUPPORT VECTOR MACHINE; MEDICAL DECISION MAKING; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; SUPRAVENTRICULAR PREMATURE BEAT; VENTRICULAR PREMATURE CONTRACTION;

EID: 84874540138     PISSN: 17468094     EISSN: 17468108     Source Type: Journal    
DOI: 10.1016/j.bspc.2012.08.004     Document Type: Article

References (49)

1. U.R. Acharya, C.M. Lim, and P. Joseph HRV analysis using correlation dimension and detrended fluctuation analysis Innovations and Technology in Biology and Medicine (ITBM-RBM) 23 2002 333 339
2. U.R. Acharya, N. Kannathal, and S.M. Krishnan Comprehensive analysis of cardiac health using heart rate signals Physiological Measurement 25 2004 1139 1151
3. U.R. Acharya, E.C.P. Chua, K.C. Chua, C.M. Lim, and T. Tamura Analysis and automatic identification of sleep stages using higher order spectra International Journal of Neural Systems 20 6 2010 509 521
4. Acharya, U.R., Ng, E.Y.K., Sree, S.V., Chua, K.C., Chattopadhyay, S. Higher order spectra analysis of breast thermograms for the automated identification of breast cancer, Expert Systems, in press.
5. U.R. Acharya, K.C. Chua, E.Y.K. Ng, W. Yu, and C. Chee Application of higher order spectra for the identification of diabetes retinopathy stages Journal of Medical Systems 32 6 2008 481 488
6. P.S. Addison Wavelet transforms and the ECG: a review Physiological Measurement 26 5 2005 R155 R199
7. T.O. Aje, and M. Miller Cardiovascular disease: a global problem extending into the developing world The World Journal of Cardiology 1 December (1) 2009 3 10
8. S.S. Anand, and S. Yusuf Stemming the global tsunami of cardiovascular disease Lancet 377 February (9765) 2011 529 532
9. C.M. Bishop Neural Networks for Pattern Recognition 1995 Clarendon Press
10. Brij N. Singh, and Arvind K. Tiwari Optimal selection of wavelet basis function applied to ECG signal denoising Digital Signal Processing 16 May (3) 2006 275 287
11. P. deChazal, and R.B. Reilly A patient-adapting heartbeat classifier using ECG morphology and heartbeat interval features IEEE Transactions on Biomedical Engineering 53 December (12) 2006 2535 2543
12. N. Christianini, and J.S. Taylor An Introduction to Support Vector Machines and Other Kernel Based Learning Methods 2000 Cambridge University Press
13. K.C. Chua, V. Chandran, R. Acharya, and C.M. Lim Higher Order Spectral (HOS) analysis of epileptic EEG signals Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE 22-26 August 2007 6495 6498
14. K.C. Chua, V. Chandran, U.R. Acharya, and C.M. Lim Cardiac state diagnosis using higher order spectra of heart rate variability Journal of Medical & Engineering Technology 32 2 2008 145 155
15. K.C. Chua, V. Chandran, Rajendra Acharya, and C.M. Lim Automatic identification of epilepsy by HOS and power spectrum parameters using EEG signals: a comparative study Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE 20-25 August 2008 3824 3827
16. K.C. Chua, V. Chandran, U.R. Acharya, and C.M. Lim Analysis of epileptic EEG signals using higher order spectra Journal of Medical & Engineering Technology, UK 33 1 2009 42 50
17. K.C. Chua, V. Chandran, U.R. Acharya, and C.M. Lim Application of higher order statistics/spectra in biomedical signals-a review Medical Engineering and Physics 32 7 2010 679 689
18. D.A. Coast, R.M. Stern, G.G. Cano, and S.A. Briller An approach to cardiac arrhythmia analysis using hidden Markov models IEEE Transactions on Biomedical Engineering 37 September (9) 1990 826 836
19. Abdelhamid Daamouche, Latifa Hamami, Naif Alajlan, and Farid Melgani A wavelet optimization approach for ECG signal classification Biomedical Signal Processing and Control 2011 Available online 30 July
20. Dingfei Ge, Narayanan Srinivasan, and Shankar M. Krishnan Cardiac arrhythmia classification using autoregressive modelling BioMedical Engineering Online 1 5 2002 1 12
21. Zumray Dokur, and Tamer Olmez ECG beat classification by a novel hybrid neural network Computer Methods and Programs in Biomedicine 66 September 2-3 2001 167 181
22. R.O. Duda, P.E. Hart, and D.G. Stork Pattern Classification 2nd edition 2001 Wiley
23. Terrill Fancott, and David H. Wong A minicomputer system for direct high speed analysis of cardiac arrhythmia in 24 h ambulatory ECG tape recordings IEEE Transactions on Biomedical Engineering BME-27 December (12) 1980 685 693
24. A.L. Goldberger Clinical Electrocardiography: A Simplified Approach 7th edition 2006 Elsevier
25. Steve Gunn, May 1998. Support Vector Machines for classification and Regression, Technical Report, University of Southampton.
26. S.S. Haykin Neural Networks: A Comprehensive Foundation 2nd edition 1999 Prentice Hall
27. Yu Hen Hu, S. Palreddy, and W.J. Tompkins A patient-adaptable ECG beat classifier using a mixture of experts approach IEEE Transactions on Biomedical Engineering 44 September (9) 1997 891 900
28. H.V. Huikuri, A. Castellanos, and R.J. Myerburg Sudden death due to cardiac arrhythmias The New England Journal of Medicine 345 November (20) 2001 1473 1482
29. O.T. Inan, L. Giovangrandi, and G.T.A. Kovacs Robust neural-network-based classification of premature ventricular contractions using wavelet transform and timing interval features IEEE Transactions on Biomedical Engineering 53 December (12) 2006 2507 2515
30. T. Ince, S. Kiranyaz, and M. Gabbouj A generic and robust system for automated patient-specific classification of ECG signals IEEE Transactions on Biomedical Engineering 56 May (5) 2009 1415 1426
31. Wei Jiang, and G. Seong Kong Block-based neural networks for personalized ECG signal classification IEEE Transactions on Neural Networks 18 November (6) 2007 1750 1761
32. L. Khadra, A.S. Al-Fahoum, and S. Binajjaj A quantitative analysis approach for cardiac arrhythmia classification using higher order spectral techniques Biomedical Engineering, IEEE Transactions 52 11 2005 1840 1845
33. A. Khazaee, and A. Ebrahimzadeh Classification of electrocardiogram signals with support vector machines and genetic algorithms using power spectral features Biomedical Signal Processing and Control 5 October (4) 2010 252 263
34. B.-U. Kohler, C. Hennig, and R. Orglmeister The principles of software QRS detection Engineering in Medicine and Biology Magazine, IEEE 21 January-February (1) 2002 42 57
35. Kang-Ping Lin, and W.H. Chang QRS feature extraction using linear prediction IEEE Transactions on Biomedical Engineering 36 October (10) 1989 1050 1055
36. M. Lagerholm, C. Peterson, G. Braccini, L. Edenbrandt, and L. Sornmo Clustering ECG complexes using Hermite functions and self-organizing maps IEEE Transactions on Biomedical Engineering 47 July (7) 2000 838 848
37. Mark, R., Moody, G., 1997, May. MIT-BIH Arrhythmia Database [Online]. Available: http://ecg.mit.edu/dbinfo.html.
38. Roshan Joy Martis, Chandan Chakraborty, and Ajoy K. Ray A two-stage mechanism for registration and classification of ECG using Gaussian mixture model Pattern Recognition 42 November (11) 2009 2979 2988
39. F. Melgani, and Y. Bazi Classification of electrocardiogram signals with support vector machines and particle swarm optimization IEEE Transactions on Information Technology in Biomedicine 12 September (5) 2008 667 677
40. Amit K. Mishra, and Shantanu Raghav Local fractal dimension based ECG arrhythmia classification Biomedical Signal Processing and Control 5 April (2) 2010 114 123
41. C.L. Nikias, and M.R. Raghuveer Bispectrum estimation: a digital signal processing framework Proceedings of the IEEE 75 July (7) 1987 869 891
42. C.L. Nikias Higher-order spectral analysis Engineering in Medicine and Biology Society, 1993. Proceedings of the 15th Annual International Conference of the IEEE 1993 319
43. S. Osowski, and Tran Hoai Linh ECG beat recognition using fuzzy hybrid neural network IEEE Transactions on Biomedical Engineering 48 November (11) 2000 1265 1271
44. Jiapu Pan, and Willis J. Tompkins A real-time QRS detection algorithm IEEE Transactions on Biomedical Engineering BME-32 March (3) 1985 230 236
45. O. Sayadi, M.B. Shamsollahi, and G.D. Clifford Robust detection of premature ventricular contractions using a wave-based Bayesian framework IEEE Transactions on Biomedical Engineering 57 February (2) 2010 353 362
46. J.A.K. Suykens, and J. Vandewalle Least square support vector machine classifiers Neural Processing Letters 9 1999 293 300
47. Elif Derya Ubeyli Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents Computer Methods and Programs in Biomedicine 93 March (3) 2009 313 321
48. WHO. Cardiovascular diseases (CVDs) [Internet]. [cited 18.02.11]. Available from: http://www.who.int/mediacentre/factsheets/fs317/en/index.html.
49. J.M. Zurada Introduction to Artificial Neural Systems 1992 West