Application of higher order cumulant features for cardiac health diagnosis using ECG signals

International Journal of Neural Systems

Volumn 23, Issue 4, 2013, Pages

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

^a DEPARTMENT OF ELECTRONICS AND COMPUTER ENGINEERING   (Singapore)

^b UNIVERSITY OF MALAYA   (Malaysia)

^c Fortis Hospitals   (India)

^d INDIAN INSTITUTE OF TECHNOLOGY   (India)

Author keywords

classifier; cumulants; Electrocardiogram; higher order statistics; principal component analysis (PCA)

Indexed keywords

ATRIAL PREMATURE CONTRACTION; AUTOMATIC DIAGNOSTIC SYSTEMS; CUMULANTS; ELECTRICAL ACTIVITY OF THE HEART; HIGHER ORDER CUMULANTS; LEAST SQUARE SUPPORT VECTOR MACHINES; NONLINEAR AND NON-STATIONARY SIGNALS; VENTRICULAR PREMATURE CONTRACTIONS;

CLASSIFIERS; DISCRETE WAVELET TRANSFORMS; HEART; HIGHER ORDER STATISTICS; PRINCIPAL COMPONENT ANALYSIS; SUPPORT VECTOR MACHINES;

ELECTROCARDIOGRAPHY;

ARTICLE; ARTIFICIAL NEURAL NETWORK; ELECTROCARDIOGRAPHY; HEART; HEART ARRHYTHMIA; HUMAN; PATHOPHYSIOLOGY; PHYSIOLOGY; PRINCIPAL COMPONENT ANALYSIS; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; WAVELET ANALYSIS;

ARRHYTHMIAS, CARDIAC; ELECTROCARDIOGRAPHY; HEART; HUMANS; NEURAL NETWORKS (COMPUTER); PRINCIPAL COMPONENT ANALYSIS; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINES; WAVELET ANALYSIS;

EID: 84879112946     PISSN: 01290657     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0129065713500147     Document Type: Article

References (98)

1. S. S. Anand and S. Yusuf, Stemming the global tsunami of cardiovascular disease, Lancet 377(9765) (2011) 529-532.
2. D. Lloyd-Jones, R. Adams, M. Carnethon, G. De Simone, T. B. Ferguson, K. Flegal, E. Ford, K. Furie, A. Go, K. Greenlund, N. Haase, S. Hailpern, M. Ho, V. Howard, B. Kissela, S. Kittner, D. Lackland, L. Lisabeth, A. Marelli, M. McDermott, J. Meigs, D. Mozaffarian, G. Nichol, C. O'Donnell, V. Roger, W. Rosamond, R. Sacco, P. Sorlie, R. Stafford, J. Steinberger, T. Thom, S. Wasserthiel-Smoller, N. Wong, J. Wylie-Rosett and Y. Hong, American heart association statistics committee and stroke statistics subcommittee. Heart disease and stroke statistics-2009 update: A report from the american heart association statistics committee and stroke statistics subcommittee, Circulation 119(3) (2009) 480-486.
3. M. Hadhoud, M. Eladawy and A. Farag, Computer aided diagnosis of cardiac arrhythmias, in Proc. IEEE Int. Conf. Computer Engineering and Systems (2006), pp. 262-265.
4. Fancott, Terrill, Wong and H. David, A minicomputer system for direct high speed analysis of cardiac arrhythmia in 24 h ambulatory ECG tape recordings, IEEE Trans. Biomed. Eng. 27(12) (1980) 685-693. (Pubitemid 11460963)
5. K.-P. Lin and W. H. Chang, QRS feature extraction using linear prediction, IEEE Trans. Biomed. Eng. 36(10) (1989) 1050-1055. (Pubitemid 20088831)
6. H. Dai, H. Zhang and W. Wang, Structural reliability assessment by local approximation of limit state functions using adaptive Markov chain simulation and support vector regression, Comput.-Aided Civ. Infrastruct. Eng. 27(9) (2012) 676-686.
7. D. A. Coast, R. M. Stern, G. G. Cano and S. A. Briller, An approach to cardiac arrhythmia analysis using hidden Markov models, IEEE Trans. Biomed. Eng. 37(9) (1990) 826-836. (Pubitemid 20368417)
8. E. D. Wandekokem, E. Mendel, F. Fabris, M. Valen-tim, R. J. Batista, F. M. Varejao and T. W. Rauber, Diagnosing multiple faults in oil rig motor pumps using support vector machine classifier ensembles, Integr. Comput.-Aided Eng. 18(1) (2011) 61-74.
9. V. Jumutc, P. Zayakin and A. Borisov, Ranking-based kernels in applied biomedical diagnostics using support vector machine, Int. J. Neural Syst. 21(6) (2011) 459-473.
10. F. Melgani and Y. Bazi, Classification of electrocardiogram signals with support vector machines and particle swarm optimization, IEEE Trans. Inform. Technol. Biomed. 12(5) (2008) 667-677.
11. V. Plevris and M. Papadrakakis, A hybrid particle swarm-gradient algorithm for global structural optimization, Comput.-Aided Civ. Infrastruct. Eng. 26(1) (2011) 48-68.
12. H. Tao, J. M. Zain, M. M. Ahmed, A. N. Abdalla and W. Jing, A wavelet-based particle swarm optimization algorithm for digital image watermarking, Integr. Comput.-Aided Eng. 19(1) (2012) 81-91.
13. DingfeiGe, N. Srinivasan and S. M. Krishnan, Cardiac arrhythmia classification using autoregressive modelling, BioMed. Eng. Online 1(5) (2002) 1-12.
14. R. J. Martis, C. Chakraborty and A. K. Ray, A two-stage mechanism for registration and classification of ECG using Gaussian mixture model, Pattern Recognit. 42(11) (2009) 2979-2988.
15. E. D. Ubeyli, Combining recurrent neural networks with eigenvector methods for classification of ECG beats, Digital. Signal Process. 19(2) (2009) 320-329.
16. A. Panakkat and H. Adeli, Recurrent neural net work for approximate earthquake time and loca tion prediction using multiple seismicity indicators, Comput.-Aided Civil Infrastruct. Eng. 24(4) (2009) 280-292.
17. S. Freitag, W. Graf and M. Kaliske, Recurrent neural networks for fuzzy data, Integr. Comput.-Aided Eng. 18(3) (2011) 265-280.
18. D. Theodoridis, Y. Boutalis and M. Christodoulou, Dynamical recurrent neuro-fuzzy identification schemes employing switching parameter hoping, Int. J. Neural Syst. 22(2) (2012) 1250004-1250016.
19. C. M. Lin, A. B. Ting, C. F. Hsu and C. M. Chung, Adaptive control for MIMO uncertain nonlinear systems using recurrent wavelet neural network, Int. J. Neural Syst. 22(1) (2012) 37-50.
20. E. D. Ubeyli, ECG beats classification using mul-ticlass support vector machines with error correcting output codes, Digit. Signal Process. 17(3) (2007) 675-684. (Pubitemid 46551740)
21. A. Shoeb, T. Pang, J. Guttag and S. Schachter, Non invasive computerized system for automatically initiating vagus nerve stimulation following patient-specific detection of seizures or epileptiform discharges, Int. J. Neural Syst. 19(3) (2009) 157-172.
22. S. Ghosh-Dastidar and H. Adeli, Improved spiking neural networks for EEG classification and epilepsy and seizure detection, Integr. Comput.-Aided Eng. 14(3) (2007) 187-212. (Pubitemid 47191475)
23. S. Ghosh-Dastidar and H. Adeli, A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection, Neural Netw. 22 (2009) 1419-1431.
24. H. Adeli and S. Ghosh-Dastidar (in corroboration with Nahid Dadmehr), Automated EEG-based Diagnosis of Neurological Disorders-Inventing the Future of Neurology (CRC Press, Taylor & Francis, Boca Raton, Florida, 2010).
25. H. Kim and H. Adeli, Wavelet hybrid feedback-least mean square algorithm for robust control of cable-stayed bridges, J. Bridge Eng. ASCE, 10(2) (2005a) 116-123. (Pubitemid 40303919)
26. H. Kim and H. Adeli, Wavelet hybrid feedback-least mean square algorithm for robust control of cable-stayed bridges, J. Bridge Eng. ASCE, 10(2) (2005b) 116-123. (Pubitemid 40303919)
27. R. K. H. Galvavo and T. Yoneyama, Improving the discriminatory capabilities of a neural classifier by using a biased-wavelet layer, Int. J. Neural Syst. 09(3) (1999) 167-174.
28. H. Adeli and A. Samant, An adaptive conjugate gradient neural network-wavelet model for traffic incident detection, Comput.-Aided Civ. Infrastruct. Eng. 13(4) (2000) 251-260.
29. S. Ghosh-Dastidar and H. Adeli, Wavelet-clustering-neural network model for freeway incident detection, Comput.-Aided Civ. Infrastruct. Eng. 18(5) (2003) 325-338. (Pubitemid 36971722)
30. S. Ghosh-Dastidar and H. Adeli, Neural network-wavelet micro-simulation model for delay and queue length estimation at freeway work zones, J. Transp. Eng. ASCE, 132(4) (2006) 331-341. (Pubitemid 43870498)
31. X. Jiang and H. Adeli, Pseudospectra, MUSIC, and dynamic wavelet neural network for damage detection of highrise buildings, Int. J. Numer. Methods Eng. 71(5) (2007) 606-629. (Pubitemid 47190309)
32. S. Cho, Modelling and prediction for chaotic FIR laser attractor using rational function neural network, Int. J. Neural Syst. 11(1) (2001) 89-99.
33. Y. Zhao and M. Small, Equivalence between feeling the pulse on the human wrist and the pulse pressure wave at fingertip, Int. J. Neural Syst. 15(4) (2005) 277-286. (Pubitemid 44099495)
34. S. L. Hung and H. Adeli, Parallel backpropagation learning algorithms on cray Y-MP8/864 supercomputer, Neurocomputing 5(6) (1993) 287-302. (Pubitemid 23341366)
35. S. L. Hung and H. Adeli, Object-Oriented back propagation and its application to structural design, Neu-rocomputi ng 6(1) (1994) 45-55. (Pubitemid 24075160)
36. R. A. Osornio-Rios, J. P. Amezquita-Sanchez, R. J. Romero-Troncoso and A. Garcia-Perez, MUSIC-neural network analysis for locating structural damage in truss-type structures by means of vibrations, Comput.-Aided Civ. Infrastruct. Eng. 27(9) (2012) 687-698.
37. U. R. Abeyratne, G. Zhang and P. Saratchandran, EEG source localization: A comparative study of classical and neural network methods, Int. J. Neural Syst. 11(4) (2001) 509-521.
38. M. Cabrerizo, M. Ayala, M. Goryawala, P. Jayakar and M. Adjouadi, A new parametric feature descriptor for the classification of epileptic and control EEG records in pediatric population, Int. J. Neural Syst. 22(2) (2012) 1250001-1250016.
39. U. R. Acharya, E. C. P. Chua, K. C. Chua, L. C. Min and T. Tamura, Analysis and automatic identification of sleep stages using higher order spectra, Int. J. Neural Syst. 20(6) (2010) 509-521.
40. R. Orre, A. Bate, G. N. Noren, E. Swahn, S. Arn-borg and I. R. Edwards, A bayesian recurrent neural network for unsupervised pattern recognition in large incomplete data sets, Int. J. Neural Syst. 15(3) (2005) 207-222. (Pubitemid 44099502)
41. Z. Sankari and H. Adeli, HeartSaver: A mobile cardiac monitoring system for auto-detection of atrial fibrillation, myocardial infarction, and atrio-ventricular block, Comput. Biol. Med. 41 (2011) 211-220.
42. A. Khazaee and A. Ebrahimzadeh, Classification of electrocardiogram signals with support vector machines and genetic algorithms using power spectral features, Biomed. Signal Process. Control 5 (2010) 252-263.
43. A. Samant and H. Adeli, Feature extraction for traffic incident detection using wavelet transform and linear discriminant analysis, Comput.-Aided Civ. Infrastruct. Eng. 13(4) (2000) 241-250.
44. A. Karim and H. Adeli, Incident detection algorithm using wavelet energy representation of traffic patterns, J. Transp. Eng. ASCE, 128(3) (2002) 232-242. (Pubitemid 34500232)
45. X. Jiang and H. Adeli, Wavelet packet-autocorrelation function method for traffic flow pattern analysis, Comput.-Aided Civ. Infrastruct. Eng. 19(5) (2004) 324-337. (Pubitemid 38964850)
46. E. Figueiredo, G. Park, J. Figueiras, C. Farrar and K. Worden, Influence of the autoregressive model order on damage detection, Comput.-Aided Civ. Infrastruct. Eng. 26(3) (2011) 225-238.
47. X. Jiang, S. Mahadevan and H. Adeli, Bayesian wavelet packet denoising for structural system identification, Struct. Contr. Health Monit. 14(2) (2007) 333-356. (Pubitemid 46491912)
48. H. Adeli, S. Ghosh-Dastidar and N. Dadmehr, A spatio-temporal wavelet-chaos methodology for EEG-based diagnosis of alzheimer's disease, Neu-rosci. Lett. 444(2) (2008) 190-194.
49. J. Xiang and M. Liang, Wavelet-based detection of beam cracks using modal shape and frequency measurements, Comput.-Aided Civ. Infrastruct. Eng. 27(6) (2012) 439-454.
50. M. Engin, ECG beat classification using neuro-fuzzy network, Pattern Recognit. Lett. 25 (2004) 1715-1722.
51. Khan and H. Yin Efficient blind image deconvolu-tion using spectral nongaussianity Integr. Comput.-Aided Eng. 19 (4) 2012 331-340.
52. U. R. Acharya, N. Kannathal and S. M. Krish-nan, Comprehensive analysis of cardiac health using heart rate signals, Physiol. Meas. 25 (2004) 1139-1151. (Pubitemid 39423316)
53. U. R. Achaya, C. M. Lim and P. Joseph, HRV analysis using correlation dimension and detrended fluctuation analysis, Innov. Technol. Biol. Med. (ITBM-RBM) 23 (2002) 333-339. (Pubitemid 36061568)
54. T. Xiong, M. Zhang, C. W. Shu, S. C. Wong and P. Zhang, High order computational scheme for a dynamic continuum model for bi-directional pedestrian flows, Comput.-Aided Civ. Infrastruct. Eng. 26(4) (2011) 297-310.
55. K. C. Chua, V. Chandran, U. R. Acharya and C. M. Lim, Analysis of epileptic EEG signals using higher order spectra, J. Med. Eng. Technol. 33(1) (2009) 42-50.
56. U. R. Acharya, S. V. Sree and J. S. Suri, Automatic detection of epileptic EEG signals using higher order cumulant features, Int. J. Neural Syst. 21(5) (2011) 403-414.
57. K. C. Chua, V. Chandran, U. R. Acharya and C. M. Lim, Cardiac state diagnosis using higher order spectra of heart rate variability, J. Med. Eng. Technol. 32(2) (2008) 145-155. (Pubitemid 351302367)
58. R. J. Martis, U. R. Acharya, A. K. Ray and C. Chakraborty, Application of higher order cumulants to ECG signals for the cardiac health diagnosis, Eng. Medicine and Biology Society, EMBC, 2011 Annual Int. Conf. IEEE, 30 August 2011-3 September (2011), pp. 1697-1700.
59. Y. Kutlu and D. Kuntalp, Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients, Comput. Methods Programs Biomed. 105(3) (2012) 257-267.
60. R. Mark and G. Moody, MIT-BIH Arrhythmia Database. [Online]. Available at http://ecg.mit.edu/dbinfo.html.
61. A. L. Goldberger, Clinical electrocardiography: A Simplified Approach, 7th edn. (Elsevier, 2006).
62. B. N. Singh and A. K. Tiwari, Optimal selection of wavelet basis function applied to ECG signal denois-ing, Digit. Signal Process. 16(3) (2006) 275-287.
63. Z. Zhou and H. Adeli, Time-frequency signal analysis of earthquake records using Mexican hat wavelets, Comput.-Aided Civ. Infrastruct. Eng. 18(5) (2003a) 379-389. (Pubitemid 36971726)
64. Z. Zhou and H. Adeli, Wavelet energy spectrum for time-frequency localization of earthquake energy, Int. J. Imaging Syst. Technol. 13(2) (2003b) 133-140.
65. H. Kim and H. Adeli, Hybrid control of smart structures using a novel wavelet-based algorithm, Comput.-Aided Civ. Infrastruct. Eng. 20(1) (2005) 7-22. (Pubitemid 40464642)
66. H. Adeli and S. Ghosh-Dastidar, Mesoscopic-wavelet freeway work zone flow and congestion feature extraction model, J. Transp. Eng. ASCE 130(1) (2004) 94-103. (Pubitemid 38119552)
67. M. Ahmadlou and H. Adeli, Wavelet-synchronization methodology: A new approach for EEG-based diagnosis of ADHD, Clini. EEG Neurosci. 41(1) (2010) 1-10.
68. M. Ahmadlou, H. Adeli and A. Adeli, Fractality and a wavelet-chaos-neural network methodology for EEG-based diagnosis of autistic spectrum disorder, J. Clin. Neurophysiol. 27(5) (2010) 328-333.
69. A. Ahmadlou, H. Adeli and A. Adeli, Fractality and a wavelet-chao methodology for EEG-based diagnosis of alzheimer's disease, Alzheimer Dis. Assoc. Dis-ord. 25(1) (2011) 85-92.
70. P. S. Addison, Wavelet transforms and the ECG: A review, Physiol. Meas. 26(5) (2005) R155-R199. (Pubitemid 41140805)
71. J. Pan and W. J. Tompkins, A real-time QRS detection algorithm, IEEE Trans. Biomed. Eng. 32(3) (1985) 230-236. (Pubitemid 15148852)
72. C. L. Nikias, Higher-order spectral analysis, Engineering in Medicine and Biology Society, 1993. Proc. 15th Annual Int. Conf. of the IEEE (1993), p. 319.
73. D. R. Brillinger, Time Series: Data Analysis and Theory, Vol. 36 (Society for Industrial Mathematics, 2001).
74. W. J. Yan and W. X. Ren, Operational modal parameter identification from power spectrum density transmissibility, Comput.-Aided Civ. Infrastruct. Eng. 27(3) (2012) 202-217.
75. G. Strang and T. Nguyen, Wavelets and Filter Banks (Wellesley Cambridge Press, 1996).
76. R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification, 2nd, edn. (Wiley, 2001).
77. S. Ghosh-Dastidar, H. Adeli and N. Dadmehr, Principal component analysis-enhanced cosine radial basis function neural network for robust epilepsy and seizure detection, IEEE Trans. Biomed. Eng. 55(2) (2008) 512-518.
78. M. Al-Naser and U. Soderstrom, Reconstruction of occluded facial images using asymmetrical principal component analysis, Integr. Comput.-Aided Eng. 19(3) (2012) 273-283.
79. C. M. Bishop, Neural Networks for Pattern Recognition (Clarendon Press, 1995).
80. S. S. Haykin, Neural networks: A comprehensive foundation, 2nd Edn. (Prentice Hall, 1999).
81. S. Gunn, Support vector machines for classification and regression, Technical report, University of Southampton, May (1998).
82. N. Christianini and J. S. Taylor, An Introduction to Support Vector Machines and Other Kernel Based Learning Methods (Cambridge University Press, 2000).
83. B. E. Bussink, A. G. Holst, L. Jespersen, J. W. Deckers, G. B. Jensen and E. Prescott, Right bundle branch block: Prevalence, risk factors and outcome in the general population: Results from the Copenhagen city heart study, Eur. Heart J. 34(2) (2013) 138-146.
84. S. Baldasseroni, C. Opasich, M. Gorini, D. Lucci, N. Marchionni, M. Marini, et al., Left bundle-branch block is associated with increased 1-year sudden and total mortality rate in 5517 outpatients with congestive heart failure: A report from the Italian network on congestive heart failure, Am. Heart J. 143(3) (2002) 398-405. (Pubitemid 34185169)
85. N. M. Van Hemel, Left is worse than right: The outcome of bundle branch block in middle-aged men, Eur. Heart J. 26(21) (2005) 2222-2223. (Pubitemid 41541383)
86. A. Di Chiara, Right bundle branch block during the acute phase of myocardial infarction: Modern redefinitions of old concepts, Eur. Heart J. 27(1) (2006) 1-2. (Pubitemid 41785919)
87. M. W. Massing, R. J. Jr. Simpson, P. M. Rautaharju, P. J. Schreiner, R. Crow and G. Heiss, Usefulness of ventricular premature complexes to predict coronary heart disease events and mortality (from the Atherosclerosis Risk In Communities cohort), Am. J. Cardiol. 98(12) (2006) 1609-1612. (Pubitemid 44827968)
88. P. Cheriyath, F. He, I. Peters, X. Li, P. Jr. Alagona, C. Wu, M. Pu, W. E. Cascio and D. Liao, Relation of atrial and/or ventricular premature complexes on a two-minute rhythm strip to the risk of sudden cardiac death (the Atherosclerosis Risk in Communities [ARIC] study), Am. J. Cardiol. 107(2) (2011) 151-155.
89. Y. H. Hu, S. Palreddy and W. J. Tompkins, A patient-adaptable ECG beat classifier using a mixture of experts approach, IEEE Trans. Biomed. Eng. 44(9) (1997) 891-900. (Pubitemid 27357683)
90. M. Lagerholm, C. Peterson, G. Braccini, L. Edenbrandt and L. Sornmo, Clustering ECG complexes using Hermite functions and self-organizing maps, IEEE Trans. Biomed. Eng. 47(7) (2000) 838-848. (Pubitemid 30421818)
91. S. Osowski and T. HoaiLinh, ECG beat recognition using fuzzy hybrid neural network, IEEE Trans. Biomed. Eng. 48(11) (2000) 1265-1271.
92. P. de Chazal and R. B. Reilly, A patient-adapting heartbeat classifier using ECG morphology and heartbeat interval features, IEEE Trans. Biomed. Eng. 53(12) (2006) 2535-2543. (Pubitemid 46019541)
93. W. Jiang and G. S. Kong, Block-Based neural networks for personalized ECG signal classification, IEEE Trans. Neural Netw. 18(6) (2007) 1750-1761. (Pubitemid 350148425)
94. 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 Trans. Biomed. Eng. 53(12) (2006) 2507-2515. (Pubitemid 46019538)
95. T. Ince, S. Kiranyaz and M. Gabbouj, A generic and robust system for automated patient-specific classification of ECG signals, IEEE Trans. Biomed. Eng. 56(5) (2009) 1415-1426.
96. O. Sayadi, M. B. Shamsollahi and G. D. Clifford, Robust detection of premature ventricular contractions using a wave-based bayesian framework, IEEE Trans. Biomed. Eng. 57(2) (2010) 353-362.
97. R. J. Martis, U. R. Acharya, K. M. Mandana, A. K. Ray and C. Chakraborty, Application of principal component analysis to ECG signals for automated diagnosis of cardiac health, Expert Syst. Appl. 39(14) (2012) 11792-11800.
98. R. J. Martis, U. R. Acharya, K. M. Mandana, A. K. Ray and C. Chakraborty, Cardiac decision making using higher order spectra, Biomedical Signal Process. Control 8(2) (2013) 193-203.