Effect of Multiscale PCA De-noising in ECG Beat Classification for Diagnosis of Cardiovascular Diseases

Circuits, Systems, and Signal Processing

Volumn 34, Issue 2, 2015, Pages 513-533

  Alickovic, Emina ^a   Subasi, Abdulhamit ^a  

^a INTERNATIONAL BURCH UNIVERSITY   (Bosnia and Herzegovina)

Author keywords

AR modeling; k Nearest neighbor (k NN); Multilayer perceptron (MLP); Radial basis function networks (RBFN); Simple logistic; Support vector machines (SVM)

Indexed keywords

AUDIO SYSTEMS; CARDIOLOGY; CLASSIFICATION (OF INFORMATION); COMPUTER AIDED DIAGNOSIS; DISEASES; ELECTROCARDIOGRAPHY; EXTRACTION; FEATURE EXTRACTION; FUNCTIONS; MOTION COMPENSATION; MULTILAYER NEURAL NETWORKS; MULTILAYERS; NEAREST NEIGHBOR SEARCH; RADIAL BASIS FUNCTION NETWORKS; SUPPORT VECTOR MACHINES;

AR MODELS; CLASSIFICATION ACCURACY; ECG BEAT CLASSIFICATIONS; ELECTROCARDIOGRAM SIGNAL; K-NEAREST NEIGHBORS; MULTI LAYER PERCEPTRON; MULTI-SCALE PRINCIPAL COMPONENT ANALYSIS; RADIAL BASIS FUNCTION NETWORKS (RBFN);

BIOMEDICAL SIGNAL PROCESSING;

EID: 84922265438     PISSN: 0278081X     EISSN: 15315878     Source Type: Journal    
DOI: 10.1007/s00034-014-9864-8     Document Type: Article

References (66)

1. N. Acır, A support vector machine classifier algorithm based on a perturbation method and its application to ECG beat recognition systems. Expert Syst. Appl. 31, 150–158 (2006)
2. P.M. Agante, J.P. Marques de Sa, ECG noise filtering using wavelets with soft-thresholding methods. Comput. Cardiol. 26, 535–538 (1999)
3. M. Arif, M. U. Akram, and F. A. Afsar. Arrhythmia beat classification using pruned fuzzy k-nearest neighbor classifier soft computing and pattern recognition, in SOCPAR ’09, International Conference, 2009, pp. 37–42
4. M. Arif, M.U. Akram, F.A.A. Minhas, Pruned fuzzy K-nearest neighbor classifier for beat classification. J. Biomed. Sci. Eng. 3(4), 380–389 (2010)
5. B.M. Asl, S.K. Setarehdan, M. Mohebbi, Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal. Artif. Intell. Med. 44, 51–64 (2008)
6. B.R. Bakshi, Multiscale PCA with application to multivariate statistical process monitoring. AIChE J. 44(7), 1596–1610 (1998)
7. R. Besrour, Z. Lachiri, and N. Ellouze. ECG beat classifier using support vector machine. in Proceedings of the Third International Conference on Information and Communication Technologies: From Theory to Applications, ICTTA, 2008, pp. 1–5
8. B. E. Boser and I. M. Guyon. A training algorithm for optimal margin classifiers. in Fifth annual workshop on computational learning theory, Pittsburgh: ACM, 1992
9. P. de Chazal, M. O’Dwyer, R.B. Reilly, Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 51(7), 1196–1206 (2004)
10. I. Christov, I. Jekova, G. Bortolan, Premature ventricular contraction classification by the Kth nearest neighbors rule. Physiol. Meas. 26, 123–130 (2005)
11. G.D. Clifford, F. Azuaje, P.E. McSharry, Advanced Methods and Tools for ECG Data Analysis (Artech House, Norwood, 2006)
12. D. Donoho, I. Johnstone, Adapting to unknown smoothness via wavelet shrinkage. J. Am. Stat. Assoc. 90, 1200–1223 (1995)
13. K.L. Du, M.N.S. Swamy, Neural Networks in a Softcomputing Framework (Springer, London, 2006)
14. S. Dutta, A. Chatterjee, S. Munshi, Correlation technique and least square support vector machine combine for frequency domain based ECG beat classification. Med. Eng. Phys. 32, 1161–1169 (2010)
15. M. Faezipour et al., A patient-adaptive profiling scheme for ECG beat classification. IEEE Trans. Inf. Technol. Biomed. 14(5), 1153–1165 (2010)
16. M. Faezipour, T. M. Tiwari, A. Saeed, M. Nourani, and L. S. Tamil. Wavelet-based denoising and beat detection of ECG signal. in IEEE/NIH Life Science Systems and Applications Workshop (LiSSA 2009), 2009
17. A.H. Fielding, Cluster and Classification Techniques for the Biosciences (Cambridge University Press, The Edinburgh Building, Cambridge, 2007)
18. T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. (Springer, New York, 2009)
19. E. Gokgoz, A. Subasi, Effect of multiscale PCA de-noising on EMG signal classification for diagnosis of neuromuscular disorders. J. Med. Syst. 38(4), 1–10 (2014)
20. H. Gothwal, S. Kedawat, R. Kumar, Cardiac arrhythmias detection in an ECG beat signal using fast fourier transform and artificial neural network. Biomed. Sci. Eng. 4, 289–296 (2011)
21. S. Goto, M. Nakamura, K. Uosaki, On-line spectral estimation of nonstationary time series based on AR model parameter estimation and order selection with a forgetting factor. IEEE Trans. Signal Process. 43, 1519–1522 (1995)
22. H.V. Huikuri, A. Castellanos, R.J. Myerburg, Sudden death due to cardiac arrhythmias. N. Engl. J. Med. 345(20), 1473–1482 (2001)
23. I. Jekova, G. Bortolan, I. Christov, Assessment and comparison of different methods for heartbeat classification. Med. Eng. Phys. 30, 248–257 (2008)
24. M. Kania, M. Fereniec, R. Maniewski, Wavelet denoising for multi-lead high resolution ECG signals. Meas. Sci. Rev. 7(2), 30–33 (2007)
25. S. Karimifard, A. Ahmadian, and M. Khoshnevisan. Morphological heart arrhythmia detection using hermitian basis functions and kNN classifier. in 28th Annual International Conference of the IEEE Engineering in Medicine and Biology 1–15, 2006, pp. 4489–4492
26. A. Khazaee, A. Ebrahimzadeh, Classification of electrocardiogram signals with support vector machines and genetic algorithms using power spectral features. Biomed. Signal Process. Control 5, 252–263 (2010)
27. S. Kiranyaz, T. Ince, J. Pulkkinen, M. Gabbouj, Personalized long-term ECG classification: a systematic approach. Expert Syst. Appl. 38(4), 3220–3226 (2011)
28. B.U. Kohler, C. Henning, R. Orglmeister, The principles of software QRS detection. IEEE Eng. Med. Biol. Mag. 21(1), 42–57 (2002)
29. Y. Kutlu, K. Damla, A multi-stage automatic arrhythmia recognition and classification system. Comput. Biol. Med. 41, 37–45 (2011)
30. S. le Cessie and J. C. van Houwelingen. (2012) Weka 3 - Data Mining with Open Source Machine Learning Software in Java. [Online]. http://weka.sourceforge.net/doc.dev/weka/classifiers/functions/Logistic.html
31. C. Li, C. Zheng, C.F. Tai, Detection of ECG characteristic points using wavelet transforms. IEEE Trans. Biomed. Eng. 42(1), 21–28 (1995)
32. R.J. Martis, U.R. Acharya, K.M. Mandana, A.K. Ray, C. Chakraborty, Application of principal component analysis to ECG signals for automated diagnosis of cardiac health. Expert Syst. Appl. 39, 11792–11800 (2012)
33. F. Melgani, B. Yakoub, Classification of ECG signals with support vector machine and particle swarm optimization III. IEEE Trans. Inf. Technol. Biomed. 12, 667–677 (2008)
34. MIT-BIH Arrhythmia Database Directory. [Online]. http://www.physionet.org/physiobank/database/html/mitdbdir/mitdbdir.htm (2014)
35. J. G. Murphy and M. A. Lloyd, Mayo Clinic Cardiology: Concise Textbook, 3rd ed. Rochester, MN: Mayo Clinic Scientific Press and New York: Informa Healthcare USA, 2007
36. J. Muthuswamy, N.V. Thakor, Spectral analysis methods for neurological signals. J. Neurosci. Methods 83, 1–14 (1998)
37. J. Nadal and M. ve Bossan. Classification of cardiac arrhythmia based on principal components analysis and feed forward neural networks. in IEEE Proceedings on Computers in Cardiology, pp. 341–344, 1993
38. Y. Özbay, R. Ceylan, B. Karlik, A fuzzy clustering neural network architecture for classification of ECG arrhythmias. Comput. Biol. Med. 36, 376–388 (2006)
39. Y. Özbay, R. Ceylan, B. Karlik, Integration of type-2 fuzzy clustering and wavelet transform in a neural network based ECG classifier. Expert Syst. Appl. 38, 1004–1010 (2011)
40. J. Pan, W.J. Tompkins, A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. BME–32(3), 230–236 (1985)
41. J. Pardey, S. Roberts, L. Tarassenko, A review of parametric modelling techniques for EEG analysis. Med. Eng. Phys. 18(1), 2–11 (1996)
42. T. Poggio, F. Girosi, Networks for approximation and learning. Proc. IEEE 78(9), 1481–1497 (1990)
43. M.J.D. Powell, Radial basis functions for multivariable interpolation, in Algorithms for Approximation, ed. by J.C. Mason, M.G. Cox (Clarendon Press, Oxford, 1987)
44. G. K. Prasad and J. S. Sahambi. Classification of ECG arrhythmias using multiresolution analysis and neural networks. in Conference on convergent technologies for Asia-Pacific region (TENCON 2003) 1, 2003, pp. 227–231
45. Z. Qin, J. Chen, Y. Liu, J. Lu, Evolving RBF Neural Networks for Pattern Classification. Lecture Notes in Computer Science vol. 3801, pp. 957–964 (2005)
46. S. Raghav and A. K. Mishra. Fractal feature based ECG arrhythmia classification. in Proceedings of IEEE TENCON, 2008
47. R. Rosenblatt, The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65, 386–408 (1958)
48. S.L. Salzberg, On comparing classifiers: pitfalls to avoid and a recommended approach. Data Min. Knowl. Discov. 1(3), 317–328 (2007)
49. O. Sayadi, M.B. Shamsollahi, G.D. Clifford, Robust detection of premature ventricular contractions using a wave-based bayesian framework. IEEE Trans. Biomed. Eng. 57(2), 353–362 (2010)
50. J.L. Semmlow, Biosignal and Biomedical Image Processing: Matlab-Based Applications (Marcel Dekker Inc, Hoboken, 2004)
51. C.P. Shen et al., Detection of cardiac arrhythmia in electrocardiograms using adaptive feature extraction and modified support vector machines. Expert Syst. Appl. 39, 7845–7852 (2012)
52. M.H. Song, J. Lee, K.J. Cho, K.J. Lee, S.K. Yoo, Support vector machine based arrhythmia classification using reduced features. Int. J. Control Automat. Syst. 3(4), 571–579 (2005)
53. A. Subasi, E. Erçelebi, A. Alkan, E. Koklukaya, Comparison of subspace-based methods with AR parametric methods in epileptic seizure detection. Comput. Biol. Med. 36, 195–208 (2006)
54. M.S. Thaler, The Only EKG Book You’ll Ever Need, 3rd edn. (Lippincott Williams & Wilkins, Philadelphia, 1999)
55. V. Vapnik, The nature of statistical learning theory (Springer, New York, 1995)
56. J.T. Willerson, H. Wellens, J.N. Cohn, D.R. Holmes, Cardiovascular Medicine, 3rd edn. (Springer, London, 2007)
57. I.H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools And Techniques (Morgan Kaufmann Publishers (Elsevier), San Francisco, 2005)
58. P.A. Wolf, R.D. Abbott, W.B. Kannel, Atrial fibrillation as an independent risk factor for stroke: the Framingham study. Stroke 22(8), 983–988 (1991)
59. Y.-C. Yeh, W.-J. Wang, C.W. Chiou, A novel fuzzy c-means method for classifying heartbeat cases from ECG signals. Measurement 43, 1542–1555 (2010)
60. Z. Yong, H. Wenxue, and X. Yonghong. ECG beats feature extraction based on geometric algebra computational intelligence and software engineering. in International Conference on Computational Intelligence and Software Engineering, 2009 (CiSE 2009), 2009, pp. 1–3
61. S.-N. Yu, K.-T. Chou, Integration of independent component analysis and neural networks for ECG beat classification. Expert Syst. Appl. 34, 2841–2846 (2008)
62. A.E. Zadeh, A. Khazaee, V. Ranaee, Classification of the electrocardiogram signals using supervised classifiers and efficient features. Comput. Methods Program Biomed. 99, 179–194 (2010)
63. D. Zhang. Wavelet approach for ECG baseline wander correction and noise reduction. in IEEE Engineering in Medicine and Biology Society, 2005, pp. 1212–1215
64. R. Zhang, G. McAllister, B. Scotney, S. McClean, G. Houston, Combining wavelet analysis and Bayesian networks for the classification of auditory brainstem response. IEEE Trans. Info. Tech. Biomed 10(3), 458–467 (2006)
65. Z. Zidelmal, A. Amirou, D. Ould-Abdeslamb, J. Merckleb, ECG beat classification using a cost sensitive classifier. Comp. Methods Programs Biomed. 11(3), 570–577 (2013)
66. M.H. Zweig, G. Campbell, Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin. Chem. 39, 561–577 (1993)