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Role of independent component Analysis in intelligent ECG signal Processing

Sarfraz, M 2014, Role of independent component Analysis in intelligent ECG signal Processing , PhD thesis, University of Salford.

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The Electrocardiogram (ECG) reflects the activities and the attributes of the human heart and reveals very important hidden information in its structure. The information is extracted by means of ECG signal analysis to gain insights that are very crucial in explaining and identifying various pathological conditions. The feature extraction process can be accomplished directly by an expert through, visual inspection of ECGs printed on paper or displayed on a screen. However, the complexity and the time taken for the ECG signals to be visually inspected and manually analysed means that it‟s a very tedious task thus yielding limited descriptions. In addition, a manual ECG analysis is always prone to errors: human oversights. Moreover ECG signal processing has become a prevalent and effective tool for research and clinical practices. A typical computer based ECG analysis system includes a signal preprocessing, beats detection and feature extraction stages, followed by classification. Automatic identification of arrhythmias from the ECG is one important biomedical application of pattern recognition. This thesis focuses on ECG signal processing using Independent Component Analysis (ICA), which has received increasing attention as a signal conditioning and feature extraction technique for biomedical application. Long term ECG monitoring is often required to reliably identify the arrhythmia. Motion induced artefacts are particularly common in ambulatory and Holter recordings, which are difficult to remove with conventional filters due to their similarity to the shape of ectopic xiii beats. Feature selection has always been an important step towards more accurate, reliable and speedy pattern recognition. Better feature spaces are also sought after in ECG pattern recognition applications. Two new algorithms are proposed, developed and validated in this thesis, one for removing non-trivial noises in ECGs using the ICA and the other deploys the ICA extracted features to improve recognition of arrhythmias. Firstly, independent component analysis has been studied and found effective in this PhD project to separate out motion induced artefacts in ECGs, the independent component corresponding to noise is then removed from the ECG according to kurtosis and correlation measurement. The second algorithm has been developed for ECG feature extraction, in which the independent component analysis has been used to obtain a set of features, or basis functions of the ECG signals generated hypothetically by different parts of the heart during the normal and arrhythmic cardiac cycle. ECGs are then classified based on the basis functions along with other time domain features. The selection of the appropriate feature set for classifier has been found important for better performance and quicker response. Artificial neural networks based pattern recognition engines are used to perform final classification to measure the performance of ICA extracted features and effectiveness of the ICA based artefacts reduction algorithm. The motion artefacts are effectively removed from the ECG signal which is shown by beat detection on noisy and cleaned ECG signals after ICA processing. Using the ICA extracted feature sets classification of ECG arrhythmia into eight classes with fewer independent components and very high classification accuracy is achieved.

Item Type: Thesis (PhD)
Themes: Health and Wellbeing
Schools: Schools > School of Computing, Science and Engineering
Depositing User: M Sarfraz
Date Deposited: 26 Mar 2015 16:31
Last Modified: 05 Apr 2016 18:18
References: 1. Acernese, F., Ciaramella, A., De Martino, S., Falanga, M., Godano, C., & Tagliaferri, R. (2004). Polarisation analysis of the independent components of low frequency events at Stromboli volcano (Eolian Islands, Italy). Journal of Volcanology and Geothermal Research, 137(1), 153–168. 2. Acharya, R. (2007). Advances in cardiac signal processing. Springer. 3. Acharya, R., Kumar, A., Bhat, P. S., Lim, C. M., Kannathal, N., & Krishnan, S. M. (2004). Classification of cardiac abnormalities using heart rate signals. Medical and Biological Engineering and Computing, 42(3), 288–293. 4. Afonso, V. X., Tompkins, W. J., Nguyen, T. Q., & Luo, S. (1999). ECG beat detection using filter banks. Biomedical Engineering, IEEE Transactions on, 46(2), 192–202. 5. Al-Fahoum, A. S., & Howitt, I. (1999). Combined wavelet transformation and radial basis neural networks for classifying life-threatening cardiac arrhythmias. Medical & Biological Engineering & Computing, 37(5), 566–573. 6. Anand, S. S., & Yusuf, S. (2011). Stemming the global tsunami of cardiovascular disease. Lancet, 377(9765), 529–32. doi:10.1016/S0140-6736(10)62346-X 7. Asl, B. M., Setarehdan, S. K., & Mohebbi, M. (2008a). Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal. Artificial Intelligence in Medicine, 44(1), 51–64. 8. Asl, B. M., Setarehdan, S. K., & Mohebbi, M. (2008b). Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal. Artificial Intelligence in Medicine, 44(1), 51–64. 9. Bache, K., & Lichman, M. (2013). UCI Machine Learning Repository. University of California, Irvine, School of Information and Computer Sciences. Retrieved from 10. Barro, S., Fernández-Delgado, M., Vila-Sobrino, J. A., Regueiro, C. V., & Sánchez, E. (1998). Classifying multichannel ECG patterns with an adaptive neural network. IEEE Engineering in Medicine and Biology Magazine : The Quarterly Magazine of the Engineering in Medicine & Biology Society, 17(1), 45–55. 11. Barros, A. K., Mansour, A., & Ohnishi, N. (1998). Removing artefacts from electrocardiographic signals using independent components analysis. Neurocomputing, 22(1), 173–186. 12. Belgacem, N., Chikh, M. A., & Reguig, F. B. (2003a). Supervised classification of ECG using neural networks. Retrieved from 13. Belgacem, N., Chikh, M. A., & Reguig, F. B. (2003b). Supervised classification of ECG using neural networks. Retrieved from 14. Bell, A. J., & Sejnowski, T. J. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7(6), 1129–1159. 15. Bingham, E., Kuusisto, J., & Lagus, K. (2002). ICA and SOM in text document analysis. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 361–362). ACM. 16. Cabras, G., Carniel, R., & Isserman, J. (2010). Signal enhancement with generalized ICA applied to Mt. Etna volcano, Italy. Bollettino Di Geofisica Teorica Ed Applicata, 51(1). 17. Cardoso, J.-F., & Souloumiac, A. (1993). Blind beamforming for non-Gaussian signals. In IEE Proceedings F (Radar and Signal Processing) (Vol. 140, pp. 362–370). IET. Retrieved from 18. Cardoso, J.-F., & Souloumiac, A. (1996). Jacobi angles for simultaneous diagonalization. SIAM Journal on Matrix Analysis and Applications, 17(1), 161–164. 19. Castells, F., Laguna, P., Sö, L., Bollmann, A., & Roig, M. (2007). Principal component analysis in ECG signal processing. EURASIP Journal on Advances in Signal Processing, 2007. 20. Castells, F., Rieta, J. J., Millet, J., & Zarzoso, V. (2005). Spatiotemporal blind source separation approach to atrial activity estimation in atrial tachyarrhythmias. Biomedical Engineering, IEEE Transactions on, 52(2), 258–267. 21. Chawla, M. P. S. (2009). A comparative analysis of principal component and independent component techniques for electrocardiograms. Neural Computing and Applications, 18(6), 539–556. 22. Cheung, Y., & Xu, L. (2001). Independent component ordering in ICA time series analysis. Neurocomputing, 41(1), 145–152. 23. Chou, K.-T., & Yu, S.-N. (2008). Categorizing Heartbeats by Independent Component Analysis and Support Vector Machines. In Intelligent Systems Design and Applications, 2008. ISDA’08. Eighth International Conference on (Vol. 1, pp. 599–602). 24. Cichocki, A., Amari, S., Siwek, K., Tanaka, T., & Phan, A. H. (2003). ICALAB for Signal Processing. Toolbox for ICA, BSS and BSE, http:/7www. Bsp. Brain. Riken. ip/[CALAB/ICALAB SignalProc. 25. Clifford, G. D., Azuaje, F., & McSharry, P. (2006). Advanced methods and tools for ECG data analysis. Artech house London. 26. Davie, A. P., Francis, C. M., Love, M. P., Caruana, L., Starkey, I. R., Shaw, T. R. D., … McMurray, J. J. V. (1996). Value of the electrocardiogram in identifying heart failure due to left ventricular systolic dysfunction. Bmj, 312(7025), 222. 27. De Chazal, P., O’Dwyer, M., & Reilly, R. B. (2004). Automatic classification of heartbeats using ECG morphology and heartbeat interval features. Biomedical Engineering, IEEE Transactions on, 51(7), 1196–1206. 28. De, O., Adams, M., Carnethon, G., Lloyd-Jones, R., Simone, T., Ferguson, K., … Hong American. (2009). D. Association Statistics Committee and Stroke Statistics Subcommittee, heart disease and stroke statistics- update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee, Circulation 119 (January (3)) ., 480–486. 29. Dokur, Z., & Ölmez, T. (2001). ECG beat classification by a novel hybrid neural network. Computer Methods and Programs in Biomedicine, 66(2), 167–181. 30. EC57, A.-A. (1998). Testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms. Association for the Advancement of Medical Instrumentation, Arlington, VA. 31. Fikret, G., & EMB. (1999). Neural network based decision making in diagnostic applications”,IEEE , , pp. ., 18(4), 89–93. 32. Goldberger, A. L., Amaral, L. A., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., … Stanley, H. E. (2000). Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals. Circulation, 101(23), e215–e220. 33. Güler, İ. (2005). ECG beat classifier designed by combined neural network model. Pattern Recognition, 38(2), 199–208. 34. Güler, İ., & Übeyli, E. D. (2005). A modified mixture of experts network structure for ECG beats classification with diverse features. Engineering Applications of Artificial Intelligence, 18(7), 845–856. 35. Guvenir, H. A., Acar, S., Demiroz, G., & Cekin, A. (1997). A supervised machine learning algorithm for arrhythmia analysis. In Computers in Cardiology 1997 (pp. 433–436). doi:10.1109/CIC.1997.647926 36. Haykin, S. (1995). Neural Networks: Foundation. MacMillan College Publishing Company,. 37. Holter, N. J. (1961). New Method for Heart Studies Continuous electrocardiography of active subjects over long periods is now practical. Science, 134(3486), 1214–1220. 38. Homaeinezhad, M. R., Atyabi, S. A., Tavakkoli, E., Toosi, H. N., Ghaffari, A., & Ebrahimpour, R. (2012). ECG arrhythmia recognition via a neuro-SVM–KNN hybrid classifier with virtual QRS image-based geometrical features. Expert Systems with Applications, 39(2), 2047–2058. 39. Hu, W., Xie, D., & Tan, T. (2004). A hierarchical self-organizing approach for learning the patterns of motion trajectories. IEEE Transactions on Neural Networks / a Publication of the IEEE Neural Networks Council, 15(1), 135–44. doi:10.1109/TNN.2003.820668 40. Huang, H. F., Hu, G. S., & Zhu, L. (2012). Sparse representation-based heartbeat classification using independent component analysis. Journal of Medical Systems, 36(3), 1235–1247. 41. Huikuri, H. V., Castellanos, A., & Myerburg, R. J. (2001). Sudden death due to cardiac arrhythmias. The New England Journal of Medicine, 345(20), 1473–82. doi:10.1056/NEJMra000650 42. Hyvarinen, A. (1999). Fast and robust fixed-point algorithms for independent component analysis. Neural Networks, IEEE Transactions on, 10(3), 626–634. 43. Hyvärinen, A. (1999). The fixed-point algorithm and maximum likelihood estimation for independent component analysis. Neural Processing Letters, 10(1), 1–5. 44. Hyvärinen, A., Karhunen, J., & Oja, E. (2001). What is Independent Component Analysis? Independent Component Analysis, 145–164. 45. Hyvärinen, A., & Oja, E. (2000a). Independent component analysis: algorithms and applications. Neural Networks, 13(4), 411–430. 46. Hyvärinen, A., & Oja, E. (2000b). Independent component analysis: algorithms and applications. Neural Networks, 13(4), 411–430. 47. Jang, J.-S. R., Sun, C.-T., & Mizutani, E. (1997a). Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [Book Review]. Automatic Control, IEEE Transactions on, 42(10), 1482–1484. 48. Jang, J.-S. R., Sun, C.-T., & Mizutani, E. (1997b). Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [Book Review]. Automatic Control, IEEE Transactions on, 42(10), 1482–1484. 49. Jiang, X., Zhang, L., Zhao, Q., & Albayrak, S. (2006a). ECG arrhythmias recognition system based on independent component analysis feature extraction. In TENCON 2006. 2006 IEEE Region 10 Conference (pp. 1–4). 50. Jiang, X., Zhang, L., Zhao, Q., & Albayrak, S. (2006b). ECG arrhythmias recognition system based on independent component analysis feature extraction (pp. 1–4). 51. Jung, T.-P., Humphries, C., Lee, T.-W., Makeig, S., McKeown, M. J., Iragui, V., & Sejnowski, T. J. (1998). Removing electroencephalographic artefacts: comparison between ICA and PCA. In Neural Networks for Signal Processing VIII, 1998. Proceedings of the 1998 IEEE Signal Processing Society Workshop (pp. 63–72). IEEE. 52. Jung, T.-P., Makeig, S., Lee, T.-W., McKeown, M. J., Brown, G., Bell, A. J., & Sejnowski, T. J. (2000). Independent component analysis of biomedical signals (pp. 633–644). 53. Jung, T.-P., Makeig, S., Westerfield, M., Townsend, J., Courchesne, E., & Sejnowski, T. J. (2000). Removal of eye activity artefacts from visual event-related potentials in normal and clinical subjects. Clinical Neurophysiology, 111(10), 1745–1758. 54. Jung, T.-P., Makeig, S., Westerfield, M., Townsend, J., Courchesne, E., & Sejnowski, T. J. (2001). Analysis and visualization of single-trial event-related potentials. Human Brain Mapping, 14(3), 166–185. 55. Karhunen, J., & Oja, E. (2001). Hyva¨rinen, A., & Independent component analysis. John Wiley Sons. 56. Keerthi, S. S., & Lin, C.-J. (2003). Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Computation, 15(7), 1667–1689. 57. Khawaja, A. (2006). Automatic ECG analysis using principal component analysis and wavelet transformation. Univ.-Verlag Karlsruhe. Retrieved from 58. Kohonen, T. (1987). Adaptive, associative, and self-organizing functions in neural computing., 26(23), 4910–8. 59. Kohonen, T. (1988). Self-organization and associative memory. Self-Organization and Associative Memory, 100 Figs. XV, 312 Pages.. Springer-Verlag Berlin Heidelberg New York. Also Springer Series in Information Sciences, Volume 8, 1. 60. Kolenda, T., Hansen, L. K., & Sigurdsson, S. (2000). Independent components in text. In Advances in Independent Component Analysis (pp. 235–256). Springer. 61. Kwak, N., & Choi, C.-H. (2003). Feature extraction based on ICA for binary classification problems. Knowledge and Data Engineering, IEEE Transactions on, 15(6), 1374–1388. 62. Kwak, N., Choi, C.-H., & Choi, J. Y. (2001). Feature extraction using ica. In Artificial Neural Networks—ICANN 2001 (pp. 568–573). Springer. Retrieved from 63. Li, Y., Powers, D., & Peach, J. (2000). Comparison of blind source separation algorithms. Advances in Neural Networks and Applications, 18–21. 64. Linh, S., Osowski, M., & IEEE. (2003). T.H. Stodolski, On-line heart beat recognition using Hermite polynomials and neuro-fuzzy network, on Instrumentation and Measurement 52 (August (4)) ., 1224–1231. 65. Lu, C.-J., Lee, T.-S., & Chiu, C.-C. (2009). Financial time series forecasting using independent component analysis and support vector regression. Decision Support Systems, 47(2), 115–125. 66. Madhuranath, H., & Haykin, S. (1998). Improved Activation Functions for Blind Separation: Details of Algebraic Derivations. CRL Internal Report No. 67. Makeig, S., Bell, A. J., Jung, T.-P., Sejnowski, T. J., & others. (1996). Independent component analysis of electroencephalographic data. Advances in Neural Information Processing Systems, 145–151. 68. Makeig, S., Jung, T.-P., Bell, A. J., Ghahremani, D., & Sejnowski, T. J. (1997). Blind separation of auditory event-related brain responses into independent components. Proceedings of the National Academy of Sciences, 94(20), 10979–10984. 69. Malmivuo, J., & Plonsey, R. (1995). Bioelectromagnetism: principles and applications of bioelectric and biomagnetic fields. Oxford University Press. 70. Martis, R. J., Acharya, U. R., & Min, L. C. (2013). ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform. Biomedical Signal Processing and Control, 8(5), 437–448. 71. Matei, B. (2000). A review of independent component analysis techniques. Signal, 6(7), 8. 72. McGarry, K., Sarfraz, M., & MacIntyre, J. (2007). Integrating gene expression data from microarrays using the self-organising map and the gene ontology. In Pattern Recognition in Bioinformatics (pp. 206–217). Springer. 73. Meyer, C. D. (2000). Matrix analysis and applied linear algebra. Siam. 74. Minami, K., Nakajima, H., & Toyoshima, T. (1999). Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network. Biomedical Engineering, IEEE Transactions on, 46(2), 179–185. 75. Mishra, P., & Singla, S. K. (2013). Artefact Removal from Biosignal using Fixed Point ICA Algorithm for Pre-processing in Biometric Recognition. Measurement Science Review, 13(1), 7–11. 76. Mohammadzadeh-Asl, B., & Setarehdan, S. K. (2006). Neural network based arrhythmia classification using heart rate variability signal. In Proceedings of the EUSIPCO. 77. Moody, G. B., Mark, R. G., & Goldberger, A. L. (2001). PhysioNet: a web-based resource for the study of physiologic signals. Engineering in Medicine and Biology Magazine, IEEE, 20(3), 70–75. 78. Moody, G. B., Muldrow, W., & Mark, R. G. (1984). A noise stress test for arrhythmia detectors. Computers in Cardiology, 11(3), 381–384. 79. Naik, G. R., & Kumar, D. K. (2011). An overview of independent component analysis and its applications. Informatica: An International Journal of Computing and Informatics, 35(1), 63–81. 80. Naik, G. R., Kumar, D. K., & Arjunan, S. P. (2010). Independent Component Analysis For Classification Of Surface Electromyography Signals During Different MVCs. In Proceedings of the 20th International EURASIP Conference-BIOSIGNAL 2010: Analysis of Biomedical Signals and Images (pp. 352–358). 81. Nazmy, T. M., El-Messiry, H., & Al-Bokhity, B. (2010). Adaptive neuro-fuzzy inference system for classification of ECG signals (pp. 1–6). Retrieved from 82. Negnevitsky, M. (2005). Artificial intelligence: a guide to intelligent systems. Pearson Education. 83. Olmez, T. (1997). Classification of ECG waveforms using RCE neural network and genetic algorithm’,Electronics Letters, , pp. ., 33(8), 1561–1562. 84. Owis, M. I., Youssef, A.-B., & Kadah, Y. M. (2002). Characterisation of electrocardiogram signals based on blind source separation. Medical and Biological Engineering and Computing, 40(5), 557–564. 85. Pan, J., & Tompkins, W. J. (1985). A real-time QRS detection algorithm. Biomedical Engineering, IEEE Transactions on, (3), 230–236. 86. Pu, Q., & Yang, G.-W. (2006). Short-text classification based on ICA and LSA. In Advances in Neural Networks-ISNN 2006 (pp. 265–270). Springer. Retrieved from

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