Dianosing Heart Diseases Using ANN and GA

The heart is complex systems that reveals many clues about its condition in electrocardiogram (ECG), and is one of the most important organs in a human body.The walls of the heart contain myocardial tissues which contract to push the blood through the body. This contract occurs because of passing el...

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Main Author:	Mohammed, Ghassan Nashat
Format:	Thesis
Language:	eng eng
Published:	2009
Subjects:	QA76.76 Fuzzy System.
Online Access:	https://etd.uum.edu.my/1626/1/Ghassan_Nashat_Mohammed.pdf https://etd.uum.edu.my/1626/2/1.Ghassan_Nashat_Mohammed.pdf
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id	my-uum-etd.1626
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
topic	QA76.76 Fuzzy System.
spellingShingle	QA76.76 Fuzzy System. Mohammed, Ghassan Nashat Dianosing Heart Diseases Using ANN and GA
description	The heart is complex systems that reveals many clues about its condition in electrocardiogram (ECG), and is one of the most important organs in a human body.The walls of the heart contain myocardial tissues which contract to push the blood through the body. This contract occurs because of passing electrical current in the heart muscle the electrical current can be captured and analyzed to diagnose the heart state. This operation is done by using electrocardiograph (ECG) device; this device captures the electrical signal, filters it from noise signals, and amplifies it. Then it displays the signal on the screen or prints it on the trace paper then the doctor interprets the ECG signal to diagnose the disease.This project discusses using artificial intelligent (AI) to process and analyze the ECG signal to diagnose the heart disease directly and display detailed report about the heart state by using the artificial neural network (ANN) after training it and finding the values of the connection weights using the genetic algorithm (GA) to choose the best values to the weights.The GA is qualified in enhancing the weights of the ANN since the ANN is trained using the classical algorithm (back-propagation), the genetic algorithm is used as a co-training algorithm for enhancing the connection weights values and minimizing the error value.
format	Thesis
qualification_name	masters
qualification_level	Master's degree
author	Mohammed, Ghassan Nashat
author_facet	Mohammed, Ghassan Nashat
author_sort	Mohammed, Ghassan Nashat
title	Dianosing Heart Diseases Using ANN and GA
title_short	Dianosing Heart Diseases Using ANN and GA
title_full	Dianosing Heart Diseases Using ANN and GA
title_fullStr	Dianosing Heart Diseases Using ANN and GA
title_full_unstemmed	Dianosing Heart Diseases Using ANN and GA
title_sort	dianosing heart diseases using ann and ga
granting_institution	Universiti Utara Malaysia
granting_department	College of Arts and Sciences (CAS)
publishDate	2009
url	https://etd.uum.edu.my/1626/1/Ghassan_Nashat_Mohammed.pdf https://etd.uum.edu.my/1626/2/1.Ghassan_Nashat_Mohammed.pdf
_version_	1747827178847862784
spelling	my-uum-etd.16262013-07-24T12:12:35Z Dianosing Heart Diseases Using ANN and GA 2009 Mohammed, Ghassan Nashat College of Arts and Sciences (CAS) College of Arts and Sciences QA76.76 Fuzzy System. The heart is complex systems that reveals many clues about its condition in electrocardiogram (ECG), and is one of the most important organs in a human body.The walls of the heart contain myocardial tissues which contract to push the blood through the body. This contract occurs because of passing electrical current in the heart muscle the electrical current can be captured and analyzed to diagnose the heart state. This operation is done by using electrocardiograph (ECG) device; this device captures the electrical signal, filters it from noise signals, and amplifies it. Then it displays the signal on the screen or prints it on the trace paper then the doctor interprets the ECG signal to diagnose the disease.This project discusses using artificial intelligent (AI) to process and analyze the ECG signal to diagnose the heart disease directly and display detailed report about the heart state by using the artificial neural network (ANN) after training it and finding the values of the connection weights using the genetic algorithm (GA) to choose the best values to the weights.The GA is qualified in enhancing the weights of the ANN since the ANN is trained using the classical algorithm (back-propagation), the genetic algorithm is used as a co-training algorithm for enhancing the connection weights values and minimizing the error value. 2009 Thesis https://etd.uum.edu.my/1626/ https://etd.uum.edu.my/1626/1/Ghassan_Nashat_Mohammed.pdf application/pdf eng validuser https://etd.uum.edu.my/1626/2/1.Ghassan_Nashat_Mohammed.pdf application/pdf eng public masters masters Universiti Utara Malaysia [1] Michael W. Zimmerman, 'Classification of ECG ST Events as ischemic or nonischemic using reconstructed phase spaces' MSc thesis. Marquette University,Milwaukee, Wisconsin, May 2004. [2] Abdullah S., 'ECG pattern recognition' MSc. Sharif University of Technology, Tehran, Iran 2003. [3] Yukinori Suzuki, “Self- Organizing QRS-Wave Recognition in ECG Using Neural Networks” IEEE Transaction on Neural Networks, Vol.6, No.6, November 1995 . [4] Cuiwei Li, Chongxun Zheng and Changfeng Tai, “Detection of ECG Characteristic Points Using Wavelet Transforms”, IEEE Transaction on Biomedical Engineering, Vol. 42, No. 1, January 1998. [5] Yu Hen Hu, Surekha Palreddy and Willis J. Tompkins, “A Patient - Adaptable ECG Beat Classifier Using a mixture of Expert Approach”, IEEE Transaction on Biomedical Engineering, Vol. 44, No. 9, September 1997. [6] Stanislaw Osowski and Tran Hoai Linh, ‘ECG Beat Recognition Using Fuzzy Hybrid Neural Network’, IEEE Transaction on Biomedical Engineering, Vol. 48, No. 11, November 2001. [7] Alaa J.S., 'Design and implementation of PC-based ECG processing and analysis system', MSc. thesis Computer Department, College of Electronics and Electrical techniques, Baghdad, Iraq, 2006 . [8] Ischemia: The Thomson Corporation, 2002.63 [9] Warnick. WC, John J Ross J, Karalis DG. Echocardiography: Stress echocardiography in clinical practice. Vol.2003. Philadelphia, PA: MCP Hahnemann University, 1996. [10] Computers in cardiology challenges. Vol.2003. Cambridge, MA, 2003. [11] Rajendra Acharya U, Jasjit S., Jos A.E. Spaan and Shankar M. Krishnan,'Advances in Cardiac Signal Processing', Springer-Verlag, Berlin Heidelberg, 2007. [12] Coronary Heart Disease (Arteriosclerotic Coronary Artery Disease; Ischemic Heart Disease), 37 Ed: Stamford: Appleton & Lange, 1997. [13] D. A. Kohli, "CT Coronary Angiograms," Und J Raiol Imag, vol. 13, pp. 11,2003. [14] V. Fuster, Hurst's the heart, 10th Ed. New York: McGraw-Hill Health Professions Division, 2001. [15] M. D. Thippeswamy H. Murthy, "Echocardiogram," in Medical Encyclopedia.Washington D.C.: National Institutes of Health, 2002. [16] A. Støylen and K. Bjørnstad, "Stress echocardiography " . [17] A. S. Bensky, W. Covitz, and R. H. DuRant, "Primary care physicians' use of Screening echocardiography," Pediatrics, vol. 103, pp. e40, 1999 . [18] Zaid S. Ali 'Neural networks for ECG analysis', MSc. thesis, Electrical Engineering Department, University of Baghdad.1996 . [19] F. Jager, A. Taddei, G. Moody, M. Emdin, G. Antolic, R. Dorn, A. Smrdel, C.Marchesi, and R. Mark, "Long-term ST database: a reference for the developmentband evaluation of automated ischaemia detectors and for the study of thebdynamics of myocardial ischaemia," Medical and Biological Engineering and Computing, vol. 41, pp. 172-182, 2003. [20] http:// www.generation5.org/essys.shtml,"generation%" web master.64 [21] Emad E. 'learning Hopfield using genetic algorithm', MSc. Thesis University of Technology, 2000. [22] Suha S. Eliawi, 'Optimization of artificial neural network by using genetic algorithm', MSc. thesis, Computer Science Department, University of Technology,2002. [23] HOLLAND, J.H., “Adaptation in natural and artificial systems: na introductory analysis with applications to biology, control and artificial intelligence.” Cambridge: Cambridge press 1992 reedição 1975. [24] GOLDBERG, D.E. “Genetic Algorithms in Search, Optimization, and Machine Learning.” Reading, Mass.: Addison-Whesley, 1989. [25] KOZA, J.R. “Genetic programming: On the programming of computers by means of natural selection.” Cambridge, Mass.: MIT Press, 1992. [26] Patrick V. der 'Introduction to neural network' University of Amsterdam. 1996. [27] Alan E. 'ECG learning center' University of Utah, Salt Lake City, Utah 2006 . [28] V.S. Chouhan and S.S. Mehta’ Detection of QRS Complexes in 12-lead ECG Using Adaptive Quantized Threshold ‘ VOL.8 No.1, 2008. [29] Ilknur Can, Kudret Aytemir, Ahmet Uğur Demir, Ali Deniz, Orcun Ciftci, Lale Tokgozoglu, Ali Oto, Altay Sahin ‘P-wave duration and dispersion in patients with obstructive sleep apnea , Volume 133, Pages e85-e89 2009. [30] Rahime Ceylan, Yüksel Özbay, Bekir Karlik, ‘Expert Systems with Applications ‘, Volume 36, Pages 6721-6726 2009 . [31] Ahmad R. Naghsh-Nilchi and A. Rahim Kadkhodamohammadi,” Cardiac Arrhythmias ClassificationMethod Based on MUSIC, Morphological Descriptors,and Neural Network”.65 [32] Elif Derya Übeyli, ‘Expert Systems with Applications’, Volume 36, Pages 8758-8767 2009. [33] Jone R. Hampton, 'THE ECG MADE EASY', Elsevier Ltd, Sixth edition, 2003. [34] Frank G. Yanowitz, "ECG learning center", Spencers.ECCLES Health Sciences Library, February, 2005 http://medstat.med.utah.edu/kw/ecg. [35] World invisible, "The human heart", World invisible webmaster, November 13,2005 www.worldinvisible.com . [36] Howard D., Mark B., 'Neural network Toolbox 2 User's Guide for matlab', Math works 2006. [37] Genetic Algorithm and Direct Search Toolbox 2 User’s Guide for mat lab Math works (2006). [38] Horner, K. ‘Methodology as a Productivity Tool, in Software Productivity’,Handbook, J. Keyes (ed), New York, NY: Wind crest/McGraw-Hill, pp.97-117,1993. [39]Yourdon, E.’ A Natural Productivity in Object-Orientation, in Software Productivity Handbook, J. Keyes (ed), New York, NY: Wind crest/McGraw-Hill,pp.97-117, 1993. [40] RUMELHART, D. E., AND MCCLELLAND, J. L. Parallel Distributed Processing—Exploration in the Microstructures of Cognition, Volume I Foundations.MIT Press, Cambridge, MA, 1986. [41] ZURADA, J. M. Introduction to Artificial Neural Networks. West Publishing,St. Paul, 1992.66 [42] Govindan A, Deng G. and Power J.’ Electrogram analysis during atrial fibrillation using wavelet and neural network techniques ‘, Proc. SPIE 3169. pp. 557-62 , 1997 . [43] Amara Graps, “An Introduction to Wavelet”, IEEE Comp.Sc. And Eng., vol.no. 2, 1995 . [44] Senhadi L, Carrault G, Bellanger J.J, and Passariello G, “Comparing Wavelet Transforms for Recognizing Cardiac Patterns”, IEEE Engineering in Medicine and Biology Magazine, Publication, vol. 14, no. 2, pp. 167-173. Mar/Apr 1995. [45] Cuiwei Li, Chongxum Zheng and Changfeng Tai,“Detection of ECG characteristic points using wavelets transforms”, IEEE Transactions on Biomedical Engineering, vol. 42, no.1, pp. 21-28, 1995. [46] Walid El-Atabany,’ Nonlinear dynamical modeling of ECG signals based on phase density matrix representation ‘, 2004. [47] Sun Y, Chan K.L. and Krishnan S.M, Arrhythmia detection and recognition in ECG signals using nonlinear techniques. Ann, Biomed. Eng. 28, S-37, 2000. [48] Silipo R. and Marchesi C., Artificial Neural Networks for automatic ECG Analysis. IEEE Transactions on signal processing. 46(5): 1417-1425, 1998. [49] Sugiura, T. Hirata, H. Harada, Y. and Kazui, T., “Automatic discrimination of arrhythmia waveforms using fuzzy logic”, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society,vol. 20, no. 1, 1998. [50] Acharya U R, Subbanna Bhat P, Iyengar S S, Rao A and Dua S, “Classification of heart rate using artificial neural network and fuzzy equivalence relation”, Pattern Recognit., vol. 36, pp. 61–68, 2003.67 [51] Kannathal N, Puthusserypady S.K, Lim Choo Min, Acharya, U.R. and Laxminarayan, S. “Cardiac State Diagnosis using Adaptive Neuro-Fuzzy Technique”, Proceedings of the IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, September 1-4, 2005. [52] Roulette Wheel Selection & William Astubblefield,"Artificial Intelligent Structure and Stratigies for complex problem “, third addition, addison wasely logman, Inc.1998. [53] Patrick Henry Winston (1993). Artificial Intelligence. Addison Wesley [54] http://c2.com/cgi/wiki?GeneticAlgorithm [55] Renetha Malar Sathahasivam ,”GA Based For Real Time Dispaching In Silterra”.

Dianosing Heart Diseases Using ANN and GA

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