Artificial neural network based ECG arrhythmia classification

Journal of Mechanics in Medicine and Biology

Volumn 9, Issue 4, 2009, Pages 507-525

  Haseena, H ^a   Joseph, Paul K ^b   Mathew, Abraham T ^b  

^a MES COLLEGE OF ENGINEERING   (India)

^b NATIONAL INSTITUTE OF TECHNOLOGY CALICUT   (India)

Author keywords

Auto regressive model; Electrocardiogram; Neural networks; Spectral entropy

Indexed keywords

EID: 76449119987     PISSN: 02195194     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0219519409003103     Document Type: Article

References (33)

1. Tatara E, Cinar A, Interpreting ECG data by integration statistical and artificial intelligence tools, IEEE Eng Biomed Biol 2(1):36-41, 2002.
2. Caswell SA, Kluge KS, Chiang CMJ, Jenkins JM, Pattern recognition of cardiac arrhythmias using two intracardiac channels, Proc Comput Cardiol 181-184, 1993.
3. Thakor NV, Natarajan A, Tomaselli G, Multi way sequential hypothesis testing for tachyarrhythmia discrimination, IEEE Trans Biomed Eng 41(5):480-487, 1994.
4. Afonoso VX, Tompkins WJ, Detecting ventricular fibrillation: Selecting the appropriate time-frequency analysis tool for the application, IEEE Eng Med Bio Mag 14(2):152-159, 1995.
5. Finelli CJ, The time sequenced adaptive filter for analysis of cardiac arrhythmias in intraventricular electrocardiograms, IEEE Trans Biomed Eng 43(8):811-819, 1996.
6. Senhadji L, Carrault G, Bellanger JJ, Passariello G, Comparing wavelet transforms for recognizing cardiac patterns, IEEE Eng Med Biol 14(2):167-173, Mar./Apr. 1995.
7. Khadra L, Al-Fahoum AS, Al-Nashash H, Detection of life threatening cardiac arrhythmias using the wavelet transformation, Med Biol Eng Comput 35(6):626-632, Nov. 1997.
8. Tian L, Tompinks WJ, Time domain based algorithm for detection of ventricular fibrillation, Proc EMBS 19th Annual Int Conf, Chicago, 3, pp. 374-377, 1997.
9. Zhang XS, Zhu YS, Thakor NV, Wang ZZ, Detecting ventricular tachycardia and fibrillation by complexity measure, IEEE Trans Biomed Eng 46(5):548-555, May 1999.
10. Chen SW, A two stage discrimination of cardiac arrhythmia using a total least squarebased prony modelling algorithm, IEEE Trans Biomed Eng 47:1317-1327, Oct. 2000.
11. Owis MI, Abou-Zied A, Youssef AB, Kadah YM, Study of features based on nonlinear dynamic modelling in ECG arrhythmia detection and classification, IEEE Trans Biomed Eng 49(7):733-736, July 2002.
12. Ham FM, Han S, Classification of cardiac arrhythmias using fuzzy ARTMAP, IEEE Trans Biomed Eng 43:425-430, 1996.
13. Dingfei Ge, Narayanan S, Shankar MK, Cardiac arrhythmia classification using autoregressive modelling, Biomed Eng Online, 2002.
14. Ding-Fei GE, Bei-Ping HOU, Xin-Jian X, Study of feature based on auto regressive modelling in ECG Automatic Diagnosis, ACTA Automatica Sinica 33(5):462-466, May 2007.
15. Christinin DJ, Bennett FM, Lutchen KR, Ahmed HM, Hausdorff JM, Oriol N, Application of linear and nonlinear time series modelling to heart rate dynamics analysis, IEEE Trans Biomed Eng 42(4):411-415, Apr. 1995.
16. Hu YH, Palreddy S, Tomkins W, A patient adaptable ECG beat classifier using a mixture of experts approach, IEEE Trans Biomed Eng 44:891-900, 1997.
17. Minami K, Nakajima H, Toyoshima T, Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network, IEEE Trans Biomed Eng 46(2):179-185, Feb. 1999.
18. Yu S-N, Chen Y-H, Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network, Pattern Recogn Lett 28(10):1142-1150, July 2007.
19. Maglaveras N, Stamkapoulos T, Diamntaras K, Pappas C, Strintzis M, ECG pattern recognition and classification using non-linear transformations and neural networks: A review, Int J Med Inform 52:191-208, 1998.
20. Rajendra AU, Suri JS, Jos AES, Krishnan SM, Advances in Cardiac Signal Processing, Springer Berlin Heidelberg, pp. 210-211, 2007.
21. http://www.physionet.org/
22. Heger JW, Niemann JT, Roth RF, Criley JM (eds.), Cardiology, 4th ed., Williams- Wilkins, Baltimore, 1998.
23. Gari DC, Francisco A, Patrick EM, Advanced Methods and Tools for ECG Data Analysis, Artech House, London, p. 62, 2006.
24. Sinha NK, Kuszta B, Modelling and Identification of Dynamic Systems, VanNostrand Reinhold Company, New York, p. 263.
25. Anuradha VC, Veera RB, ANN for classification of cardiac arrhythmias, ARPN J Eng Appl Sci 3(3):1-6, June 2008.
26. Simon H, Neural Networks: A Comprehensive Foundation, Macmillan College Publishing Company, New York, 1994.
27. Mathworks Inc., Neural Networks Toolbox User's Guide, version.3 (Release 11), 1998.
28. Elif DU, Probabilistic neural networks employing Lyapunov exponents for analysis of Doppler ultrasound signals, Compt Biol Med 38:82-89, 2008.
29. Kanmani S, Rhymend UV, Sankaranarayanan V, Thambidurai P, Object-oriented software fault prediction using neural networks, Inform Software Tech 49:483-492, 2007.
30. Tsai C-Y, An iterative feature reduction algorithm for probabilistic neural networks, Omega 28:513-524, 2000.
31. Yu S-N, Chou K-T, Integration of independent component analysis and neural networks for ECG beat classification, Expert Syst Appl 34:2841-2846, 2008.
32. Ge D, Srinivasan N, Krishnan SM, Cardiac arrhythmia classification using autoregressive modelling, Biomed Eng Online, 2002.
33. Prasad GK, Sahambi JS, Classification of ECG arrhythmias using multi-resolution analysis and neural networks, Proc IEEE Conf on Convergent Technologies (Tecon2003), Bangalore, India, 1, pp. 227-231, 2003.