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AR-based Method for ECG Classification and Patient Recognition
Branislav Vuksanovic, Mustafa Alhamdi
Pages - 74 - 92     |    Revised - 15-08-2013     |    Published - 15-09-2013
Volume - 7   Issue - 2    |    Publication Date - September 2013  Table of Contents
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KEYWORDS
Electrocardiogram Classification, Individual Patient Recognition, AR Model, MIT/BIH Database.
ABSTRACT
The electrocardiogram (ECG) is the recording of heart activity obtained by measuring the signals from electrical contacts placed on the skin of the patient. By analyzing ECG, it is possible to detect the rate and consistency of heartbeats and identify possible irregularities in heart operation. This paper describes a set of techniques employed to pre-process the ECG signals and extract a set of features – autoregressive (AR) signal parameters used to characterise ECG signal. Extracted parameters are in this work used to accomplish two tasks. Firstly, AR features belonging to each ECG signal are classified in groups corresponding to three different heart conditions – normal, arrhythmia and ventricular arrhythmia. Obtained classification results indicate accurate, zero-error classification of patients according to their heart condition using the proposed method. Sets of extracted AR coefficients are then extended by adding an additional parameter – power of AR modelling error and a suitability of developed technique for individual patient identification is investigated. Individual feature sets for each group of detected QRS sections are classified in p clusters where p represents the number of patients in each group. Developed system has been tested using ECG signals available in MIT/BIH and Politecnico of Milano VCG/ECG database. Achieved recognition rates indicate that patient identification using ECG signals could be considered as a possible approach in some applications using the system developed in this work. Pre-processing stages, applied parameter extraction techniques and some intermediate and final classification results are described and presented in this paper.
CITED BY (2)  
1 Sahu, O., Anand, V., Kanhangad, V., & Pachori, R. B. (2015). Classification of magnetic resonance brain images using bi-dimensional empirical mode decomposition and autoregressive model. Biomedical Engineering Letters, 5(4), 311-320.
2 Amien, M. B., & Japir, T. E. K. A Reliable Arrhythmias-Recognition Scheme Via Wavelet and Multiclass Support Vector Machine.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
1 B. Surawicz. Electrophysiologic Basis of E.C.G. and Cardiac Arrhythmias, Lippincott Williams and Wilkins illustrated edition, 1995.
2 J. Adamec and R. Adamec. ECG Holter: Guide to Electrocardiographic Interpretation,Springer, 2008.
3 B. Kohler, C. Hennig and R. Orglmeister. "The principles of software QRS detection", IEEE Eng Med Biol Mag, vol 21, issue 1, pp. 42-57, 2002.
4 J. Pan and W. Tompkins. "A Real Time QRS Detection Algorithm", in Biomedical Engineering IEEE Transactions on, vol. 32, issue 3, pp. 230-236, 1985.
5 I. Romero and L. Serrano. "ECG frequency domain features extraction: a new characteristic for arrhythmias classification", in Engineering in Medicine and Biology Society, 2001.Proceedings of the 23rd Annual International Conference of the IEEE. vol. 2, pp. 2006-2008 2001.
6 A. R.M.S.S. and B. Boashash. "Time-frequency domain features of ECG signals: their application in P wave detection using the cross Wigner-Ville distribution. in Acoustics,Speech, and Signal Processing", Acoustics, Speech, and Signal Processing. ICASSP-89.,1989 International Conference on, vol. 3, pp. 1524 - 1527, 1989.
7 L. Senhadji. "Comparing wavelet transforms for recognizing cardiac patterns", Engineering in Medicine and Biology Magazine,IEEE, vol. 14, issue 2, pp. 167-173, 1995.
8 C. Zheng and C. Tai. "Detection of ECG characteristic points using wavelet transforms",IEEE Trans Biomed Eng, vol. 42, issue 1, pp. 21-8, 1995.
9 Y. Hu, S. Palreddy and ,W. Tompkins. "A patient-adaptable ECG beat classifier using a mixture of experts approach", IEEE Trans Biomed Eng, vol. 44, issue 9, pp. 891-900, 1997.
10 M. Lagerholmet. "Clustering ECG complexes using Hermite functions and self-organizing maps", Biomedical Engineering, IEEE Transactions on, vol. 47, issue 7, pp. 838-848, 2000.
11 D. Cuesta-Frau. "Feature extraction methods applied to the clustering of electrocardiographic signals. A comparative study. in Pattern Recognition", in Proceedings.16th International Conference on. vol.3, pp. 961-964, 2002.
12 M. a. Nygårds and L. Sörnmo. "Delineation of the QRS complex using the envelope of the e.c.g", Medical and Biological Engineering and Computing, vol. 21, issue 5, pp. 538-547,1983.
13 P. e. a. Laguna. " Adaptive estimation of QRS complex wave features of ECG signal by the hermite model", Medical and Biological Engineering and Computing, vol. 34, issue 1, pp. 58-68, 1996.
14 G. Dayong. "Bayesian ANN classifier for ECG arrhythmia diagnostic system: a comparison study. in Neural Networks", Proceedings. IEEE International Joint Conference on. vol.4, pp.2383-2388. 2005.
15 J. Kors and J. van Bemmel. "Methods Inf Med, Classification methods for computerized interpretation of the electrocardiogram", Methods of information in Medicine on, vol. 29, issue 4, pp. 330-6, 1990.
16 D. Coast. "An approach to cardiac arrhythmia analysis using hidden Markov models.Biomedical Engineering", IEEE Transactions on, vol. 37, issue 9, pp. 826-836, 1990.
17 T. Yeap, F. Johnson and M. Rachniowski. "ECG Beat Classification By A Neural Network", in Engineering in Medicine and Biology Society 1990, Proceedings of the Twelfth Annual International Conference of the IEEE, pp. 1457-1458, 1990.
18 S. Osowski and L. Tran Hoai. "ECG beat recognition using fuzzy hybrid neural network",Biomedical Engineering, IEEE Transactions on, vol. 48, issue 11, pp. 1265-1271, 2001.
19 W. Yang. "A short-time multifractal approach for arrhythmia detection based on fuzzy neural network", Biomedical Engineering, IEEE Transactions on, vol. 48, issue 9, pp. 989-995, 2001.
20 R. Silipo and C. Marchesi. "Artificial neural networks for automatic ECG analysis", Signal Processing, IEEE Transactions on, vol. 46, issue 5, pp. 1417-1425, 1998.
21 P. Domingos and M. Pazzani. "Machine Learning, On the Optimality of the Simple Bayesian Classifier under Zero-One Loss", Machine learning, vol. 29, issue 2, pp. 103-130, 1997.
22 M. Tjoa. "Artificial neural networks for the classification of cardiac patient states using ECG and blood pressure data" in Intelligent Information Systems Conference, The Seventh Australian and New Zealand 2001, pp.323-327, 2001.
23 G. Dingfei, S. Narayanan and M. K. Shankar. "Cardiac arrhythmia classification using autoregressive modeling", BioMedical Engineering OnLine, 2002. [Online]. USENET:http://www.biomedical-engineering-online.com/content/1/1/5., Nov. 13, 2002 [Aug. 28, 2013].
24 J. Huang, Z. Jianping and X. Yiqing. " Source classification using pole method of AR model",IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP-97.,1997, vol. 1, pp. 567 - 570, 1997.
25 A. Ouelli, B. ElhadadiL, H. Aissaoui and B. Bouikhalene. "AR Modeling for Automatic Cardiac Arrhythmia Diagnosis using QDF Based Algorithm", International Journal of Advanced Research in Computer Science and Software Engineering, vol. 2, Issue 5, 2012.
26 D. Gari, A. Francisco and M. Patrick. Biomedical Engineering. :Advanced Methods And Tools for ECG Data Analysis, Artech House, Inc., 2006.
27 M. Tarvainen, P. Ranta-aho and P. Karjalai. "An advanced detrending method with application to HRV analysis", Biomedical Engineering, IEEE Transactions on, vol. 49, issue2, pp. 172-175, 2002.
28 J. F. Moraes. "A QRS complex detection algorithm using electrocardiogram leads",Computers in Cardiology, pp. 205 - 208, 2002.
29 P. Q. Liu. "A robust method for QRS detection based on modified p-spectrum. Acoustics,Speech and Signal Processing", IEEE International Conference on 2008, pp. 501 – 504,2008.
30 V. Afonso. "ECG beat detection using filter banks", Biomedical Engineering, IEEE Transactions on, vol. 46, issue 2, pp. 192-202, 1999.
31 S. Marple. Digital spectral analysis: with applications. ????????-???? ?????? ?????????? ??????, Prentice-Hall., 1987.
32 M. Hayes. Statistical digital signal processing and modeling, John Wiley & Sons., 1996.
33 M. De Hoon. "Why Yule-Walker should not be used for autoregressive modelling", Annals of Nuclear Energy, vol. 23, issue 15, pp. 1219-1228, 1996.
34 K. Burnham and D. Anderson. Model selection and multimodel inference: a practical information-theoretic approach, Springer., 2002.
35 J. Shadbolt and J. Taylor. Perspectives in neural computing : Neural networks and the financial markets: predicting, combining, and portfolio optimisation, Springer., 2002.
36 A. Un-Chi Yeh and W.-J. Wang. "Heartbeat Case Determination Using Fuzzy Logic Method on ECG Signals", International Journal of Fuzzy Systems, vol. 11, issue 4, 2009.
37 M. Tavassoli, M. Ebadzadeh and H. Malek. "Classification of cardiac arrhythmia with respect to ECG and HRV signal by genetic programming", Canadian Journal on Artificial Intelligence,Machine Learning and Pattern Recognition, vol. 3, 2012.
38 Leski and A. Momot. "Bayesian and empirical Bayesian approach to weighted averaging of ECG signal", Builletin of the polish academy of sciences, vol. 55, Issue 4, 2007.
39 D. P. S.Karpagachelvi. "ECG Feature Extraction Techniques - A Survey Approach", (IJCSIS) International Journal of Computer Science and Information Security, vol. 8, issue 1, 2010.
40 F. Van der Heijden. Classification, Parameter Estimation and State Estimation - An Engineering Approach Using MATLAB, John Wiley & Sons., 2004.
41 G. McLachlan. Discriminant Analysis and Statistical Pattern Recognition (Wiley Series in Probability and Statistics), Wiley-Interscience., 2004.
42 D. Milano. "Politecnico Biosignals Archives on CD-ROM", (Copyright (C) Politecnico. 1992).,1992.
43 G. Moody and R. Mark. "The MIT-BIH Arrhythmia Database on CD-ROM and software for use with it", in Computers in Cardiology 1990, Proceedings., 1990.
Dr. Branislav Vuksanovic
University of Portsmouth - United Kingdom
branislav.vuksanovic@port.ac.uk
Mr. Mustafa Alhamdi
University of Portsmouth - United Kingdom