A practical reconstructed phase space approach for ECG arrhythmias classification

Journal of Medical Engineering and Technology

Volumn 37, Issue 7, 2013, Pages 401-408

  Al Fahoum, Amjed S ^a   Qasaimeh, Awni M ^a  

^a YARMOUK UNIVERSITY   (Jordan)

Author keywords

Arrhythmias classification; Atrial fibrillation; ECG; Reconstructed phase space (RPS)

Indexed keywords

ATRIAL FIBRILLATION; CLASSIFICATION ALGORITHM; CLASSIFICATION PARAMETERS; RECONSTRUCTED PHASE SPACE; SIGNAL PROCESSING TECHNIQUE; VENTRICULAR ARRHYTHMIAS; VENTRICULAR FIBRILLATION; VENTRICULAR TACHYCARDIA;

ALGORITHMS; DATA PROCESSING; DISEASES; ELECTROCARDIOGRAPHY; PHASE SPACE METHODS; SIGNAL PROCESSING; STATISTICAL TESTS; TIME DOMAIN ANALYSIS;

CLASSIFICATION (OF INFORMATION);

ARTICLE; DIAGNOSTIC ACCURACY; DISEASE CLASSIFICATION; ELECTROCARDIOGRAM; HEART ATRIUM FIBRILLATION; HEART VENTRICLE FIBRILLATION; HEART VENTRICLE TACHYCARDIA; METHODOLOGY; P WAVE; PRIORITY JOURNAL; Q WAVE; SENSITIVITY AND SPECIFICITY; T WAVE;

ALGORITHMS; ARRHYTHMIAS, CARDIAC; DATABASES, FACTUAL; DIAGNOSIS, COMPUTER-ASSISTED; ELECTROCARDIOGRAPHY; HUMANS; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 84884558856     PISSN: 03091902     EISSN: 1464522X     Source Type: Journal    
DOI: 10.3109/03091902.2013.819946     Document Type: Article

References (31)

1. Grauer, K., and Curry, R.W., 1992, Clinical electrocardiography: A primary care approach. (Boston, MA: Blackwell Scientific Publication).
2. Khadra, L., Al-Fahoum, A., and Binajjaj, S., 2005, A Quantitative analysis approach for cardiac arrhythmia classification using higher order spectral techniques. IEEE Transactions on Biomedical Engineering, 45, 1840-1845.
3. Acharya, R.U., Kannathal, N., and Krishnan, S.M., 2004, Comprehensive analysis of cardiac health using heart rate signals. Physiological Measurement, 25, 1139-1351.
4. Addison, P.S., Watson, J.N., Clegg, G.R., Holzer, M., Sterz, F., and Robertson, C.E., 2000, Evaluating arrhythmias in ECG signals using wavelet transforms. IEEE Engineering in Medicine and Biology, 19, 104-109.
5. Sun Y., Chan, K.L., and Krishnan, S.M., 2005, Life-threatening ventricular arrhythmia recognition by nonlinear descriptor. BioMedical Engineering OnLine, 4
6. Afonso, V.X., and Tompkins, W.J., 1995, Detecting Ventricular Fibrillation. IEEE Engineering in Medicine and Biology, 14, 152-159.
7. Roberts, F.M., Povinelli, R.J., and Ropella, K.M., 2005, Identification of ECG arrhythmias using phase space reconstruction. Proceedings of principles and practice of knowledge discovery in database (PKDD'01), Freiburg, Germany, pp. 411-423.
8. Povinelli, R.J., Roberts, F.M., Johnson, M.T., and Ropella, K.M., 2002, Are nonlinear ventricular arrhythmia characteristics lost: As signal duration decreases? Computers in Cardiology, Memphis, Tenessee, 29, 221-224.
9. Povinelli, R.J., Johnson, M.T., Lindgren, A.C., Roberts, F.M., and Ye, J., 2006, Statistical models of reconstructed phase space for signal classification. IEEE Transactions on Signal Processing, 54, 2178-2186.
10. Roberts, F.M., Povinelli, R.J., and Ropella, K.M., 2003, Rhythm classification using reconstructed phase space of signal frequency sub-bands. Computers in Cardiology, 30, 61-64.
11. Povinelli, R.J., Johnson, M.T., Lindgren, A.C., and Ye, J., 2004, Time series classification using Gaussian mixture models of reconstructed phase spaces. IEEE Transactions on Knowledge and Data Engineering, 16, 779-783.
12. Al-Fahoum, A., and Qasaimeh, A., 2006, ECG arrhythmia classification using simple reconstructed phase space approach. Computers in Cardiology, 33, 757-760.
13. Mneimneh, M.A., and Povinelli, R.J., 2007, RPS/GMM approach toward the localization of myocardial infarction. Computers in Cardiology, 34, 185-188.
14. Sun, X., Zhao, Y., and Xue, X., 2011, Analyzing spatial characters of the ECG signal via complex network method. 4th International Conference on Biomedical Engineering and Informatics (BMEI), 3, 1650-1653.
15. Amann, A., Tratnig, R., and Unterkofler, K., 2007, Detecting ventricular fibrillation by time delay methods. IEEE Transactions in Biomedical Engineering, 54, 174-177.
16. Anuradha, B., and Reddy, V., 2008, Cardiac arrhythmia classification using fuzzy classifiers. Journal of Theoretical and Applied Information Technology, 4, 353-359.
17. Ceylan, R., Uzbay, Y., and Karlik, B., 2009, A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network. Expert Systems with Applications, 36, 6721-6726.
18. Roopaei, M., Boostania, R., Rohani, R.S., Taghavib, M.A., and Azimifara, Z., 2010, Chaotic based reconstructed phase space features for detecting ventricular fibrillation. Biomedical Signal Processing and Control, 5, 318-327.
19. Nejadgholi, I., Moradi, M., and Abdolali, F., 2011, Using phase space reconstruction for patient independent heartbeat classification in comparison with some benchmark methods. Computers in Biology and Medicine, 41, 411-419.
20. Parvaneh, S., Golpayegani, M.R.H., Firoozabadi, M., and Haghjoo, M., 2011, Predicting the spontaneous termination of atrial fibrillation based on Poincare section in the electrocardiogram phase space. Proceedings of the IMechE Part H: Journal of Engineering in Medicine, 226, 3-20.
21. Volosin, K., Stadler, R.W., Wyszynski, R., and Kirchhof, P., 2013, Tachycardia detection performance of implantable loop recorders: Results from a large 'real life' patient cohort and patients with induced ventricular arrhythmias. Europace, 15, doi:10.1093/europace/eut036.
22. Luz, E.J., Nunes, T.M., Albuquerque, V.H., Papa, J.P., and Menotti, D., 2013, ECG arrhythmia classification based on optimum-path forest. Expert Systems with Applications, 40, 3561-3573.
23. The research resource for complex physiologic signals [Internet], PhysioNet. [Cited 2013 Apr 1]. Available from: www.physionet.org.
24. Kantz, H., and Schrreiber, T., 1997, Non Linear time series analysis. (Cambridge: Cambridge University Press).
25. Cao, L., 1997, Practical method for determining the minimum embedding dimension of a scalar time series. Physica D, 110, 43-50.
26. Frye, S.J., 1988, Cardiac Rhythm Disorders: An introduction using the nursing process. (Baltimore, MD: Williams & Wilkins).
27. Acharya, R.U., Kumar, A., Bhat, P.S., Lim, C.M., Iyengar, S.S., Kannathal, N., and Krishnan, S.M., 2004, Classification of cardiac abnormalities using heart rate signals. Medical & Biological Engineering & Computing, 42, 288-293.
28. Zimmerman, M.W., Povinelli, R.J., Johnson, M.T., and Ropella, K.M., 2003, A reconstructed phase space approach for distinguishing ischemic from non-ischemic ST changes using Holter ECG Data. Computers in Cardiology, 30, 243-246.
29. Coast, D.A., Steven, R.M., Cano, G., and Briller, S.A., 1990, An approach to cardiac arrhythmia analysis using hidden Markov Models. IEEE Transaction on Biomedical Engineering, 37, 826-836.
30. Lohman, J., and Lohman, T., 2006, Long Term Cardiac Monitor. US Patent 2006; Patent No.: US 7, 117, 031 B2.
31. Zahng, Y., McCabe, A., Yonce, D., and Thompson, J., 2007, Methods and Apparatus for Arryhthmia detection and classification using wireless ECG. US Patent 2007; Patent No.: US 7, 212, 849 B2.