Classification of Paroxysmal Atrial Fibrillation using Second Order System
DOI:
https://doi.org/10.11113/jt.v67.2765Keywords:
Atrial fibrillation, normal sinus rhythm, hypertension, stroke, electrocardiogram, second order systemAbstract
In this paper, we monitored and analyzed the characteristics of atrial fibrillation in patient using second order approach. Atrial fibrillation is a type of atria arrhythmias, disturbing the normal heart rhythm between the atria and lower ventricles of the heart. Heart disease and hypertension increase risk of stroke from atrial fibrillation. This study used electrocardiogram (ECG) signals from Physiobank, namely MIT-BIH Atrial Fibrillation Dataset and MIT-BIH Normal Sinus Rhythm Dataset. In total, 865 episodes for each type of ECG signal were classified, specifically normal sinus rhythm (NSR) of human without arrhythmia, normal sinus rhythm of atrial fibrillation patient (N) and atrial fibrillation (AF). Extracted parameters (forcing input, natural frequency and damping coefficient) from second order system were characterized and analyzed. Their ratios, time derivatives, and differential derivatives were also observed. Altogether, 12 parameters were extracted and analysed from the approach. The results show significant difference between the three ECGs of forcing input, and derivative of forcing input. Overall system performance gives specificity and sensitivity of 84.9 % and 85.5 %, respectively.
References
J. B. Shea, and S. F. Sears. 2008. A Patient’s Guide to Living with Atrial Fibrillation. Circulation. 117: E340–E343.
S. Nattel. 2002. New Ideas About Atrial Fibrillation 50 Years on. Nature J. 415: 219–226.
The Star, 29 Oct 2012, Stroke - No. 3 killer in Malaysia. via electronic access, http://thestar.com.my/lifestyle/story.asp?file=/2012/10/29/lifefocus/12052614&sec=lifefocus."
L. R. Caplan. 2006. Stroke. 1st ed. American Academy of Neurology: Demos New York. 4–5.
K. Yodogawa, Y. Seino, T. Ohara, M. Hayashi, Y. Miyauchi, T. Katoh, K. Mizuno. Prediction of Atrial ï¬brillation After Ischemic Stroke Using P-Wave Signal Averaged Electrocardiography, J. Cardiology. In Press.
A. Martinez, R. Alcaraz and J. J. Rieta. 2012. Study on the P-wave Feature Time Course as Early Predictors of Paroxysmal Atrial ï¬brillation. Physiological Measurement. 33: 1959–1974.
I. Christov, G. Bortolan, and I. Daskalov. 2001. Sequential Analysis for Automatic Detection of Atrial Fibrillation and Flutter. Computers in Cardiology. Rotterdam, The Netherlands. 28: 293–296.
S. Dash, K. H. Chon, S. Lu, and E. A. Raeder. 2009. Automatic Real Time Detection of Atrial Fibrillation. Annals of Biomedical Engineering. 37(9): 1701–1709.
F. Lombardi, D. Tarricone, F. Tundo, F. Colombo, S. Belletti and C. Fiorentini. 2004. Autonomic Nervous System and Paroxysmal Atrial Fibrillation: A Study Based on the Analysis of RR Interval Changes Before, During and After Paroxysmal Atrial Fibrillation. European Heart Journal. 25(14): 1242–1248.
K Tateno, and L. Glass. 2000. A Method for Detection of Atrial Fibrillation using RR Intervals. Computers in Cardiology. Cambridge, Massachusetts, USA. 27: 391–394.
F. D. Murgatroyd, B. Xie, X. Copie, I. Blankoff, A. J. Camm, and M. Malik. 1995. Identification of Atrial Fibrillation Episodes in Ambulatory Electrocardiographic Recordings: Validation of a Method for Obtaining Labeled R-R Interval Files. Pacing and Clinical Electrophysiology. 18(6): 1315–1320.
G. B. Moody, and R. G. Mark. 1983. A New Method for Detecting Atrial Fibrillation using R-R Intervals. Computers in Cardiology. 10: 227–230.
M. Stridh, and M. Rosenqvist. 2012. Automatic Screening of Atrial Fibrillation in Thumb-ECG Recordings. Computing in Cardiology. 39: 193–196.
P. De Chazal and C. Heneghan. 2001. Automated Assessment of Atrial Fibrillation. Computers in Cardiology. 28: 117–120.
S. Kara, and M. Okandan. 2007. Atrial Fibrillation Classification with Artificial Neural Networks. Pattern Recognition. Elsevier. 40(11): 2967–2973.
D. Filos, I. Chouvarda, G. Dakos, L. Mantziari,V. Vassilikos and N. Maglaveras. 2012. Wavelet Variability of SA Node Originated P Waves in Atrial Fibrillation and in Signals with Ectopic Beats, 34th Annual International Conference of the IEEE EMBS, San Diego, California USA. 6369–6372.
N. Maglaveras, I. Chouvarda, G. Dakos, V. Vasilikos, S. Mochlas, and G. Louridas. 2002. Analysis of Atrial Fibrillation after CABG using Wavelets. Computers in Cardiology. Memphis, USA. 29: 89–92.
S. Petrutiu, A. V. Sahakian, and S. Swiryn. 2007. Abrupt Changes in Fibrillatory Wave Characteristics at the Termination of Paroxysmal Atrial Fibrillation in Humans. The European J Pacing. 9(7): 466–470.
C. Mora, J. Castells, R. Ruiz, and J. J. Rieta. 2004. Prediction of Spontaneous Termination of Atrial Fibrillation Using Time Frequency Analysis of the Atrial Fibrillatory Wave. Computers in Cardiology. 31: 109–112.
D. Al-Dabass and M. Ren. 2007. Semantic Mining Dynamics for Games Language Processing, Asia Modelling Symposium 2007. IEEE Computer Society. Phuket. 313–318.
D. Al-Dabass, R. Cant, and C. Langensiepen. 2006. Semantics Mining for Opponent Strategy Estimation, CGAMES 2006, Louisville, Kentucky.
M. A. Othman, N. Mat Safri, I. Abdul Ghani, and F. K. Che Harun. 2012. Characterization of Ventricular Tachycardia and Fibrillation Using Semantic Mining. Computer and Information Science. 5(5): 35–44.
Mohd Afzan Othman, Norlaili Mat Safri, Ismawati Abdul Ghani, Fauzan Khairi Che Harun, and Ismail Ariffin. 2013. A New Semantic Mining Approach for Detecting Ventricular Tachycardia and Ventricular Fibrillation. Biomedical Signal Processing and Control. 8: 222–227.
Mohd Afzan Othman and Norlaili Mat Safri. 2012. Characterization of Ventricular Arrhythmias Using a Semantic Mining Algorithm. Journal of Mechanics in Medicine and Biology. 12(3): 1250049-(1–11).
N. A. Abdul-Kadir, N. Mat Safri, and M. A. Othman. 2013. Feasibility Study of Semantic Mining for Predicting the Onset of Atrial Fibrillation. In: 4th International Graduate Conference on Engineering Science & Humanity 2013, 4th IGCESH2013. 184–189.
A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. Ch. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C-K. Peng, Stanley H. E. 2000. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation. 101(23): e215–e220.
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