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 ENG  Vol.5 No.10 B , October 2013
Identification of Atrial Fibrillation Using Complex Network Similarity
Abstract: We investigate the use of complex network similarity for the identification of atrial fibrillation. The similarity of the network is estimated via the joint recurrence plot and Hamming distance. Firstly, we transform multi-electrodes epicardium signals recorded from dogs into the recurrence complex network. Then, we extract features representing its similarity. Finally, epicardium signals are classified utilizing the classification and regression tree with extracted features. The method is validated using 1000 samples including 500 atrial fibrillation cases and 500 normal sinus ones. The sensitivity, specificity and accuracy of the identification are 98.2%, 98.8% and 98.5% respectively. This experiment indicates that our approach may lay a foundation for the prediction of the onset of atrial fibrillation.
Cite this paper: Zhang, Y. and Wang, Y. (2013) Identification of Atrial Fibrillation Using Complex Network Similarity. Engineering, 5, 22-26. doi: 10.4236/eng.2013.510B005.
References

[1]   A. L. Waldo, “Mechanism of Atrial Fibrillation,” Journal of Cardiovascular Electrophysiology, Vol. 14, No. 12, 2003, pp. s267-s274. http://dx.doi.org/10.1046/j.1540-8167.2003.90401.x

[2]   S. Nattel, A. Shiroshita-Takeshita, B. Brundel and L. Rivard, “Mechanisms of Atrial Fibrillation: Lessons from Animal Models,” Progress in Cardiovascular Diseases, Vol. 48, No. 1, 2005, pp. 9-28. http://dx.doi.org/10.1016/j.pcad.2005.06.002

[3]   J. Zhang and M. Small, “Complex Network from Pseudoperiodic Time Series: Topology versus Dynamics,” Physical Review Letters, Vol. 96, No. 23, 2006, Article ID: 238701. http://dx.doi.org/10.1103/PhysRevLett.96.238701

[4]   L. Lacasa, B. Luque, F. Ballesteros, J. Luque and J. C. Nuno, “From Time Series to Complex Networks: The Visibility Graph,” PNAS, Vol. 105, No. 13, 2008, pp. 4972- 4975. http://dx.doi.org/10.1073/pnas.0709247105

[5]   Y. Yang and H. Yang, “Complex Network-Based Time Series Analysis,” Physica A, Vol. 387, 2008, pp. 1381- 1386. http://dx.doi.org/10.1016/j.physa.2007.10.055

[6]   Z. Gao and N. Jin, “Complex Network from Time Series Based on Phase Space Reconstruction,” Chaos, Vol. 19, No. 3, 2009, Article ID: 033137. http://dx.doi.org/10.1063/1.3227736

[7]   N. Marwan , J. F. Donges, Y. Zou, R. V. Donner and J. Kurthsa, “Complex Network Approach for Recurrence Analysis of Time Series,” Physics Letters A, Vol. 373, No. 9, 2009, pp. 4246-4254. http://dx.doi.org/10.1016/j.physleta.2009.09.042

[8]   L. Uldry, V. Jacquemet, N. Virag, L. Kappenberger and J. M. Vesin. “Estimating the Time Scale and Anatomical Location of Atrial Fibrillation Spontaneous Termination in a Biophysical Model,” Medical and Biological Engineering and Computing, Vol. 50, 2012, pp. 155-163. http://dx.doi.org/10.1007/s11517-011-0859-3

[9]   R. Sun, Y. Wang, S. Yang and Z. Fang. “Detecting Cardiac Arrhythmias Based on Phase Space Analysis,” Journal of Biomedical Engineering, Vol. 25, No. 4, 2008, pp. 934-937.

[10]   Y. Hirata and K. Aihara, “Identifying Hidden Common Causes from Bivariate Time Series: A Method Using Recurrence Plots,” Physical Review E, Vol. 81, 2010, Article ID: 016203. http://dx.doi.org/10.1103/PhysRevE.81.016203

[11]   N. Marwan, M. C. Romano, M. Thiel and J. Kurths, “Recurrence Plots for the Analysis of Complex Systems,” Physics Reports, Vol. 438, 2007, pp. 237-329. http://dx.doi.org/10.1016/j.physrep.2006.11.001

[12]   H. Yanga, S. Bukkapatnamb, T. Leb and R. Komanduric, “Identification of Myocardial Infarction (MI) Using Spatio-Temporal Heart Dynamics,” Medical Engineering & Physics, Vol. 34, 2012, pp. 485-497. http://dx.doi.org/10.1016/j.medengphy.2011.08.009

 
 
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