Abstract
Complex fractionated atrial electrograms (CFAEs) represent the electrophysiologic substrate for atrial fibrillation (AF). Individual signal complexes in CFAEs reflect electrical activity of electrophysiologic substrate at given time. To identify CFAEs sites, we developed algorithm based on wavelet transform allowing automated feature extraction from source signals. Signals were ranked by three experts into four classes. We compiled a representative data set of 113 instances with extracted features as inputs and average of expert ranking as the output. In this paper, we present results of our GAME data mining algorithm, that was used to (a) predict average ranking of experts, (b) classify into three classes. The performance of the GAME algorithm was compared to well known data mining techniques using robust ten times tenfold cross validation. Results indicate that wavelet signal decomposition could carry high level of predictive information about the state of electrophysiologic substrate and that the GAME algorithm outperforms other data mining techniques (such as decision trees, linear regression, neural networks, Support Vector Machines, etc.) in both prediction and classification accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Weka open source data mining software (September 2006), http://www.cs.waikato.ac.nz/ml/weka/
The fake game environment for the automatic knowledge extraction (March 2008), http://www.sourceforge.net/projects/fakegame
Brown, G.: Diversity in Neural Network Ensembles. PhD thesis, The University of Birmingham, School of Computer Science, Birmingham B15 2TT, United Kingdom (January 2004)
Calkins, H., Brugada, J., Packer, D., et al.: Hrs/ehra/ecas expert consensus statement on catheter and surgical ablation of atrial fibrillation: recommendations for personnel, policy, procedures and follow-up. Heart Rhythm 4, 816–861 (2007)
Daubechies, I.: Ten lectures on Wavelts, Philadelphia, Pennsylvania, USA (1992)
Donoho, D.: De-noising by soft-thresholding. IEEE Trans. on Inf. Theory 41(3), 613–662 (1995)
Drchal, J., Kordík, P., Šnorek, M.: Dataset Visualization Based on a Simulation of Intermolecular Forces. In: IWIM 2007 - International Workshop on Inductive Modelling, Praha, vol. 1, pp. 246–253. Czech Technical University in Prague (2007)
Fuster, V., Rydn, L., Cannom, D.S., et al.: Acc/aha/esc 2006 guidelines for the management of patients with atrial fibrillation: a report of the american college of cardiology/american heart association task force on practice guidelines and the european society of cardiology committee for practice guidelines (writing committee to revise the 2001 guidelines for the management of patients with atrial fibrillation): developed in collaboration with the european heart rhythm association and the heart rhythm society. Circulation 114(7), 257–354 (2006)
Hansen, L., Salamon, P.: Neural network ensembles. IEEE Trans. Pattern Anal. Machine Intelligence 12(10), 993–1001 (1990)
Allessie, M.A., Houben, R.P.M.: Processing of intracardiac electrograms in atrial fibrillation. diagnosis of electropathological substrate of af. IEEE Eng. Med. Biol. Mag. 25, 40–51 (2006)
Ivakhnenko, A.G.: Polynomial theory of complex systems. IEEE Transactions on Systems, Man, and Cybernetics SMC-1(1), 364–378 (1971)
Kordík, P.: Game - group of adaptive models evolution. Technical Report DCSE-DTP-2005-07, Czech Technical University in Prague, FEE, CTU Prague, Czech Republic (2005)
Kordík, P.: Fully Automated Knowledge Extraction using Group of Adaptive Models Evolution. PhD thesis, Czech Technical University in Prague, FEE, Dep. of Comp. Sci. and Computers, FEE, CTU Prague, Czech Republic (September 2006)
Kord’ık, P., Kovářík, O., Šnorek, M.: Optimization of Models: Looking For The Best Strategy. In: Proceedings of the 6th EUROSIM Congress on Modelling and Simulation, Vienna, vol. 2, pp. 314–320. ARGESIM (2007)
Kremen, V., Lhotska, L.: Evaluation of novel algorithm for search of signal complexes to describe complex fractionated atrial electrogram. In: Proceedings of Biosignal, Biosignals (2008)
Mandischer, M.: A comparison of evolution strategies and backpropagation for neural network training. Neurocomputing (42), 87–117 (2002)
Nademanee, K., McKenzie, J., Kosar, E., et al.: A new approach for catheter ablation of atrial fibrillation: mapping of the electrophysiologic substrate. J. Am. Coll. Cardiol. 43, 2044–2053 (2004)
Ng, J., Kadish, A., Goldberger, J.: Effect of electrogram characteristics on the relationship of dominant frequency to atrial activation rate in atrial fibrillation. Heart Rhythm 3, 1295–1305 (2006)
Sanders, P., Berenfeld, O., Hocini, M., et al.: Spectral analysis identifies sites of high-frequency activity maintaining atrial fibrillation in humans. Circulation 112, 789–797 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kordík, P., Křemen, V., Lhotská, L. (2008). The GAME Algorithm Applied to Complex Fractionated Atrial Electrograms Data Set. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87559-8_89
Download citation
DOI: https://doi.org/10.1007/978-3-540-87559-8_89
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87558-1
Online ISBN: 978-3-540-87559-8
eBook Packages: Computer ScienceComputer Science (R0)