Abstract
This paper presents a new way to solve the inverse problem of electrocardiography in terms of heart model parameters. The developed event estimation and recognition method uses a unified neural network (UNN)-based optimization system to determine the most relevant heart model parameters. A UNN-based preliminary ECG analyzer system has been created to reduce the searching space of the optimization algorithm. The optimal model parameters were determined by a relation between objective function minimization and robustness of the solution. The final evaluation results, validated by physicians, were about 96% correct. Starting from the fact that input ECGs contained various malfunction cases, such as Wolff-Parkinson-White (WPW) syndrome, atrial and ventricular fibrillation, these results suggest this approach provides a robust inverse solution, circumventing most of the difficulties of the ECG inverse problem.
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References
Thaker, N.V., Ferrero, J.M.: Electrophysiologic models of heart cells and cell networks. IEEE Eng. Med. Biol. Soc. Magazine 17(5), 73–83 (1998)
MacLeod, R.S., Brooks, D.H.: Recent progress in inverse problems in electrocardiology. IEEE Eng. Med. Biol. Soc. Magazine 17(1), 73–83 (1998)
Szilágyi, S.M.: Event Recognition, Separation and Classification from ECG Recordings. In: Proc 20th Ann. Int. Conf. IEEE EMBS, pp. 236–239. IEEE Computer Society Press, Los Alamitos (1998)
Cuppen, J.J.M., van Oosterom, A.: Model studies with inversely calculated isochrones of ventricular depolarization. IEEE Trans. Biomed. Eng. 31, 652–659 (1984)
Guanglin, L., Bin, H.: Localization of the site of origin of cardiac activation by means of a heart-model-based electrocardiographic imaging approach. IEEE Trans. Biomed. Eng. 48, 660–669 (2001)
Shahidi, A.V., Savard, P., Nadeau, R.: Forward and inverse problems of electrocardiography: modeling and recovery of epicardial potentials in humans. IEEE Trans. Biomed. Eng. 41, 249–256 (1994)
Mirvis, D.M.: Validation of body surface electrocardiographic mapping. In: Mirvis, D.M. (ed.) Body surface electrocardiographic mapping, pp. 63–74. Kluwer, Dordrecht (1988)
Minami, K., Nakajima, H., Yoyoshima, T.: Real time discrimination of the ventricular tachyarrhythmia with Fourier-transform neural network. IEEE Trans. Biomed. Eng. 46, 179–185 (1999)
Lagerholm, M., et al.: Clustering ECG complexes using Hermite functions and self-organizing maps. IEEE Trans. Biomed. Eng. 47, 838–847 (2000)
Osowski, S., Hoai, L.T.: ECG beat recognition using fuzzy hybrid neural network. IEEE Trans. Biomed. Eng. 48, 1265–1271 (2001)
Osowski, S., Hoai, L.T., Markiewicz, T.: Support vector machine-based expert system for reliable heartbeat recognition. IEEE Trans. Biomed. Eng. 51, 582–589 (2004)
Vapnik, V.: Statistical learning theory. Wiley, New York (1998)
Smola, A., Scholkopf, B.: A tutorial on support vector regression. Royal Holloway College, Univ. London, NeuroColt Tech. Rep., NV2-TR-1998-030 (1998)
Szilágyi, S.M., Benyó, Z., Dávid, L.: Heart model based ECG signal processing. In: Proc. 5th IFAC Symp. Modell Contr. Biomed. Syst., pp. 213–217 (2003)
Szilágyi, S.M., Benyó, Z., Dávid, L.: WPW syndrome identification and classification using ECG analysis. In: Proc. World Congr. Med. Phys. Biomed. Eng. (2003)
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Szilágyi, S.M., Szilágyi, L., Benyó, Z. (2007). Spatial Heart Simulation and Analysis Using Unified Neural Network. In: Melin, P., Castillo, O., Ramírez, E.G., Kacprzyk, J., Pedrycz, W. (eds) Analysis and Design of Intelligent Systems using Soft Computing Techniques. Advances in Soft Computing, vol 41. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72432-2_35
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DOI: https://doi.org/10.1007/978-3-540-72432-2_35
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