Abstract
Cooperation in natural processes is very important feature, which is modeled by many nature-inspired algorithms. Nature inspired metaheuristics have interesting stochastic properties which make them suitable for use in data mining, data clustering and other computationally demanding application areas. It is because they often produce robust solutions in fairly reasonable time. This paper presents an application of clustering method inspired by the behavior of real ants in the nature in biomedical signal processing. The ants cooperatively maintain and evolve a pheromone matrix which is used to select features. The main aim of this study was to design and develop a combination of feature extraction and classification methods for automatic recognition of significant structure in biological signal recordings. The method is targeted towards speeding up and increasing objectivity of identification of important classes and may be used for online classification. Inherent properties of the method make it suitable for analysis of newly incoming data. The method can be also used in the expert classification process. We have obtained significant results in electrocardiogram and electroencephalogram recordings, which justify the use of such method.
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References
Abraham, A., Grosan, C., Ramos, V.: Swarm Intelligence in Data Mining (Studies in Computational Intelligence). Springer (2006)
Bursa, M., Huptych, M., Lhotska, L.: The use of nature inspired methods in electrocardiogram analysis. International Special Topics Conference on Information Technology in Biomedicine [CD-ROM]. Piscataway: IEEE (2006)
Bursa, M., Lhotska, L.: Modified ant colony clustering method in long-term electrocardiogram processing. Proceedings of the 29th Annual International Conference of the IEEE EMBS pp. 3249–3252 (2007)
Bursa, M., Lhotska, L., Macas, M.: Hybridized swarm metaheuristics for evolutionary random forest generation. Proceedings of the 7th International Conference on Hybrid Intelligent Systems 2007 (IEEE CSP) pp. 150–155 (2007)
Chow, T., Kereiakes, D.J., Bartone, C., Booth, T., Schloss, E.J., Waller, T., Chung, E., Menon, S., Nallamothu, B.K., Chan, P.S.: Microvolt t-wave alternans identifies patients with ischemic cardiomyopathy who benefit from implantable cardioverter-defibrillator therapy. J Am Coll Cardiol 49(1), 50–58 (2007). DOI 10.1016/j.jacc.2006.06.079. http://content.onlinejacc.org/cgi/content/abstract/49/1/50
Chudacek, V., Lhotska, L.: Unsupervised creation of heart beats classes from long-term ecg monitoring. Conference: Analysis of Biomedical Signals and Images. 18th International EURASIP Conference Biosignals 2006. Proceedings. 18, 199–201 (2006)
Deneubourg, J.L., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., Chretien, L.: The dynamics of collective sorting robot-like ants and ant-like robots. In: Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats, pp. 356–363. MIT Press, Cambridge, MA, USA (1990)
Dorigo, M., Blum, C.: Ant colony optimization theory: A survey. Theoretical Computer Science Issues 2–3 344, 243–278 (2005)
Dorigo, M., Caro, G.D., Gambardella, L.M.: Ant algorithms for discrete optimization. Artif. Life 5(2), 137–172 (1999). DOI http://dx.doi.org/10.1162/106454699568728
Dorigo, M., Stutzle, T.: Ant Colony Optimization. MIT Press, Cambridge, MA (2004)
Dunn, J.C.: Well separated clusters and optimal fuzzy partitions. Journal of Cybernetics 4, 95–104 (1974)
Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000). Circulation Electronic Pages: http://circ.ahajournals.org/cgi/content/full/101/23/e215
Handl, J., Knowles, J., Dorigo, M.: Ant-based clustering and topographic mapping. Artificial Life 12(1) 12, 35–61 (2006)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. Proceedings IEEE International Conference on Neural Networks IV, 1942–1948 (1995)
Mahalanobis, P.: On the generalised distance in statistics. Proceedings of the National Institute of Science of India 12, 49–55 (1936)
Myers, C.S., Rabiner, L.R.: A comparative study of several dynamic time-warping algorithms for connected word recognition. The Bell System Technical Journal 607, 1389–1409 (1981)
Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998). URL http://www.ics.uci.edu/~mlearn/MLRepository.html
R. O. Schoonderwoerd, e.a.: Ant-based load balancing in telecommunications networks. Adaptive Behavior 5 pp. 169–207 (1996)
Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comp App. Math 20, 53–65 (1987)
Scher, M.S.: Automated EEG-sleep analyses and neonatal neurointensive care (2004)
Stutzle, T., Hoos, H.: Max-min ant system. Future Gen. Comput. Syst. 16 8, 889–914 (2000)
Teofilo, L., Lee-Chiong: SLEEP: a comprehensive handbook. Johm Wiley & Sons, Inc., Hoboken, New Jersey (2006)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd Edition. Morgan Kaufmann, San Francisco (2005)
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Bursa, M., Lhotska, L. (2008). Ant Colony Cooperative Strategy in Electrocardiogram and Electroencephalogram Data Clustering. In: Krasnogor, N., Nicosia, G., Pavone, M., Pelta, D. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2007). Studies in Computational Intelligence, vol 129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78987-1_29
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DOI: https://doi.org/10.1007/978-3-540-78987-1_29
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