Abstract
The neural networks (NNs) are regularly employed in biosignal processing because of their effectiveness as pattern classifiers. This study presents an overview of the application of neural networks in the field of biosignal classification (especially in anomaly detection problems), and, in addition, results of adaptations of conventional neural classifiers are presented. Statistical techniques based on pattern recognition analysis (like Principal Components Analysis and Clustering) might be use to evaluate the proposed methodology. Finally we will illustrate advantages and drawbacks of neural systems in biosignal analysis and catch a glimpse of forthcoming developments in machine learning models for the real clinical environment.
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References
Hecht-Nielse, R.: Applications of counter propagation networks. Neural Networks 1, 131–139 (1988)
MacQueen, J.B.: Some Methods for classification and Analysis of Multivariate Observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press, Berkeley (1967)
Jardins, T.D.: Cardioopulmonary Anatomy Physiology, 4th edn. (2002)
Bronzino, D.J.: The Biomedical Engineering Handbook, 2nd edn. IEEE Press, Los Alamitos (2000)
Pryor, T.A., et al.: Computer Systems for the processing of diagnostic electrocardiograms. IEEE Computer Society Press, Los Alamitos (1980)
Haykin, S.: Neural Network: A comprehensive foundation. Macmillan College Publishing, Basingstoke (1994)
Dillon, R.M., Manikopoulos, C.N.: Neural Nets non-linear prediction for speech data. IEE Electronic Letters 27(10), 824–826 (1991)
Carpenter, G.A., et al.: Fuzzy ARTMAP: An adaptive resonance architecture for incremental learning of analog maps. In: International Joint Conference on Neural Network (1992)
Fauseatt, L.: Fundamentals of Neural Networks. Prentice Hall, New Jersey (1994)
Brotherton, T., Johnson, T.: Anomaly detection for advance military aircraft using neural networks. In: Proceedings of the 2001 IEEE Aerospace Conference, Big Sky Montana (2001)
Basseville, M., Nikiforov, I.V.: Detection of Abrupt Changes: Theory and Applications. Prentice-Hall, Inc., Simon & Schuster Company, Englewood Cliff (1993)
Gustafsson, F.: Adaptive Filtering and Change Detection. John Wiley & Sons Inc., Chichester (2000)
Markou, M., Singh, S.: Novelty detection: A review - part 1: Statistical approaches. Signal Processing 83(12), 2481–2497 (2003)
Markou, M., Singh, S.: Novelty detection: A review - part 2: Neural network based approaches. Signal Processing 83(12), 2499–2521 (2003)
Takeuchi, J., Yamanishi, K.: A unifying framework for detecting outliers and change points from time series. IEEE Trans. on Knowledge and Data Engineering 18(4), 482–489 (2006)
Yamanishi, K., Takeuchi, J.: A unifying framework for detecting outliers and change points from non-stationary time series data. In: Proc. of the 8the ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 676–681 (2002)
Ide, T., Kashima, H.: Eigenspace-based anomaly detection in computer systems. In: Proc. of the 10th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 440–449 (2004)
San-Jun, H., Sung-Bae, C.: Evolutionary Neural Networks for anomaly detection based on the behavior of a program. IEEE Trans. on Systems and Cybernetics, Part B 36(3), 559–568 (2006)
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Zimeras, S., Kastania, A. (2008). The Role of Neural Networks in Biosignals Classification. In: Tsihrintzis, G.A., Virvou, M., Howlett, R.J., Jain, L.C. (eds) New Directions in Intelligent Interactive Multimedia. Studies in Computational Intelligence, vol 142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68127-4_52
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DOI: https://doi.org/10.1007/978-3-540-68127-4_52
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