Abstract
Support Vector Machines (SVM) have been applied extensively to classification and regression problems, but there are few solutions proposed for problems involving time-series. To evaluate their potential, a problem of difficult solution in the field of biological signal modeling has been chosen, namely the characterization of the cerebral blood flow autoregulation system, by means of dynamic models of the pressure-flow relationship. The results show a superiority of the SVMs, with 5% better correlation than the neural network models and 18% better than linear systems. In addition, SVMs produce an index for measuring the quality of the autoregulation system which is more stable than indices obtained with other methods. This has a clear clinical advantage.
This works was supported by FONDECYT, Chile, under project 1050082.
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Raicharoen, T., Lursinsap, C., Sanguanbhokai, P.: Application of critical support vector machine to time series prediction. In: Circuits and Systems. Proceedings of ISCAS 2003, vol. 5, pp. 741–744 (2003)
Wan-Zhao, C., Chang-Chun, Z., Wen-Xing, B., Jun-Hua, L.: Chaotic time series prediction using mean-field theory for support vector machine. Chinese Phys. 14, 922–929 (2005)
Jankowski, S., Oreziak, A.: Learning system for computer-aided ECG analysis based on support vector machines. International Journal of Bioelectromagnetism 5, 175–176 (2003)
Acir, N., Guzelis, C.: Automatic spike detection in EEG by a two-stage procedure based on support vector machines. Comput. Biol. Med. 7, 561–575 (2004)
Newell, D., Aaslid, R., Lam, A., Mayberg, T., Winn, R.: Comparison of flow and velocity during autoregulation testing in humans. Stroke 25, 793–797 (1994)
Panerai, R.: Assessment of cerebral pressure autoregulation in humans - a review of measurement methods. Physiological Measurement 19, 305–338 (1998)
Panerai, R., Evans, D., Mahony, P., Deverson, S., Hayes, P.: Assessment of thigh cuff technique for measurement of dynamic cerebral autoregulation. Stroke 31, 476–480 (2000)
Panerai, R.B., Dawson, S.L., Eames, P.J., Potter, J.F.: Cerebral blood flow velocity response to induced and spontaneous sudden changes in arterial blood pressure. Am J Physiol. 280, H2162–H2174 (2001)
Tiecks, F., Lam, A., Aalid, R., Newell, D.: Comparison of static and dynamic cerebral Autoregulation measurements. Stroke 26, 1014–1019 (1995)
Panerai, R., Dawson, S., Potter, J.: Linear and nonlinear analysis of human dynamic cerebral autoregulation. Am J Physiol. 227, H1089–H1099 (1999)
Panerai, R., Chacón, M., Pereira, R., Evans, D.: Neural network modeling of dynamic Cerebral Autoregulation: assessment and comparison with established methods. Med. Eng & Phys. 26, 43–52 (2004)
Mitsis, G., Zhang, G., Levine, B.D., Marmarelis, V.: Modeling of Nonlinear Physiological Systems with fast and Slow Dynamics. II. Application to cerebral Autoregulation. Ann. Biomedical Engineering 30, 555–565 (2002)
Schölkopf, B., Smola, A., Williamson, R.C., Bartlett, P.: New support vector algorithms. Neural Computation 12, 1207–1245 (2000)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Nerrand, O., Roussel-Ragot, P., Personnaz, L., Dreyfus, G.: Neural networks and non-linear adaptive filtering: unifying concepts and new algorithms. Neural Comput. 5, 165–199 (1993)
Frohlich, H., Zell, A.: Efficient parameter selection for support vector machines in classification and regression via model-based global optimization. In: Neural Networks. IJCNN 2005. Proceedings. 2005 IEEE International Joint Conference, vol. 3, pp. 1431–1436 (2005)
Panerai, R.B., Eames, P.J., Potter, J.F.: Variability of time-domain indices of dynamic cerebral Autoregulation. Physiol. Meas. 24, 367–381 (2003)
Chacón, M., Blanco, C., Panerai, R., Evans, D.: Nonlinear Modeling of Dynamic Cerebral Autoregulation Using Recurrent Neural Networks. In: Sanfeliu, A., Cortés, M.L. (eds.) CIARP 2005. LNCS, vol. 3773, pp. 205–213. Springer, Heidelberg (2005)
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Chacón, M., Diaz, D., Ríos, L., Evans, D., Panerai, R. (2006). Support Vector Machine with External Recurrences for Modeling Dynamic Cerebral Autoregulation. In: Martínez-Trinidad, J.F., Carrasco Ochoa, J.A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2006. Lecture Notes in Computer Science, vol 4225. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11892755_99
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DOI: https://doi.org/10.1007/11892755_99
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