Abstract
Urodynamics is a clinical test in which time series data is recorded measuring internal pressure readings as the bladder is filled and emptied. Two sets of descriptive statistics based on various pressure events from urodynamics tests have been derived from time series data. The suitability of these statistics for use as inputs for event classification through neural networks is investigated by means of the gamma test. BFGS neural network models are constructed and their classification accuracy measured. Through a comparison of the results, it is shown that the gamma test can be used to predict the reliability of models before the neural network training phase begins.
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References
Abrams, P., Urodynamics. 2006: Springer Verlag.
Evans, D. and A.J. Jones, A proof of the Gamma test. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 2002. 458(2027): p. 2759.
Broyden, C., et al., BFGS method. Journal of the Institute of Mathematics and Its Applications, 1970. 6: p. 76-90.
Zurada, J.M., Introduction to artificial neural systems. 1992.
Stefánsson, A., N. Končar, and A.J. Jones, A note on the Gamma test. Neural Computing & Applications, 1997. 5(3): p. 131-133.
Kemp, S., I. Wilson, and J. Ware, A tutorial on the gamma test. International Journal of Simulation: Systems, Science and Technology, 2004. 6(1-2): p. 67–75.
Evans, D., Data-derived estimates of noise for known smooth models using near-neighbour asymptotics, in Department of Computer Science. 2002, Cardiff University Cardiff.
Dreiseitl, S. and L. Ohno-Machado, Logistic regression and artificial neural network classification models: a methodology review. Journal of Biomedical Informatics, 2002. 35(5-6): p. 352-359.
Baxt, W. G., Application of artificial neural networks to clinical medicine. The lancet, 1995. 346(8983): p. 1135-1138.
Chai, S. S., et al., Backpropagation neural network for soil moisture retrieval using NAFE’05 data: a comparison of different training algorithms. Int Archives Photogramm, Remote Sens Spatial Inf Sci (China), 2008. 37: p. 1345.
Xia, J.H. and A. Kumta, Feedforward Neural Network Trained by BFGS Algorithm for Modeling Plasma Etching of Silicon Carbide. Plasma Science, IEEE Transactions on, 2010. 38(2): p. 142-148.
Chester, D.L. Why two hidden layers are better than one. 1990.
Cybenko, G., Continuous valued neural networks with two hidden layers are sufficient. Mathematics of Control, Signal and Systems, 1989. 2: p. 303-314.
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Hogan, S., Jarvis, P., Wilson, I. (2011). Using the Gamma Test in the Analysis of Classification Models for Time-Series Events in Urodynamics Investigations. In: Bramer, M., Petridis, M., Nolle, L. (eds) Research and Development in Intelligent Systems XXVIII. SGAI 2011. Springer, London. https://doi.org/10.1007/978-1-4471-2318-7_23
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DOI: https://doi.org/10.1007/978-1-4471-2318-7_23
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