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.

Original languageEnglish
Title of host publicationResearch and Development in Intelligent Systems XXVIII
Subtitle of host publicationIncorporating Applications and Innovations in Intel. Sys. XIX - AI 2011, 31st SGAI Int. Conf. on Innovative Techniques and Applications of Artificial Intel.
Pages299-310
Number of pages12
DOIs
Publication statusPublished - 1 Dec 2011
Event31st SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2011 - Cambridge, United Kingdom
Duration: 13 Dec 201115 Dec 2011

Publication series

NameRes. and Dev. in Intelligent Syst. XXVIII: Incorporating Applications and Innovations in Intel. Sys. XIX - AI 2011, 31st SGAI Int. Conf. on Innovative Techniques and Applications of Artificial Intel.

Conference

Conference31st SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2011
CountryUnited Kingdom
CityCambridge
Period13/12/1115/12/11

ID: 3960475