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dc.degree.departmentComputer Scienceen_US
dc.rights.availabilityUnrestricted.
dc.creatorChristiansen, Brian Thomas
dc.date.accessioned2016-11-14T23:11:58Z
dc.date.available2011-02-18T19:05:57Z
dc.date.available2016-11-14T23:11:58Z
dc.date.issued1995-08
dc.identifier.urihttp://hdl.handle.net/2346/9398en_US
dc.description.abstractThe comparison of three neural network methods for the prediction of a time series is studied. The digitization of electrocardiograph recordings gathered from a group of patients by the Massachusetts Institute of Technology Division of Health Sciences and Technology serve as the base for the time series to be predicted. The feed-forward back propagation learning algorithm, radial basis functions with orthogonal least squares learning algorithm and recurrent networks with Pearlmutter's learning algorithm are used as the three neural networks for prediction. The three methods prove successful in single point prediction and give fairly good results for as much as 5-point prediction, but beyond that the results are poor. The five points predicted represent less than one-quarter of a second of electrocardiograph recording time; thus showing all three methods unsuccessful as long term predictors.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherTexas Tech Universityen_US
dc.subjectNeural networks (Computer science)en_US
dc.subjectTime-series analysisen_US
dc.subjectElectrocardiography -- Data processingen_US
dc.titleECG time series prediction with neural networks
dc.typeThesis


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