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dc.contributor.advisorKasabov, Nikola K.
dc.contributor.advisorProfessor George Benwell
dc.contributor.advisorDr Robert Kozma
dc.contributor.advisorGeoffrey Kennedy
dc.contributor.authorSwope, Jay Arthur
dc.date.available2012-03-25T20:44:38Z
dc.date.copyright2012
dc.identifier.citationSwope, J. A. (2012). Feature Selection and Adaptive Connectionist Classification Models and a System for Biological Time Series Analysis on the case study data of Heart Rate Variability (Thesis, Doctor of Philosophy). University of Otago. Retrieved from http://hdl.handle.net/10523/2155en
dc.identifier.urihttp://hdl.handle.net/10523/2155
dc.description.abstractStatement of Problem: Biological systems are constantly evolving and multi-dimensional. They have subsystems that are coupled to each other with nonlinear interactions that are time dependent. Data measured from biological systems over time are nonstationary with changing mean and variance. In order to characterise, analyse and extract information from time dependent biological data, a model must be capable of evolving, be capable of categorising dynamic information and provide a mechanism for extraction of ongoing knowledge. In this thesis we examine existing artificial neural network (ANN) models and their capabilities in the application to real time, biological time series data. We investigate existing features extracted from biological time series data. We develop ANN techniques further incorporating extracted features in an ongoing basis and providing real time extraction of knowledge. Explanation of method and procedures: We study the human biological system by examining the time series constructed from the time differences between heart beats. Measures derived from this time series are known as heart rate variability (HRV). We extract time, frequency and fractal domain HRV features. The data was collected as part of this study from 31 post myocardial subjects and 31 age and sex matched healthy subjects. The heart beat interval time series for each subject was constructed from ECG records of twenty to thirty minute duration. Existing models are explored for data modelling including fuzzy c-means clustering, fuzzy neural networks and fuzzy adaptive resonance networks (fuzzy-ART). A new ANN model ARTdECOS is constructed, which incorporates aspects of fuzzy-ART and evolving connectionist systems (ECOS). ARTdECOS is implemented on a portable data capture device to show its viability in handling real time data and to reveal issues requiring further development. Summary of results and conclusions: Category nodes generated by fuzzy ART reach expansion limits, and multiple nodes are generated to represent a single classification state. A category amalgamation procedure in ARTdECOS allows consolidation of these multiple nodes into a single node. As a consequence, meaningful rule extraction is made possible. A graphical representation of feature boundary limits allows a quick and convenient way to extract knowledge from classification results in ARTdECOS. State switching dynamics are evident in HRV time series data through segmenting of data from individual subjects. Real time scaling of features is necessary to implement ARTdECOS in a real time environment. This is accomplished in ARTdECOS by rescaling weight vectors when input features are rescaled. ANN models are a useful tool in understanding and extracting knowledge from biological time series data. These tools may be applied to biofeedback applications in real time, ongoing environments. Fractal features provide a representation of the complexity of biological time series data, as part of multiple feature extraction across feature domains. Future research includes constructing ANN models that incorporate results generated over short time intervals into temporally global space. The global model would also incorporate anomaly information, for instance ectopic detection in HRV applications. Additional integrative ANN modelling is needed to provide a supervisory system to incorporate the addition of expert knowledge.en_NZ
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/x-rar-compressed
dc.format.mimetypeapplication/x-rar-compressed
dc.language.isoenen_NZ
dc.publisherUniversity of Otago
dc.rightsAll items in OUR Archive are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectbiological time seriesen_NZ
dc.subjectdata miningen_NZ
dc.subjectexploratory data analysisen_NZ
dc.subjectartificial neural networksen_NZ
dc.subjectevolving connectionist systemsen_NZ
dc.subjectadaptive resonance theoryen_NZ
dc.subjectfractal analysisen_NZ
dc.subjectARTdECOSen_NZ
dc.subjectrule extractionen_NZ
dc.subjectheart rate variabilityen_NZ
dc.titleFeature Selection and Adaptive Connectionist Classification Models and a System for Biological Time Series Analysis on the case study data of Heart Rate Variabilityen_NZ
dc.typeThesis
dc.date.updated2012-03-24T03:03:53Z
dc.language.rfc3066en
thesis.degree.disciplineInformation Scienceen_NZ
thesis.degree.nameDoctor of Philosophyen_NZ
thesis.degree.grantorUniversity of Otago
thesis.degree.levelDoctoral
otago.openaccessOpen
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