Using An Artificial Neural Network to Detect Activations During Ventricular Fibrillation

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dc.contributor.advisor Susan M. Blanchard, Chair en_US
dc.contributor.advisor Mark White, Member en_US
dc.contributor.advisor H. Troy Nagle, Member en_US
dc.contributor.advisor W. F. McClure, Member en_US
dc.contributor.author Young, Melanie Talanda en_US
dc.date.accessioned 2010-04-02T17:59:10Z
dc.date.available 2010-04-02T17:59:10Z
dc.date.issued 1998-07-02 en_US
dc.identifier.other etd-19980701-142321 en_US
dc.identifier.uri http://www.lib.ncsu.edu/resolver/1840.16/889
dc.description.abstract Ventricular Fibrillation (VF) is a severe cardiac arrhythmia that canresult in sudden death, a leading cause of death in the United States. DuringVF, the electrical activity of the heart becomes disordered, the ventriclescontract erratically, and an insufficient supply of blood is pumped to the body.Identification of the electrical activation sites during VF is important for theunderstanding and improved treatment of the disorder. Unipolar electrogramsof four pigs were recorded following the induction of VF. The data from theVF recordings was preprocessed using a Rule-Based Method (RBM) , a Current Source Density (CSD) method, and a Transmembrane CurrentMethod (TCM) to separate local activations from distant activity. RBM usesthe magnitude of the derivative of the voltage to identify activations. CSD is ascalar quantity that represents the magnitude of the current source or sink.TCM estimates a value proportional to the transmembrane current. Afeedforward artificial neural network (ANN) using backpropagation was trained to identify the local activations in the electrograms of VF based on the RBM and CSD calculations. Another feedforward ANN usingbackpropagation was trained using data preprocessed with not only RBM andCSD, but also TCM. In order to improve the ability of the ANNs to detect localactivations, a new training method, called staged training, was utilized. Instaged training, the ANNs were trained in stages using different sets oftraining examples. Examples were included in a particular training set basedon the minimum magnitude of the voltage derivative. When training was done in stages, both the ANN with RBM and CSD data only and the ANN with RBM,CSD, and TCM data were able to more accurately distinguish activations.Overall, the ANN, which used only RBM and CSD data, produced better results than the ANN that also included TCM data. en_US
dc.rights I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. en_US
dc.title Using An Artificial Neural Network to Detect Activations During Ventricular Fibrillation en_US
dc.degree.name MS en_US
dc.degree.level Master's Thesis en_US
dc.degree.discipline Biological and Agricultural Engineering en_US


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