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Utilizing Neural Networks to Predict Freezing of Gait in Parkinson's Patients

Abstract The artificial neural network is a form of machine learning that is highly effective at recognizing patterns in large, noise-filled datasets. Possessing these attributes uniquely qualifies the neural network as a mathematical basis for adaptability in personal biomedical devices. The purpose of this study was to determine the viability of neural networks in predicting Freezing of Gait (FoG), a symptom of Parkinson's disease in which the patient's legs are suddenly rendered unable to move. More specifically, a class of neural networks known as layered recurrent networks (LRNs) was applied to an open- source FoG experimental dataset donated to the Machine Learning Repository of the University of California at Irvine. The independent v... (more)
Created Date 2016-05
Contributor Zia, Jonathan Sargon (Author) / Panchanathan, Sethuraman (Thesis Director) / McDaniel, Troy (Committee Member) / Adler, Charles (Committee Member) / Electrical Engineering Program / Barrett, The Honors College
Subject Neural Networks / Freezing of Gait / Parkinson's Disease
Series Academic Year 2015-2016
Type Text
Extent 47 pages
Language English
Reuse Permissions All Rights Reserved
Collaborating Institutions Barrett, the Honors College
Additional Formats MODS / OAI Dublin Core / RIS

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