Matching Items (9)
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- All Subjects: Parkinson's disease
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
Description
At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson’s disease classification and severity assessment. An automated, stable, and accurate method to evaluate Parkinson’s would be significant in streamlining diagnoses of patients and providing families more time for corrective measures. We propose a methodology which incorporates TDA into analyzing Parkinson’s disease postural shifts data through the representation of persistence images. Studying the topology of a system has proven to be invariant to small changes in data and has been shown to perform well in discrimination tasks. The contributions of the paper are twofold. We propose a method to 1) classify healthy patients from those afflicted by disease and 2) diagnose the severity of disease. We explore the use of the proposed method in an application involving a Parkinson’s disease dataset comprised of healthy-elderly, healthy-young and Parkinson’s disease patients.
ContributorsRahman, Farhan Nadir (Co-author) / Nawar, Afra (Co-author) / Turaga, Pavan (Thesis director) / Krishnamurthi, Narayanan (Committee member) / Electrical Engineering Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
"The Legal Adventures of Frankie and Rosie" is a creative project that explores the nontraditional format of comics to express creative nonfiction. The project is a set of 30 independent comics that focuses on two primary college-going students who are based off of the authors. The characters, Frankie and Rosie narrate their stories through dialogue. The authors use this narrative model to archive their college experience at ASU. Representing creative nonfiction through comics yields an amalgamated format that can be challenging for both the writers to produce as well as for the readers to consume. Ultimately, the project serves as an attempt to test whether or not the comic medium can stand by itself as an appropriate format to express creative nonfictional narratives without becoming a diluted combination of its purer predecessors.
ContributorsBennewitz, Dana (Co-author) / Ray, Payel (Co-author) / Martin, Thomas (Thesis director) / Scott Lynch, Jacquelyn (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / Barrett, The Honors College (Contributor) / School of Geographical Sciences and Urban Planning (Contributor) / Electrical Engineering Program (Contributor) / School of Social Transformation (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2015-05
Description
This documentary shows how what we eat affects our planet. Meat and dairy consumption is the number one pollutant to the environment and yet it is often not discussed among environmentalists. There is so much devastation taking place on our planet due the animal agriculture industry: air pollution, and water contamination, destruction of the the Amazon rainforests. Natural resources, such as water - it takes one thousand gallons of water to produce one gallon of milk - are being over consumed. Land is being cleared of trees at a massive scale in the Amazon to make more room for land to raise livestock and grow its feed. Following the stories and experiences of several ASU students and other community members, the documentary highlights this connection between food and its effects on the environment and what people can do to make a difference.
ContributorsKoka, Vaishnavi (Author) / Barca, Lisa (Thesis director) / Meloy, Elizabeth (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
This is a lectures series on photovoltaics. As the need for electrical energy rises, mankind has struggled to meet its need in a reliable lasting way. Throughout this struggle, solar energy has come to the foreground as a complete solution. However, it has many drawbacks and needs a lot of development. In addition, the general public is unaware of how solar energy works, how it is made, and how it stands economically. This series of lectures answering those three questions.
ContributorsLeBeau, Edward Sanroma (Author) / Goryll, Michael (Thesis director) / Bowden, Stuart (Committee member) / Dauksher, Bill (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
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 variables in this experiment \u2014 the subject being tested, neural network architecture, and sampling of the majority classes \u2014 were each varied and compared against the performance of the neural network in predicting future FoG events. It was determined that single-layered recurrent networks are a viable method of predicting FoG events given the volume of the training data available, though results varied significantly between different patients. For the three patients tested, shank acceleration data was used to train networks with peak precision/recall values of 41.88%/47.12%, 89.05%/29.60%, and 57.19%/27.39% respectively. These values were obtained for networks optimized using detection theory rather than optimized for desired values of precision and recall. Furthermore, due to the nature of the experiments performed in this study, these values are representative of the lower-bound performance of layered recurrent networks trained to detect gait freezing. As such, these values may be improved through a variety of measures.
ContributorsZia, Jonathan Sargon (Author) / Panchanathan, Sethuraman (Thesis director) / McDaniel, Troy (Committee member) / Adler, Charles (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The honors thesis presented in this document describes an extension to an electrical engineering capstone project whose scope is to develop the receiver electronics for an RF interrogator. The RF interrogator functions by detecting the change in resonant frequency of (i.e, frequency of maximum backscatter from) a target resulting from an environmental input. The general idea of this honors project was to design three frequency selective surfaces that would act as surrogate backscattering or reflecting targets that each contains a distinct frequency response. Using 3-D electromagnetic simulation software, three surrogate targets exhibiting bandpass frequency responses at distinct frequencies were designed and presented in this thesis.
ContributorsSisk, Ryan Derek (Author) / Aberle, James (Thesis director) / Chakraborty, Partha (Committee member) / Electrical Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
This paper serves to report the research performed towards detecting PD and the effects of medication through the use of machine learning and finger tapping data collected through mobile devices. The primary objective for this research is to prototype a PD classification model and a medication classification model that predict the following: the individual’s disease status and the medication intake time relative to performing the finger-tapping activity, respectively.
ContributorsGin, Taylor (Author) / McCarthy, Alexandra (Co-author) / Berisha, Visar (Thesis director) / Baumann, Alicia (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2022-05
Description
This paper serves to report the research performed towards detecting PD and the effects of medication through the use of machine learning and finger tapping data collected through mobile devices. The primary objective for this research is to prototype a PD classification model and a medication classification model that predict the following: the individual’s disease status and the medication intake time relative to performing the finger-tapping activity, respectively.
ContributorsMcCarthy, Alexandra (Author) / Gin, Taylor (Co-author) / Berisha, Visar (Thesis director) / Baumann, Alicia (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2022-05
Description
The ability of magnetic resonance imaging (MRI) to image any part of the human body without the effects of harmful radiation such as in CAT and PET scans established MRI as a clinical mainstay for a variety of different ailments and maladies. Short wavelengths accompany the high frequencies present in high-field MRI, and are on the same scale as the human body at a static magnetic field strength of 3 T (128 MHz). As a result of these shorter wavelengths, standing wave effects are produced in the MR bore where the patient is located. These standing waves generate bright and dark spots in the resulting MR image, which correspond to irregular regions of high and low clarity. Coil loading is also an inevitable byproduct of subject positioning inside the bore, which decreases the signal that the region of interest (ROI) receives for the same input power. Several remedies have been proposed in the literature to remedy the standing wave effect, including the placement of high permittivity dielectric pads (HPDPs) near the ROI. Despite the success of HPDPs at smoothing out image brightness, these pads are traditionally bulky and take up a large spatial volume inside the already small MR bore. In recent years, artificial periodic structures known as metamaterials have been designed to exhibit specific electromagnetic effects when placed inside the bore. Although typically thinner than HPDPs, many metamaterials in the literature are rigid and cannot conform to the shape of the patient, and some are still too bulky for practical use in clinical settings. The well-known antenna engineering concept of fractalization, or the introduction of self-similar patterns, may be introduced to the metamaterial to display a specific resonance curve as well as increase the metamaterial’s intrinsic capacitance. Proposed in this paper is a flexible fractal-inspired metamaterial for application in 3 T MR head imaging. To demonstrate the advantages of this flexibility, two different metamaterial configurations are compared to determine which produces a higher localized signal-to-noise ratio (SNR) and average signal measured in the image: in the first configuration, the metamaterial is kept rigid underneath a human head phantom to represent metamaterials in the literature (single-sided placement); and in the second, the metamaterial is wrapped around the phantom to utilize its flexibility (double-sided placement). The double-sided metamaterial setup was found to produce an increase in normalized SNR of over 5% increase in five of six chosen ROIs when compared to no metamaterial use and showed a 10.14% increase in the total average signal compared to the single-sided configuration.
ContributorsSokol, Samantha (Author) / Sohn, Sung-Min (Thesis director) / Allee, David (Committee member) / Jones, Anne (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2022-05