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Topological Descriptors for Parkinson's Disease Classification and Regression Analysis

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.…

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

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…

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
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RFID Chip for DNA Tracking Identification

Description

A primary need of Forensic science is to individualize missing persons that cannot be identified after death. With the use of advanced technology, Radio Frequency Identification (RFID) implant chips can drastically improve digital tracking and enable robust biological and legal identification. In this paper, I will discuss applications between different…

A primary need of Forensic science is to individualize missing persons that cannot be identified after death. With the use of advanced technology, Radio Frequency Identification (RFID) implant chips can drastically improve digital tracking and enable robust biological and legal identification. In this paper, I will discuss applications between different microchip technologies and indicate reasons why the RFID chip is more useful for forensic science. My results state that an RFID chip is significantly more capable of integrating a mass volume of background information, and can utilize implanted individuals’ DNA profiles to decrease the missing persons database backlogs. Since today’s society uses a lot of digital devices that can ultimately identify people by simple posts or geolocation, Forensic Science can harness that data as an advantage to help serve justice for the public in giving loved ones closure.
ContributorsChastain, Hope Natasha (Author) / Kanthswamy, Sree (Thesis director) / Oldt, Robert (Committee member) / School of Mathematical and Natural Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Remote Sensing and Identification of Dendrite Structures

Description
Metallically embedded dendritic structures have the potential to become a cost-effective means of conducting microwave frequency identification. They are grown quickly and contain no extra circuitry. However, their reaction to microwave frequency signatures has been unknown. Fractals Unlimited (the thesis group) aimed to test the viability of the dendritic structures…

Metallically embedded dendritic structures have the potential to become a cost-effective means of conducting microwave frequency identification. They are grown quickly and contain no extra circuitry. However, their reaction to microwave frequency signatures has been unknown. Fractals Unlimited (the thesis group) aimed to test the viability of the dendritic structures to produce unique electromagnetic signatures through the transmission and reflection of microwaves. This report will detail the work that was done by one team member throughout the last two semesters.
ContributorsEnriquez, Eric Antonio (Co-author) / Kim, Gyoungjae (Co-author) / Martin, Aston (Co-author) / Tennison, William (Co-author) / Trichopolous, Georgios (Thesis director) / Kozicki, Michael (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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Prediction at the Tip of Your Fingers: A Machine Learning Approach to Predict Parkinson's Disease and the Effects of Medication

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…

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
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Prediction at the Tip of Your Fingers: A Machine Learning Approach to Predict Parkinson's Disease and the Effects of Medication

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…

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