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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
Parkinson's disease is a neurodegenerative disorder in the central nervous system that affects a host of daily activities and involves a variety of symptoms; these include tremors, slurred speech, and rigid muscles. It is the second most common movement disorder globally. In Stage 3 of Parkinson's, afflicted individuals begin to develop an abnormal gait pattern known as freezing of gait (FoG), which is characterized by decreased step length, shuffling, and eventually complete loss of movement; they are unable to move, and often results in a fall. Surface electromyography (sEMG) is a diagnostic tool to measure electrical activity in the muscles to assess overall muscle function. Most conventional EMG systems, however, are bulky, tethered to a single location, expensive, and primarily used in a lab or clinical setting. This project explores an affordable, open-source, and portable platform called Open Brain-Computer Interface (OpenBCI). The purpose of the proposed device is to detect gait patterns by leveraging the surface electromyography (EMG) signals from the OpenBCI and to help a patient overcome an episode using haptic feedback mechanisms. Previously designed devices with similar intended purposes utilize accelerometry as a method of detection as well as audio and visual feedback mechanisms in their design.
ContributorsAnantuni, Lekha (Author) / McDaniel, Troy (Thesis director) / Tadayon, Arash (Committee member) / Harrington Bioengineering Program (Contributor) / School of Human Evolution and Social Change (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This thesis discusses the experiences of starting and building a support group for teenagers who have a loved one with Parkinson's Disease. One of the goals of this thesis was to share our experiences with the staff at the Muhammad Ali Parkinson's Center, and the teenagers who will be taking over this group in the future. We discuss why we wanted to start the group, how it's foundation was built, and the challenges we faced and overcame. This is done by highlighting three significant group meetings, and various implications. Transportation, funding, and other issues are discussed.
ContributorsVilla, Roberto (Co-author) / Kisana, Haroon (Co-author) / Montesano, Mark (Thesis director) / Abbaszadegan, Hamed (Committee member) / Barrett, The Honors College (Contributor) / School of Historical, Philosophical and Religious Studies (Contributor) / School of Art (Contributor) / School of Life Sciences (Contributor) / School for the Science of Health Care Delivery (Contributor)
Created2015-05
Description
I conducted a research study with the intent to test an educational tool (a PowerPoint presentation) to evaluate its effectiveness at educating a group of nursing students (ASU college juniors) about Parkinson's disease (PD), Parkinson's medication and medication administration guidelines, and the necessity of getting patients with PD their medication on time. This research was based on the fact that a majority of patients with PD do not get their medication on time in a healthcare environment, and that structured interventions will increase awareness and knowledge of the specific needs of the PD patient. Upon analyzing the results of a survey given before and after the presentation, this educational tool was effective, but more research is needed to justify its implementation into education.
ContributorsHodges, Marie Bernadette (Author) / LuPone, Kathy (Thesis director) / Ash, Deborah (Committee member) / Arizona State University. College of Nursing & Healthcare Innovation (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
The following paper discusses the potential for Designed Ankyrin Repeat Proteins (DARPin) use as a diagnostic tool for neurodegenerative diseases in particular Alzheimer's disease (AD) and Parkinson's disease (PD). The two structures investigated for AD and PD were ADC7 and PDC1. Plasmid transformation was performed in order to grow the DARPin in E. coli for simple expression. Following growth and purification the proteins were validated using SDS-PAGE, Western Blot, BCA and indirect sandwich ELISA using transgenic mouse brain tissue. Targeted functionality of the DARPin structure was utilized during characterization methods to ensure the efficacy of the protein as a diagnostic for the respective disease targets. Both the ADC7 and PDC1 demonstrated improved binding with transgenic mice compared to wild type with a maximum 1.8 and 1.7 relative ratio, respectively. Additionally, both of the proteins demonstrated exclusive binding to their disease target and did not provide false positive results.
ContributorsTindell, John (Co-author) / Card, Emma (Co-author) / Sierks, Michael (Thesis director) / Nannenga, Brent (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Misfolding and aggregation of alpha-synuclein (a-syn) has been strongly correlated with the pathogenesis of Parkinson's disease (PD). Reagents such as single chain antibody fragments (scFv) that can interact with specific aggregate forms of a-syn can be very useful to study how different aggregate forms affect cells. Here we utilize two scFvs, D5 and 10H, that recognize two distinct oligomeric forms of a-syn to characterize the presence of different a-syn aggregates in animal models of PD.
ContributorsAlam, Now Bahar (Author) / Sierks, Michael (Thesis director) / Pauken, Christine (Committee member) / Williams, Stephanie (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor) / Harrington Bioengineering Program (Contributor)
Created2013-05
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
Introduction: Lateral reactive stepping is correlated with impairment in people with Parkinson’s Disease (PwPD). Despite this, there is little known of lateral stepping strategies and performance of these strategies in reactive stepping. Objective: To characterize step strategy in people with PD, characterize changes in these stepping strategies through training, and identify performance improvements in the lateral step strategies. Methods: A total of 31 PwPd who are currently at risk for falls took part in an 18-week various background reactive stepping intervention. The stepping strategies were assessed on two baseline assessments (B1 and B2) immediately followed by a 6- session step training intervention occurring over two weeks. Step strategies were again assessed immediately after training (P1) and two months later (P2). Initial outcomes were characterized step strategies, changes in step strategies, and improvement in performance of step strategies. Results: Three step strategies were established and split into two groups (no cross and cross). Changes in step strategies did not occur significantly both before and after training. Improvement in performance of the step strategies occurred at a significant amount (p=0.05) via a decrease in use of support after training occurred for any step strategies utilized. Conclusion: Step strategies were characterized, and performance of strategies was improved upon following the 2-week training. Lateral step strategies are defined and repeated throughout reactive step training with potential for improvement.
ContributorsBosard, Cal (Author) / Peterson, Daniel (Thesis director) / Larson, David (Committee member) / Barrett, The Honors College (Contributor) / College of Health Solutions (Contributor)
Created2023-05
Description
While REM Sleep Behavior disorder (RBD) has been linked with synucleinopathies, difficulties persist in clinically convenient diagnostic tools which can differentiate between underlying diseases. Identifying markers in the gait of RBD patients may ease the diagnostic process and indicate potential or status for developing more severe disorders. Individuals were referred to Movement Disorders Center of Arizona (MDCA) by a sleep specialist with a confirmed diagnosis of RBD, or those who were clinically indicated after questioning. All participants underwent a skin-biopsy test for α-synuclein, I-ioflupane dopamine transporter(DAT) scan, and had their gait velocity, cadence and stride dynamics assessed by an automated gait analysis system.
ContributorsWebster, Samuel (Author) / Peterson, Daniel (Thesis director) / Evidente, Virgilio (Committee member) / Barrett, The Honors College (Contributor) / Department of Physics (Contributor)
Created2023-05
Description
Overview of optical fractionator technique and Parkinson's Disease relevancy and risk, and its implications towards the relationship between microglial inflammation and Parkinson's Disease.
ContributorsLloyd, Lillian (Author) / Kordower, Jeffery (Thesis director) / George, Rohi (Committee member) / Barrett, The Honors College (Contributor) / Department of Psychology (Contributor) / School of Life Sciences (Contributor)
Created2023-05