Matching Items (5)
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- All Subjects: Parkinson's disease
- Creators: Electrical Engineering Program
- Member of: Theses and Dissertations
- Resource Type: Text
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 quality of user interface designs largely depends on the aptitude of the designer. The ability to generate mental abstract models and characterize a target user audience helps greatly when conceiving a design. The dry cleaning point-of-sale industry lacks quality user interface designs. These impaired interfaces were compared with textbook design techniques to discover how applicable published interface design concepts are in practice. Four variations of a software package were deployed to end users. Each variation contained different design techniques. Surveyed users responded positively to interface design practices that were consistent and easy to learn. This followed textbook expectations. Users however responded poorly to customization options, an important feature according to textbook material. The study made conservative changes to the four interface variations provided to end-users. A more liberal approach may have yielded additional results.
ContributorsSmith, Andrew David (Author) / Nakamura, Mutsumi (Thesis director) / Gottesman, Aaron (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2014-05
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
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