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  1. KEEP
  2. Theses and Dissertations
  3. Barrett, The Honors College Thesis/Creative Project Collection
  4. Topological Descriptors for Parkinson's Disease Classification and Regression Analysis
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Topological Descriptors for Parkinson's Disease Classification and Regression Analysis

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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.

Date Created
2020-05
Contributors
  • Rahman, 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)
Topical Subject
  • Parkinson's disease
  • Topological Data Analysis
  • Machine Learning
  • Persistence Diagrams
  • Persistence Images
Resource Type
Text
Extent
5 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Barrett, The Honors College Thesis/Creative Project Collection
Series
Academic Year 2019-2020
Handle
https://hdl.handle.net/2286/R.I.56458
Embargo Release Date
Sat, 10/17/2020 - 17:17
Level of coding
minimal
Cataloging Standards
asu1
System Created
  • 2020-04-18 12:03:54
System Modified
  • 2021-08-11 04:09:57
  •     
  • 1 year 5 months ago
Additional Formats
  • OAI Dublin Core
  • MODS XML

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