Multi-digit processing and contextualized analysis on the digital symbol digit test
Name
1192544154-MIT.pdf
Size
3.54 MB
Format
Adobe PDF
Checksum (MD5)
dd4d668d1b655950e5d7cf241a2810ce
Author(s)
DeTienne, Elizabeth A.
Advisor(s)
Randall Davis and Dana L. Penney.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
Neurocognitive decline has been shown to occur as early as 15-20 years before obvious symptoms develop for patients with Alzheimer's. Current therapies work best when begun early, but it is very difficult to detect subtle cognitive change before obvious symptoms manifest. We have done analysis on the handwritten data from the digital Symbol Digit Test with the aim of detecting subtle cognitive decline early in the disease progression. We used a large dataset to augment the MNIST handwritten digit dataset. This has enabled us to recognize digits 0-12 with high accuracy, making it possible to automate scoring of the test. We also analyzed subtle features of the handwriting. We contextualized this data through visualizations, revealing a number of interesting trends and deviations for healthy patients versus patients with cognitive decline. For example, impaired participants tend to have more ink than we would expect for their average digit height, and pause for longer before writing a digit. We believe that this analysis will provide valuable new insights into a person's cognitive status.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 53-55).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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