2026-09-22T11:05:55Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1220522026-06-06T00:49:38Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Randall Davis.
Huang, Lauren(Lauren A.)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2019-09-11T21:55:18Z
2019-09-11T21:55:18Z
2017
2017
https://hdl.handle.net/1721.1/122052
1108620165
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 81-82).
Neurodegenerative diseases affect the cognition of millions of people worldwide, degrading their quality of life and placing a burden on their families. Early identication can be extremely beneficial in treating or slowing down the onset of these diseases. One technique used to identify early warning signs is the use of cognitive tests. Unfortunately, grading these tests is subjective. In this study, we quantitatively evaluated the digital Symbol Digit Test (dSDT), in which patients translate symbols into digits based on a given mapping. In collaboration with Dr. Penney of Lahey Clinic, we administered the dSDT to over 170 patients using a digitizing pen that measures its position on the page and the pressure applied. We developed support vector machine and logistic regression classifiers that indicate Alzheimer's Disease and Parkinson's Disease with an area under the curve of 0.957 and 0.963, respectively.
by Lauren Huang.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
82 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
The digital symbol digit test : screening for Alzheimer's and Parkinson's
Thesis
application/pdf
2019-09-11T21:55:15Z
Publication
Master
EECS
unknown
unknown
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<Abstract>Neurodegenerative diseases affect the cognition of millions of people worldwide, degrading their quality of life and placing a burden on their families. Early identication can be extremely beneficial in treating or slowing down the onset of these diseases. One technique used to identify early warning signs is the use of cognitive tests. Unfortunately, grading these tests is subjective. In this study, we quantitatively evaluated the digital Symbol Digit Test (dSDT), in which patients translate symbols into digits based on a given mapping. In collaboration with Dr. Penney of Lahey Clinic, we administered the dSDT to over 170 patients using a digitizing pen that measures its position on the page and the pressure applied. We developed support vector machine and logistic regression classifiers that indicate Alzheimer's Disease and Parkinson's Disease with an area under the curve of 0.957 and 0.963, respectively.</Abstract>
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