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dc.contributor.advisorRandall Davis.en_US
dc.contributor.authorHuang, Lauren(Lauren A.)en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2019-09-11T21:55:18Z
dc.date.available2019-09-11T21:55:18Z
dc.date.copyright2017en_US
dc.date.issued2017en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/122052
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 81-82).en_US
dc.description.abstractNeurodegenerative 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.en_US
dc.description.statementofresponsibilityby Lauren Huang.en_US
dc.format.extent82 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT 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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleThe digital symbol digit test : screening for Alzheimer's and Parkinson'sen_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.oclc1108620165en_US
dc.description.collectionM.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienceen_US
dspace.imported2019-09-11T21:55:15Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentEECSen_US


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