Detection of Dementia: Using Electroencephalography and Machine Learning

Simple item page
dc.contributor.authorAhmed, Tanveer
dc.contributor.supervisorGebali, Fayez
dc.contributor.supervisorEl Miligi, Haytham
dc.date.accessioned2023-12-06T00:31:18Z
dc.date.available2023-12-06T00:31:18Z
dc.date.copyright2023en_US
dc.date.issued2023-12-05
dc.degree.departmentDepartment of Electrical and Computer Engineering
dc.degree.levelMaster of Applied Science M.A.Sc.en_US
dc.description.abstractDementia is a general term used to describe a decline in mental ability that interferes with daily life. This thesis aims to investigate the use of EEG (Electroencephalography) signals to detect dementia, which offers a promising approach in individuals with dementia, as they provide a non-invasive measure of brain activity during language tasks, which can be analyzed using machine learning algorithms to identify patterns. We also implemented various EEG features extraction and selection techniques and machine learning algorithms that have been used and provide an analysis of the results obtained. We also reported that the most people in the age bracket of 60-69 are most likely to have dementia, with females in common. Overall, K-means achieved the highest Silhouette Score for our clustering results is approximately 0.295. And Decision Tree and Random Forest models achieved the best accuracy of 95.83%. The SVM and Logistic Regression models also achieved good accuracy of 91.67% with the Decision Tree and Random Forest slightly outperforming them.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/15674
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectDementiaen_US
dc.subjectElectroencephalographyen_US
dc.subjectSignal Processingen_US
dc.subjectMachine Learningen_US
dc.titleDetection of Dementia: Using Electroencephalography and Machine Learningen_US
dc.typeThesisen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Ahmed_Tanveer_MASc_2023.pdf
Size:
2.65 MB
Format:
Adobe Portable Document Format
Description:
MASc Thesis
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Electronic Theses and Dissertations (ETD)
 
University of Victoria Libraries

We acknowledge and respect the Lək̓ʷəŋən (Songhees and Esquimalt) Peoples on whose territory the university stands, and the Lək̓ʷəŋən and W̱SÁNEĆ Peoples whose historical relationships with the land continue to this day.