Detection of Dementia: Using Electroencephalography and Machine Learning
Date
2023-12-05
Authors
Ahmed, Tanveer
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Abstract
Dementia 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.
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Keywords
Dementia, Electroencephalography, Signal Processing, Machine Learning