Diagnosing Alzheimer’s Disease and Frontotemporal Dementia Using Machine Learning and EEG


  • Andrew Yao Conestoga High School
  • Guillermo Goldsztein
  • Alfred Renaud




Alzheimer's disease, Frontotemporal dementia, machine learning, EEG


Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are types of neurodegenerative dementias characterized by progressive cognitive decline. Electroencephalography (EEG) signal analysis is becoming a promising, inexpensive method to early diagnose AD and FTD. Prior research has applied machine learning to classify AD and healthy patients (HC) based on EEG readings, but not distinguishing between AD, FTD, and healthy patients. In the present paper, power spectral features will be extracted from raw EEG recordings for random forest classifiers and artificial neural networks to differentiate among AD, FTD, and HC patients. The first two minutes of 88 EEG recordings were used from the 2nd Department of Neurology in Thessaloniki. The models achieved 96%, 92%, and 95% accuracy when dealing with binary classification problems (AD and HC, AD and FTD, HC and FTD) and 90.4% for all three classes. The accuracies improve upon the results of previous literature.


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How to Cite

Yao, A., Goldsztein, G., & Renaud, A. (2023). Diagnosing Alzheimer’s Disease and Frontotemporal Dementia Using Machine Learning and EEG. Journal of Student Research, 12(4). https://doi.org/10.47611/jsrhs.v12i4.5843



HS Research Articles