Detection of Alzheimer's Disease and Frontotemporal Dementia: An Explainable Machine Learning Approach Using EEG Signals

Alzheimer's disease and frontotemporal dementia are among the most prevalent neurodegenerative diseases worldwide. With the absence of definitive treatments, early detection and precise diagnosis remain crucial for alleviating patient suffering and reducing healthcare costs. This study aimed to differentiate Alzheimer's disease, frontotemporal dementia and normal cognition using electroencephalography biomarkers. Significant power spectral density and functional connectivity features were identified via one-way analysis of variance and Scheffe's post-hoc test. A three-class classification was performed by a support vector machine classifier and explainable artificial intelligence technique was employed to investigate feature importance. This classifier achieved an average accuracy of 84.09%, and linear shapley additive explanation model identified T5-Fpl connectivity in alpha band as the most important feature, consistent with findings from previous studies. Further clinical research and brain network analysis using advanced deep learning models could enhance our understanding of the cognitive functions and neural mechanisms underlying these dementias.