In this proof-of-concept study, we demonstrate that the quantitative analysis of brief (5 min), resting-state EEGs in the frequency domain using a portable, low density (14 channels) montage reveals significant differences between AD patients and HC. Moreover, a transparent, explainable machine learning approach, guided by conventional statistical methods to identify relevant data features in specific channels and frequency bins based on empirically significant values, results in classifier models that can distinguish subjects in either HC or AD category with high accuracy.
Alzheimer’s disease is the most common cause of dementia among elderly people but lacks treatments capable of slowing disease progression1. The lack of reliable disease endpoints and/or biomarkers contributes in part to the lack of effective therapies12. Functional imaging studies might provide insight, however,…
