The development of an automated machine learning pipeline for the detection of Alzheimer’s Disease
Written by Black Hot Fire Network on October 28, 2022
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 . . .
