The diagnosis of Alzheimer's disease (AD) in routine clinical practice is
most commonly based on subjective clinical interpretations. Quantitative
electroencephalography (QEEG) measures have been shown to reflect
neurodegenerative processes in AD and might qualify as affordable and thereby
widely available markers to facilitate the objectivization of AD assessment.
Here, we present a novel framework combining Riemannian tangent space mapping
and elastic net regression for the development of brain atrophy markers. While
most AD QEEG studies are based on small sample sizes and psychological test
scores as outcome measures, here we train and test our models using data of one
of the largest prospective EEG AD trials ever conducted, including MRI
biomarkers of brain atrophy.Comment: Presented at NIPS 2017 Workshop on Machine Learning for Healt
