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.
|Publication status||Published - 8 Dec 2017|
|Event||NIPS 2017 Workshop on Optimization: 10th NIPS Workshop on Optimization for Machine Learning - Long Beach, Long Beach, United States|
Duration: 8 Dec 2017 → …
Conference number: 10
|Workshop||NIPS 2017 Workshop on Optimization|
|Period||8/12/17 → …|