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28 March 2024
 
  » arxiv » 1711.8359

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Riemannian tangent space mapping and elastic net regularization for cost-effective EEG markers of brain atrophy in Alzheimer's disease
Wolfgang Frühwirt ; Matthias Gerstgrasser ; Pengfei Zhang ; Leonard Weydemann ; Markus Waser ; Reinhold Schmidt ; Thomas Benke ; Peter Dal-Bianco ; Gerhard Ransmayr ; Dieter Grossegger ; Heinrich Garn ; Gareth W. Peters ; Stephen Roberts ; Georg Dorffner ;
Date 22 Nov 2017
AbstractThe 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.
Source arXiv, 1711.8359
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