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2017 | OriginalPaper | Buchkapitel

Bayesian Gaussian Process Classification from Event-Related Brain Potentials in Alzheimer’s Disease

verfasst von : Wolfgang Fruehwirt, Pengfei Zhang, Matthias Gerstgrasser, Dieter Grossegger, Reinhold Schmidt, Thomas Benke, Peter Dal-Bianco, Gerhard Ransmayr, Leonard Weydemann, Heinrich Garn, Markus Waser, Michael Osborne, Georg Dorffner

Erschienen in: Artificial Intelligence in Medicine

Verlag: Springer International Publishing

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Abstract

Event-related potentials (ERPs) have been shown to reflect neurodegenerative processes in Alzheimer’s disease (AD) and might qualify as non-invasive and cost-effective markers to facilitate the objectivization of AD assessment in daily clinical practice. Lately, the combination of multivariate pattern analysis (MVPA) and Gaussian process classification (GPC) has gained interest in the neuroscientific community. Here, we demonstrate how a MVPA-GPC approach can be applied to electrophysiological data. Furthermore, in order to account for the temporal information of ERPs, we develop a novel method that integrates interregional synchrony of ERP time signatures. By using real-life ERP recordings of a prospective AD cohort study (PRODEM), we empirically investigate the usefulness of the proposed framework to build neurophysiological markers for single subject classification tasks. GPC outperforms the probabilistic reference method in both tasks, with the highest AUC overall (0.802) being achieved using the new spatiotemporal method in the prediction of rapid cognitive decline.

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Metadaten
Titel
Bayesian Gaussian Process Classification from Event-Related Brain Potentials in Alzheimer’s Disease
verfasst von
Wolfgang Fruehwirt
Pengfei Zhang
Matthias Gerstgrasser
Dieter Grossegger
Reinhold Schmidt
Thomas Benke
Peter Dal-Bianco
Gerhard Ransmayr
Leonard Weydemann
Heinrich Garn
Markus Waser
Michael Osborne
Georg Dorffner
Copyright-Jahr
2017
DOI
https://doi.org/10.1007/978-3-319-59758-4_7

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