PET-validated EEG-Machine learning algorithm predicts brain amyloid pathology in pre-dementia alzheimer’s disease
N. H. Kim, U. Park, S. W. Kang
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AbstractDeveloping reliable biomarkers is important for screening Alzheimer’s disease (AD) and monitoring its progression. Although EEG is non-invasive direct measurement of brain neural activity and has potentials for various neurologic disorders, vulnerability to noise, difficulty in clinical interpretation and quantification of signal information have limited its clinical application. There have been many research about machine learning (ML) adoption with EEG, but the accuracy of detecting AD is not so high or not validated with Aβ PET scan. We developed EEG-ML algorithm to detect brain Aβ pathology among subjective cognitive decline (SCD) or mild cognitive impairment (MCI) population, and validated it with Aβ PET.Keywords: EEG-Machine learning, brain amyloid pathology, pre-dementia alzheimer’s disease
N. H. Kim, U. Park, S. W. Kang (2022). PET-validated EEG-Machine learning algorithm predicts brain amyloid pathology in pre-dementia alzheimer’s disease. Gerontechnology, 21(s),2-2
https://doi.org/10.4017/gt.2022.21.s.789.2.sp3