Machine learning-based prediction of post-stroke cognitive status using electroencephalography-derived brain network attributes

Machine learning-based prediction of post-stroke cognitive status using electroencephalography-derived brain network attributes

ObjectivesMore than half of patients with acute ischemic stroke develop post-stroke cognitive impairment (PSCI), a significant barrier to future neurological recovery. Thus, predicting cognitive trajectories post-AIS is crucial. Our primary objective is to determine whether brain network properties...

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Bibliographic Details
Main Authors: Minwoo Lee, Yuseong Hong, Sungsik An, Ukeob Park, Jaekang Shin, Jeongjae Lee, Mi Sun Oh, Byung-Chul Lee, Kyung-Ho Yu, Jae-Sung Lim, Seung Wan Kang
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-09-01
Series:Frontiers in Aging Neuroscience
Subjects:
ischemic stroke
cognition
electroencephalography
functional network
machine learning
Online Access:https://www.frontiersin.org/articles/10.3389/fnagi.2023.1238274/full

Internet

https://www.frontiersin.org/articles/10.3389/fnagi.2023.1238274/full

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