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

ABSTRACT:

Objectives

More 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 from electroencephalography (EEG) can predict post-stroke cognitive function using machine learning approach.

Methods

We enrolled consecutive stroke patients who underwent both EEG during the acute stroke phase and cognitive assessments 3 months post-stroke. We preprocessed acute stroke EEG data to eliminate low-quality epochs, then performed independent component analysis and quantified network characteristics using iSyncBrain®. Cognitive function was evaluated using the Montrea

SUBMITTER: Lee M 

PROVIDER: S-EPMC10568623 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature