Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Sep 28:15:1238274.
doi: 10.3389/fnagi.2023.1238274. eCollection 2023.

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

Minwoo Lee  1 , Yuseong Hong  2 , Sungsik An  3 , Ukeob Park  2 , Jaekang Shin  2 , Jeongjae Lee  1 , Mi Sun Oh  1 , Byung-Chul Lee  1 , Kyung-Ho Yu  1 , Jae-Sung Lim  4 , Seung Wan Kang  2
Affiliations

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

Minwoo Lee et al. Front Aging Neurosci. .

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 Montreal cognitive assessment (MoCA). We initially categorized participants based on the lateralization of their lesions and then developed machine learning models to predict cognitive status in the left and right hemisphere lesion groups.

Results: Eighty-seven patients were included, and the accuracy of lesion laterality prediction using EEG attributes was 97.0%. In the left hemispheric lesion group, the network attributes of the theta band were significantly correlated with MoCA scores, and higher global efficiency, clustering coefficient, and lower characteristic path length were associated with higher MoCA scores. Most features related to cognitive scores were selected from the frontal lobe. The predictive powers (R-squared) were 0.76 and 0.65 for the left and right stroke groups, respectively.

Conclusion: Estimating EEG-based network properties in the acute phase of ischemic stroke through a machine learning model has a potential to predict cognitive outcomes after ischemic stroke.

Keywords: cognition; electroencephalography; functional network; ischemic stroke; machine learning.

PubMed Disclaimer

Conflict of interest statement

YH, UP, JS and SK were employed by iMedisync, Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flow chart showing the statistical verifications, feature selection, and machine learning employed to predict cognitive status 3-month after stroke.
Figure 2
Figure 2
Regression results predicting the MoCA percentile score relative to the actual value for the (A) left and (B) right stroke estimated group.
See this image and copyright information in PMC

References

    1. Aerts H., Fias W., Caeyenberghs K., Marinazzo D. (2016). Brain networks under attack: robustness properties and the impact of lesions. Brain 139, 3063–3083. doi: 10.1093/brain/aww194, PMID: - DOI - PubMed
    1. Al-Qazzaz N. K., Ali S. H. B. M., Ahmad S. A., Islam M. S., Escudero J. (2018). Discrimination of stroke-related mild cognitive impairment and vascular dementia using eeg signal analysis. Med. Biol. Eng. Comput. 56, 137–157. doi: 10.1007/s11517-017-1734-7, PMID: - DOI - PubMed
    1. Bae H.-J., Kim J. Y., Kang J., Kim B. J., Han M.-K., Choi K.-H., et al. . (2022). Sherman lecture award: 15-year experience of the nationwide multicenter stroke registry in Korea. Stroke 53, 2976–2987. doi: 10.1161/STROKEAHA.122.039212, PMID: - DOI - PubMed
    1. Biesbroek J. M., Lim J.-S., Weaver N. A., Arikan G., Kang Y., Kim B. J., et al. . (2021). Anatomy of phonemic and semantic fluency: a lesion and disconnectome study in 1231 stroke patients. Cortex 143, 148–163. doi: 10.1016/j.cortex.2021.06.019, PMID: - DOI - PubMed
    1. Blum S., Jacobsen N. S. J., Bleichner M. G., Debener S. (2019). A riemannian modification of artifact subspace reconstruction for eeg artifact handling. Front. Hum. Neurosci. 13:141. doi: 10.3389/fnhum.2019.00141, PMID: - DOI - PMC - PubMed

LinkOut - more resources