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April 9, 2024Neurology

Eye Movement Detection Using Electrooculography and Machine Learning in Cardiac Arrest Patients (P10-2.006)

Chelsea Sykora Cameron Hill Michael Cronin Min Chul Shin Stephen J. Schmugge Samuel Tate Joseph Sisto Charlene Ong

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Abstract

To train a machine learning algorithm to identify eye movements from electrooculography (EOG) in a cohort of cardiac arrest (CA) patients.

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Cite This Study

Sykora et al. (2024) studied this question.

synapsesocial.com/papers/68e6fdb8b6db6435876780d4 https://doi.org/10.1212/wnl.0000000000204740

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Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Association of Quantitative Eye Movements With Neurologic Outcomes in Post-cardiac Arrest Patients (P1-19.004)Original Research · 2026
2IMPROVING EOG-BASED CLASSIFICATION PERFORMANCE: A COMPARATIVE ANALYSIS WITH VARIOUS MACHINE LEARNING ALGORITHMSOriginal Research · 2026
3Electrocardiogram (ECG)-based seizure detection using supervised machine-learningcohort · 2025 · 3 citations
4Transfer Learning for Multi-Lead ECG Analysis in Cardiac Arrest Risk Classificationother · 2025
5Intelligent Eye Gaze Localization Method Based on EEG Analysis Using Wearable Headband2024 · 1 citations

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