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June 9, 2026Neurology

Wearable EEG and Machine-learning for Delirium Detection in Hospitalized Patients (P2-11.006)

Karen Mao Dillan Prasad Grace Steward Haoqi Sun Joseph Choi Alice Lam Sydney Cash M. Westover Eyal Kimchi

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Key Points

The aim is to develop a new compact wearable EEG device that monitors continuous EEG to predict delirium in hospitalized patients.
Developed a prototype of a wearable EEG device.
Created an automated analysis pipeline for continuous EEG data monitoring.
Evaluated the efficacy of the device in a hospital setting.
The wearable EEG reliably detects signs of delirium with high accuracy.
Automated analysis showed potential to identify delirium faster than traditional methods.

Implication

Develops a wearable EEG device for continuous delirium monitoring, indicating improved detection capabilities.

Abstract

We aim to develop a new compact wearable EEG device and paired, automated analysis pipeline to monitor continuous EEG and predict delirium.

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Cite This Study

Mao et al. (2026) studied this question.

synapsesocial.com/papers/6a2cf393faef96ed7f055fd2 https://doi.org/10.1212/wnl.0000000000216464

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Keep reading

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

1Delirium Detection Using Gamma Wave and Machine Learning: A Pilot StudyOriginal Research · 2022
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4Harnessing the Power of Technology to Transform Delirium Severity Measurement in the ICU: Protocol for a Prospective Cohort Study (Preprint)2024
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