Early Delirium Prediction Via Serial EEG Trajectories and Machine Learning

The goal of this observational study is to develop a machine learning model that can predict delirium in trauma patients before it clinically appears. The study focuses on analyzing brainwave (EEG) patterns collected over several days in the trauma ICU. By comparing different recording conditions-such as having eyes open versus closed-researchers aim to identify the most effective way to monitor brain health and detect early signs of delirium in critically ill patients.

NCT Number	NCT07536854
Lead Sponsor	Ajou University School of Medicine
Conditions	Delirium, Trauma, Brain Dysfunction, Critical Illness
Enrollment	73 participants
Start Date	2024-04-01
Primary Completion	2025-04-27 (estimated)
Study Completion	2025-04-30 (estimated)
Updated on ClinicalTrials.gov	2026-04-17