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Early Delirium Prediction Via Serial EEG Trajectories and Machine Learning

NCT07536854 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 73

Last updated 2026-04-17

No results posted yet for this study

Summary

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.

Conditions

  • Delirium
  • Trauma
  • Brain Dysfunction
  • Critical Illness

Sponsors & Collaborators

  • Ajou University School of Medicine

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
65 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-04-01
Primary Completion
2025-04-27
Completion
2025-04-30

Countries

  • South Korea

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07536854 on ClinicalTrials.gov