Delirium detection using EEG: what and how to measure

Area of Science:

• Neuroscience
• Critical Care Medicine
• Medical Technology

Background:

• Delirium is frequently underrecognized in postoperative and critically ill patients, despite its significant impact.
• Electroencephalography (EEG) is sensitive to delirium but lacks diagnostic specificity in its current applications.
• There is a need for an efficient EEG-based tool for delirium detection using a minimal number of electrodes.

Purpose of the Study:

• To identify the optimal electrode derivation and EEG characteristic for discriminating delirium from non-delirium states.
• To develop a practical EEG-based tool for delirium detection in surgical patients.
• To enhance the diagnostic capabilities for delirium in intensive care settings.

Main Methods:

• Recorded standard EEGs from 28 delirious and 28 non-delirious cardiothoracic surgery patients.
• Selected artifact-free EEG data from the first minute, with eyes open and eyes closed.
• Evaluated six EEG parameters for each derivation, comparing delirium and non-delirium groups using Mann-Whitney U tests.

Main Results:

• The frontal-parietal (F8-Pz) electrode derivation and relative delta power during eyes-closed recordings showed the highest diagnostic accuracy (AUC = 0.99).
• Relative delta power distinguished delirium (0.59) from non-delirium (0.20) with high statistical significance (P = .0000000000018).
• A cutoff value of 0.37 yielded 100% sensitivity and 96% specificity for delirium detection.

Conclusions:

• Relative delta power from a two-electrode, frontal-parietal derivation during eyes-closed EEG effectively distinguishes delirium from non-delirium.
• This finding supports the development of a simplified EEG-based tool for delirium detection in postoperative patients.
• The method shows promise for improving delirium recognition in homogenous, nonsedated surgical populations.