Using Electroencephalogram Biosignal Changes for Delirium Detection in Intensive Care Units

Area of Science:

• Neuroscience
• Medical Technology
• Intensive Care Medicine

Background:

• Biosignal data from quantitative electroencephalography (QEEG) research holds potential for developing delirium detection algorithms.
• Investigating biosignal alterations during delirium states is crucial for improving patient outcomes in intensive care units.

Purpose of the Study:

• To investigate specific biosignal changes associated with delirium using QEEG data from medical intensive care unit patients.
• To assess the potential of QEEG for detecting delirium in a clinical setting.

Main Methods:

• An observational study conducted from September 2018 to December 2019 in South Korea.
• Delirium assessment using the Korean version of the Confusion Assessment Method for the Intensive Care Unit.
• Quantitative EEG (QEEG) recordings (20 minutes) analyzed for power spectrum in centroparietal and parietal regions (eyes open) using 5-minute epochs.

Main Results:

• Patients in delirium exhibited significantly lower absolute power spectra for alpha, beta, gamma waves, and spectral edge frequency 50% compared to non-delirious patients.
• Brain-mapping revealed inactivation of these specific brain waves during delirious states.
• Significant differences in QEEG power spectra were observed between delirious and non-delirious states.

Conclusions:

• Quantitative EEG (QEEG) shows promise in detecting delirium by identifying changes in the centroparietal and parietal brain regions.
• Lower power spectra of alpha, beta, gamma waves, and spectral edge frequency 50% are reliable indicators for distinguishing delirium from its absence.
• QEEG analysis can serve as a valuable tool for the objective assessment of delirium in intensive care settings.