ABSTRACT
OBJECTIVE: This exploratory study examined quantitative electroencephalography (qEEG) changes in delirium and the use of qEEG features to distinguish postoperative from non-postoperative delirium. METHODS: This project was part of the DeltaStudy, a cross-sectional,multicenterstudy in Intensive Care Units (ICUs) and non-ICU wards. Single-channel (Fp2-Pz) four-minutes resting-state EEG was analyzed in 456 patients. After calculating 98 qEEG features per epoch, random forest (RF) classification was used to analyze qEEG changes in delirium and to test whether postoperative and non-postoperative delirium could be distinguished. RESULTS: An area under the receiver operatingcharacteristic curve (AUC) of 0.76 (95% Confidence Interval (CI) 0.71-0.80) was found when classifying delirium with a sensitivity of 0.77 and a specificity of 0.63 at the optimal operating point. The classification of postoperative versus non-postoperative delirium resulted in an AUC of 0.50 (95%CI 0.38-0.61). CONCLUSIONS: RF classification was able to discriminate delirium from no delirium with reasonable accuracy, while also identifying new delirium qEEG markers like autocorrelation and theta peak frequency. RF classification could not distinguish postoperative from non-postoperative delirium. SIGNIFICANCE: Single-channel EEG differentiates between delirium and no delirium with reasonable accuracy. We found no distinct EEG profile for postoperative delirium, which may suggest that delirium is one entity, whether it develops postoperatively or not.
Subject(s)
Delirium , Electroencephalography , Postoperative Complications , Humans , Delirium/diagnosis , Delirium/physiopathology , Female , Male , Electroencephalography/methods , Aged , Postoperative Complications/diagnosis , Postoperative Complications/physiopathology , Middle Aged , Cross-Sectional Studies , Aged, 80 and overABSTRACT
BACKGROUND: Delirium is associated with neurophysiological changes that can be identified with quantitative EEG analysis techniques (qEEG). AIM: To provide an overview of studies on neurophysiological changes in delirium using various qEEG analysis techniques. METHOD: Literature review. RESULTS: In delirium, there is an increase in delta and theta activity but a decrease in activity in the alpha frequency band. Additionally, there is a decrease in functional connectivity and efficiency of the brain network in the alpha frequency band. CONCLUSION: Delirium is characterized by diffuse slowing of the EEG, reduced functional connectivity, and decreased efficiency of the brain network. Improved functional connectivity could be a new approach to treat delirium.
