Electroencephalogram-Based Complexity Measures as Predictors of Post-operative Neurocognitive Dysfunction.

Area of Science:

• Neuroscience
• Gerontology
• Medical Engineering

Background:

• Physiologic signals like electroencephalogram (EEG) exhibit complex behaviors due to multi-scale control processes.
• Multi-scale entropy (MSE) quantifies this complexity, with higher complexity generally indicating better health.
• Postoperative delirium is difficult to predict, necessitating novel biomarkers.

Purpose of the Study:

• To investigate if preoperative and intraoperative frontal EEG complexity, using MSE, can predict postoperative delirium and inattention in older adults.
• To analyze the relationship between EEG complexity changes and the development of postoperative delirium.

Main Methods:

• Computed MSE for frontal EEG recordings in 50 patients aged 60 and above, before and during surgery.
• Assessed complexity by calculating the area under the curve of sample entropy plotted against different time scales.
• Examined the crossover point where preoperative and intraoperative MSE curves intersected.

Main Results:

• Average EEG MSE was significantly higher intra-operatively compared to pre-operatively (p = 0.0003).
• A scale-dependent interaction was observed: intraoperative MSE was lower at small scales and higher at large scales than preoperative MSE (interaction p < 0.001).
• Overall EEG complexity did not correlate with delirium or attention, but the crossover point scale showed a trend towards inverse association with delirium severity change (Spearman ρ = -0.31, p = 0.054).

Conclusions:

• Intraoperative EEG complexity increases in older adults but is dependent on the scale of analysis.
• The crossover point of preoperative and intraoperative EEG complexity may serve as a potential predictor for postoperative delirium.
• Further research is warranted to explore if this crossover point reflects neural mechanisms underlying delirium susceptibility.