A novel machine learning approach for prediction of postoperative delirium using multi-domain features of high-density EEG

Clinical Snapshot

Key Findings

• P Value: p < 0.001 for linear vs nonlinear model comparison

• Effect Size: 95.71% accuracy with SVM linear kernel model

• Primary Outcome: Classification accuracy for POD prediction

• Nnt Or Sensitivity: Sensitivity and specificity not reported - only overall accuracy provided

• Confidence Interval: Not reported

Clinical Application

Low - requires expensive HD-EEG equipment and specialized technical expertise Limited applicability - HD-EEG not routinely available in Australian hospitals, would require significant infrastructure investment and training Surgical patients in specialized centers with HD-EEG capability

Abstract

Background.Postoperative delirium (POD) poses a significant risk to patients, and accurate prediction of POD can provide guidance for positive interventions. Although many studies have applied machine learning (ML) to electronic health records to predict POD, there has been a lack of studies utilizing electroencephalogram (EEG) data to accurately predict POD.Methods.This retrospective study included 142 patients from two hospitals, among whom 33 developed POD. We extracted multi-domain features from high-density EEG (HD-EEG), screened 39 key features using recursive feature elimination, and analyzed the feature space structure using linear discriminant analysis (LDA). Four ML models, namely support vector machine (SVM), logistic regression (LR), random forest (RF), and gradient boosting machine, were compared. The dataset was divided using 5-fold cross-validation for model training and testing.Results.Compared to the other three models, SVM (linear kernel) performed best in predicting POD, achieving a classification accuracy of 95.71%. Alpha (8-12 Hz) and theta (4-8 Hz) powers, combined with nonlinear dynamics, critically contributed to the model. Furthermore, the performance of the linear models (SVM and LR) was significantly (p< 0.001) better than that of the nonlinear models, with LDA confirming strong feature space separability (Jensen-Shannon divergence = 0.69).Conclusions.HD-EEG data can be used to establish high-quality ML models for POD predictions. Multidomain features enable more comprehensive integration of EEG information related to delirium. The feature space structure significantly affects the performance of the prediction model, with the linear model offering distinct advantages in predicting POD.

References

1. 1.Yu, B., Tang, M., Wang, J., Cui, X., Wu, F., Zou, Y., Liu, J., Zhang, J., & Liang, Z. (2026). A novel machine learning approach for prediction of postoperative delirium using multi-domain features of high-density EEG. Biomedical Physics & Engineering Express. https://doi.org/10.1088/2057-1976/ae5f98