Machine Learning Using Electroencephalography Predicts Acute Cerebral Injury in the Pediatric ICU
Arnold J Sansevere1,2,3,4, Syed Muhammad Anwar5,6, Julia S Keenan7,8
1Division of Epilepsy and Neurophysiology, Children's National Hospital, Washington, DC, USA. Arnold.sansevere@tuftsmedicalcenter.org.
Background And Objectives:
The objective of this study was to apply and compare machine learning techniques using clinical and electroencephalography (EEG) background features to predict acute cerebral injury in the pediatric intensive care unit (PICU).
Methods:
This was a prospective study of patients admitted to the PICU from July 2021 to January 2023. We excluded patients with preexisting cerebral injury and known epilepsy. Clinical variables collected included demographics, reason for admission, electroencephalography (EEG) features, and neuroimaging findings. Machine learning techniques to predict cerebral injury included K-nearest neighbor, logistic regression (LR), support vector machines (SVM), random forest, gradient boosting, and Adaboost classifiers. For SVM, we used the linear and radial basis function kernels (RBF). We designed a binary classification for predicting acute brain injury (class 1) versus no brain injury (class 2) and a multiclass classification to better identify details of the brain injury: no injury (class 1), unilateral injury (class 2), and bilateral injury (class 3). The performance was evaluated in terms of precision, recall, and area under the receiver operating characteristic (AUROC) curve.
Results:
Of 201 patients, 42% were female, and the median age was 3.5 years (IQR 1-11.6 years). The most common EEG background was slow/disorganized (73%). In total, 24% of patients had epileptiform discharges and 13% of patients had electrographic seizures. Acute cerebral injury was detected in 51%. For the binary classification to detect the presence or absence of acute cerebral injury, SVM with RBF was most accurate with AUROC of 0.95% and test accuracy of 0.90% for predicting cerebral injury. For the multiclass classification to detect unilateral versus bilateral cerebral injury, LR had the highest test accuracy with 0.77%.
Discussion:
Machine learning can be employed using clinical and EEG features to accurately predict acute cerebral injury in critically ill children.
