Background: Electroencephalography (EEG) depicts electrical activity in the brain, and can be used in clinical practice to monitor brain function. In neonatal care, physicians can use continuous bedside EEG monitoring to determine the cerebral recovery of newborns who have suffered birth asphyxia, which creates a need for frequent, accurate interpretation of the signals over a period of monitoring. An automated grading system can aid physicians in the Neonatal Intensive Care Unit by automatically distinguishing between different grades of abnormality in the neonatal EEG background activity patterns.
Methods: This thesis describes using support vector machine as a base classifier to classify seven grades of EEG background pattern abnormality in data provided by the BAby Brain Activity (BABA) Center in Helsinki. We are particularly interested in reconciling the manual grading of EEG signals by independent graders, and we analyze the inter-rater variability of EEG graders by building the classifier using selected epochs graded in consensus compared to a classifier using full-duration recordings.
Results: The inter-rater agreement score between the two graders was κ=0.45, which indicated moderate agreement between the EEG grades. The most common grade of EEG abnormality was grade 0 (continuous), which made up 63% of the epochs graded in consensus. We first trained two baseline reference models using the full-duration recording and labels of the two graders, which achieved 71% and 57% accuracy. We achieved 82% overall accuracy in classifying selected patterns graded in consensus into seven grades using a multi-class classifier, though this model did not outperform the two baseline models when evaluated with the respective graders’ labels. In addition, we achieved 67% accuracy in classifying all patterns from the full-duration recording using a multilabel classifier.
