Grading Method for Hypoxic-Ischemic Encephalopathy Based on Neonatal EEG

Abstract

The grading of hypoxic-ischemic encephalopathy (HIE) contributes to the clinical decision making for neonates with HIE. In this paper, an automated grading method based on electroencephalogram (EEG) data is proposed to describe the severity of HIE infants, namely mild asphyxia, moderate asphyxia and severe asphyxia. The automated grading method is based on a multi-class support vector machine (SVM) classifier, and the input features of SVM classifier include long-term features which are extracted by decomposing the EEG data into different 64 s epoch data and short-term features which are extracted by segmenting the 64 s epoch data into 8 s epoch data with 4 s overlap. Of note, the correlation coefficient and asymmetry extracted in this paper have obvious discriminating capability in HIE infants classification. The experimental results show that the proposed method can achieve the classification accuracy of 78.3%, 75.8% and 87.0% of the mild asphyxia group, moderate asphyxia group and severe asphyxia group, respectively. Moreover, the overall accuracy and kappa used to evaluate the performance of the proposed method can reach 79.5% and 0.69, respectively.