Jingmin Guo1, Xiu Cheng1, Duanpo Wu2, 3, *
CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 721-741, 2020, DOI:10.32604/cmes.2020.07470
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… More >
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