@Article{cmes.2020.07470,
AUTHOR = {Jingmin Guo, Xiu Cheng, Duanpo Wu},
TITLE = {Grading Method for Hypoxic-Ischemic Encephalopathy Based on Neonatal EEG},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {122},
YEAR = {2020},
NUMBER = {2},
PAGES = {721--741},
URL = {http://www.techscience.com/CMES/v122n2/38322},
ISSN = {1526-1506},
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.},
DOI = {10.32604/cmes.2020.07470}
}