Vol.122, No.2, 2020, pp.721-741, doi:10.32604/cmes.2020.07470
Grading Method for Hypoxic-Ischemic Encephalopathy Based on Neonatal EEG
  • Jingmin Guo1, Xiu Cheng1, Duanpo Wu2, 3, *
1 Fujian Provincial Maternity and Children’s Hospital, Affiliated Hospital of Fujian Medical University, Fuzhou, China.
2 School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, China.
3 Hangzhou Neuro Science and Technology Co., Ltd., Hangzhou, China.
∗ Corresponding Author: Duanpo Wu. Email: wuduanpo@hdu.edu.cn.
(This article belongs to this Special Issue: Computer Methods in Bio-mechanics and Biomedical Engineering)
Received 23 May 2019; Accepted 03 July 2019; Issue published 01 February 2020
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.
Hypoxic-ischemic encephalopathy, electroencephalograph, neonate, support vector machine.
Cite This Article
Guo, J., Cheng, X., Wu, D. (2020). Grading Method for Hypoxic-Ischemic Encephalopathy Based on Neonatal EEG. CMES-Computer Modeling in Engineering & Sciences, 122(2), 721–741.
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