Automated classification of neonatal amplitude-integrated EEG based on gradient boosting method
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- 作者：Tao Yang ; Weiting Chen ; wtchen@sei.ecnu.edu.cn" class="auth_mail" title="E-mail the corresponding author ; Guitao Cao gtcao@sei.ecnu.edu.cn" class="auth_mail" title="E-mail the corresponding author
- 关键词：aEEG ; GBDT ; Auto permutation entropy ; Lower border
- 刊名：Biomedical Signal Processing and Control
- 出版年：2016
- 出版时间：July 2016
- 年：2016
- 卷：28
- 期：Complete
- 页码：50-57
- 全文大小：1348 K
文摘
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We redefined and quantified the lower border of aEEG.
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Auto permutation entropy was firstly introduced to describe the characteristics of aEEG.
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GBDT method was applied to the classification of aEEG signals.
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The results using GBDT based on five features show a high classification accuracy of 93.12% and fast running speed.