Real-time multi-channel monitoring of burst-suppression using neural network technology during pediatric status epilepticus treatment
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- 作者：Christos Papadelisa ; b ; christos.papadelis@childrens.harvard.edu" class="auth_mail" title="E-mail the corresponding author ; Seyedeh Fatemeh Salimi Ashkezaria ; b ; Chiran Doshia ; b ; Sigride Thome-Souzac ; Phillip L. Pearlc ; P. Ellen Granta ; b ; d ; Robert C. Taskerc ; e ; Tobias Loddenkemperc
- 关键词：A ; artifact ; B ; burst ; BCH ; Boston Children&rsquo ; s Hospital ; BFGS ; Broyden&ndash ; Fletcher&ndash ; Goldfarb&ndash ; Shanno quasi-Newton backpropagation ; BR ; Bayesian regularization ; BS ; burst-suppression ; BSD ; burst-suppression dynamic ; BSI ; burst-suppression index ; BSP ; burst-suppression probability ; CM ; confusion matrix ; EDF ; European data format ; EEG ; electroencephalography ; FN ; false negative ; FP ; false positive ; FPR ; false positive rate ; ICU ; intensive care unit ; LM ; Levenberg&ndash ; Marquardt
- 刊名：Clinical Neurophysiology
- 出版年：2016
- 出版时间：August 2016
- 年：2016
- 卷：127
- 期：8
- 页码：2820-2831
- 全文大小：2701 K
文摘
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Multichannel real time system that automatically identifies burst-suppression.
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Estimates burst-suppression index using neural network technology.
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Excellent agreement between automated and manual classification of burst-suppression.