Application of Multivariate Empirical Mode Decomposition and Sample Entropy in EEG Signals via Artificial Neural Networks for Interpreting Depth of Anesthesia

Application of Multivariate Empirical Mode Decomposition and Sample Entropy in EEG Signals via Artificial Neural Networks for Interpreting Depth of Anesthesia

EEG (Electroencephalography) signals can express the human awareness activities and consequently it can indicate the depth of anesthesia. On the other hand, Bispectral-index (BIS) is often used as an indicator to assess the depth of anesthesia. This study is aimed at using an advanced signal process...

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Bibliographic Details
Main Authors: Jiann-Shing Shieh, Jeng-Fu Wu, Kuo-Kuang Jen, Jeng-Rung Huang, Maysam F. Abbod, Shou-Zen Fan
Format: Article
Language:English
Published: MDPI AG 2013-08-01
Series:Entropy
Subjects:
sample entropy
electroencephalography
depth of anesthesia
multivariate empirical mode decomposition
artificial neural networks
receiver operating characteristic curve
Online Access:http://www.mdpi.com/1099-4300/15/9/3325

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