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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Principais autores:	Jiann-Shing Shieh, Jeng-Fu Wu, Kuo-Kuang Jen, Jeng-Rung Huang, Maysam F. Abbod, Shou-Zen Fan
Formato:	Artigo
Idioma:	Inglês
Publicado em:	MDPI AG 2013-08-01
coleção:	Entropy
Assuntos:	sample entropy electroencephalography depth of anesthesia multivariate empirical mode decomposition artificial neural networks receiver operating characteristic curve
Acesso em linha:	http://www.mdpi.com/1099-4300/15/9/3325
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