Analysis of EEG via multivariate empirical mode decomposition for depth of anesthesia based on sample entropy

DOI10.3390/e15093458zbMath1360.92066OpenAlexW2046826796MaRDI QIDQ280538

Maysam F. Abbod, Cheng-Wei Lu, Shou-Zhen Fan, Quan Liu, Tzu-Yu Lin, Jiann-Shing Shieh, Qin Wei

Publication date: 10 May 2016

Published in: Entropy (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3390/e15093458

zbMATH Keywords

depth of anesthesiamultivariate empirical mode decompositionsample entropyelectroencephalograph

Mathematics Subject Classification ID

Biomedical imaging and signal processing (92C55) Measures of information, entropy (94A17)

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Cites Work

• Adaptive computation of multiscale entropy and its application in EEG signals for monitoring depth of anesthesia during surgery
• Multivariate empirical mode decomposition
• Approximate entropy as a measure of system complexity.
• The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
• Filter Bank Property of Multivariate Empirical Mode Decomposition