Analysis of EEG via multivariate empirical mode decomposition for depth of anesthesia based on sample entropy
type: Item
instance of: scholarly article
title: Analysis of EEG via multivariate empirical mode decomposition for depth of anesthesia based on sample entropy
zbMATH Open document ID: 1360.92066
author: Qin Wei
author: Quan Liu
author: Shou-Zhen Fan
author: Cheng-Wei Lu
author: Tzu-Yu Lin
author: Maysam Abbod
author: Jiann-Shing Shieh
published in: Entropy
publication date: 2016-05-10T00:00:00Z
review text: Summary: In monitoring the depth of anesthesia (DOA), the electroencephalography (EEG) signals of patients have been utilized during surgeries to diagnose their level of consciousness. Different entropy methods were applied to analyze the EEG signal and measure its complexity, such as spectral entropy, approximate entropy (ApEn) and sample entropy (SampEn). However, as a weak physiological signal, EEG is easily subject to interference from external sources such as the electric power, electric knives and other electrophysiological signal sources, which lead to a reduction in the accuracy of DOA determination. In this study, we adopt the multivariate empirical mode decomposition (MEMD) to decompose and reconstruct the EEG recorded from clinical surgeries according to its best performance among the empirical mode decomposition (EMD), the ensemble EMD (EEMD), and the complementary EEMD (CEEMD) and the MEMD. Moreover, according to the comparison between SampEn and ApEn in measuring DOA, the SampEn is a practical and efficient method to monitor the DOA during surgeries at real time.
Mathematics Subject Classification ID: 92C55
Mathematics Subject Classification ID: 94A17
zbMATH DE Number: 6578340
zbMATH Keywords: electroencephalograph
zbMATH Keywords: sample entropy
zbMATH Keywords: multivariate empirical mode decomposition
zbMATH Keywords: depth of anesthesia
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.3390/e15093458
OpenAlex ID: W2046826796
cites work: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
cites work: Multivariate empirical mode decomposition
cites work: Filter Bank Property of Multivariate Empirical Mode Decomposition
cites work: Adaptive computation of multiscale entropy and its application in EEG signals for monitoring depth of anesthesia during surgery
cites work: Approximate entropy as a measure of system complexity.
DOI: 10.3390/E15093458
description: scientific article; zbMATH DE number 6578340