Improving the accuracy of depth of anaesthesia using modified detrended fluctuation analysis method

Nguyen-Ky, Tai; Wen, Peng; Li, Yan

doi:10.1016/j.bspc.2009.03.001

Cited by 16 publications

(11 citation statements)

References 18 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, various studies have shown that the EEG is a non-stationary signal that exhibits non-linear or chaotic behaviors (Elbert et al, 1994 ; Pritchard et al, 1995 ; Zhang et al, 2001 ; Natarajan et al, 2004 ). This prompted many researchers to adopt non-linear analysis methods in anesthesia study, for example largest Lyapunov exponent (Fell et al, 1996 ), Hurst exponent (Alvarez-Ramirez et al, 2008 ), fractal analysis (Klonowski et al, 2006 ; Gifani et al, 2007 ; Liang et al, 2012 ), detrended fluctuation analysis (DFA) (Jospin et al, 2007 ; Nguyen-Ky et al, 2010b ), recurrence analysis (Huang et al, 2006 ), and non-linear entropies (Bruhn et al, 2001 ; Li et al, 2008a ). In particular, non-linear entropy methods describing the complexity of EEG signals, have received considerable attention.…”

Section: Introductionmentioning

confidence: 99%

EEG entropy measures in anesthesia

Liang

Wang

Sun

et al. 2015

Front. Comput. Neurosci.

239

161

View full text Add to dashboard Cite

Highlights: ► Twelve entropy indices were systematically compared in monitoring depth of anesthesia and detecting burst suppression.► Renyi permutation entropy performed best in tracking EEG changes associated with different anesthesia states.► Approximate Entropy and Sample Entropy performed best in detecting burst suppression.Objective: Entropy algorithms have been widely used in analyzing EEG signals during anesthesia. However, a systematic comparison of these entropy algorithms in assessing anesthesia drugs' effect is lacking. In this study, we compare the capability of 12 entropy indices for monitoring depth of anesthesia (DoA) and detecting the burst suppression pattern (BSP), in anesthesia induced by GABAergic agents.Methods: Twelve indices were investigated, namely Response Entropy (RE) and State entropy (SE), three wavelet entropy (WE) measures [Shannon WE (SWE), Tsallis WE (TWE), and Renyi WE (RWE)], Hilbert-Huang spectral entropy (HHSE), approximate entropy (ApEn), sample entropy (SampEn), Fuzzy entropy, and three permutation entropy (PE) measures [Shannon PE (SPE), Tsallis PE (TPE) and Renyi PE (RPE)]. Two EEG data sets from sevoflurane-induced and isoflurane-induced anesthesia respectively were selected to assess the capability of each entropy index in DoA monitoring and BSP detection. To validate the effectiveness of these entropy algorithms, pharmacokinetic/pharmacodynamic (PK/PD) modeling and prediction probability (Pk) analysis were applied. The multifractal detrended fluctuation analysis (MDFA) as a non-entropy measure was compared.Results: All the entropy and MDFA indices could track the changes in EEG pattern during different anesthesia states. Three PE measures outperformed the other entropy indices, with less baseline variability, higher coefficient of determination (R2) and prediction probability, and RPE performed best; ApEn and SampEn discriminated BSP best. Additionally, these entropy measures showed an advantage in computation efficiency compared with MDFA.Conclusion: Each entropy index has its advantages and disadvantages in estimating DoA. Overall, it is suggested that the RPE index was a superior measure. Investigating the advantages and disadvantages of these entropy indices could help improve current clinical indices for monitoring DoA.

show abstract

Section: Introductionmentioning

confidence: 99%

EEG entropy measures in anesthesia

Liang

Wang

Sun

et al. 2015

Front. Comput. Neurosci.

239

161

View full text Add to dashboard Cite

show abstract

“…Scaling exponent (SE, α SE ); estimated using the algorithm proposed by Peng [27][28][29][30], within a range from 20 to 500 ms (n from 10 to 256 samples and linear detrending). See Appendix A for more details on its calculation.…”

Section: Statistical Descriptionmentioning

confidence: 99%

Evaluation of the continuous detection of mental calculation episodes as a BCI control input

Bojorges-Valdez¹,

Yáñez-Suárez²

2015

Computers in Biology and Medicine

View full text Add to dashboard Cite

“…The values near 100 represent an awake state and 0 denotes an isoelectric EEG. There are also other methods for monitoring the DoA, such as the autoregressive class of poly-spectral models [10], Lempel-Ziv complexity analysis [11], artificial neural network [12], entropy [13], de-trended fluctuation analysis [14][15][16], modified de-trended moving average [17] and wavelet transformation [18]. Recently, a time-domain signal processing was combined with multi-layer perceptron to identify the DoA levels [19].…”

Section: Introductionmentioning

confidence: 99%

Monitoring the depth of anaesthesia using Hurst exponent and Bayesian methods

Nguyen-Ky

Wen

2014

IET signal process.

Self Cite

View full text Add to dashboard Cite

This study proposes a novel index ML DoA to identify different anaesthetic states of a patient during surgery. Based on the new index ML DoA , the assessment of depth of anaesthesia (DoA) for a patient can be clearly monitored. Firstly, a modified Bayesian wavelet threshold is proposed to de-noise the electroencephalogram (EEG) signals. Secondly, the Hurst exponent is obtained to classify four states of anaesthesia: deep anaesthesia, moderate anaesthesia, light anaesthesia and awake. Finally, the index ML DoA is derived based on the Hurst exponent and maximum-likelihood function. The ML DoA index is evaluated using clinically obtained EEG signals and the bispectral (BIS) data. The results show that the new index remains robust in the case of poor signal quality where BIS does not. Moreover, the new index ML DoA responds faster than the BIS index during the anaesthetic state transitions of patients. To validate the proposed method, the analysis of variance method is used to compare the new index ML DoA with the BIS index. The results indicate that the ML DoA distribution is better in distinguishing the five DoA states.

show abstract

Improving the accuracy of depth of anaesthesia using modified detrended fluctuation analysis method

Cited by 16 publications

References 18 publications

EEG entropy measures in anesthesia

EEG entropy measures in anesthesia

Evaluation of the continuous detection of mental calculation episodes as a BCI control input

Monitoring the depth of anaesthesia using Hurst exponent and Bayesian methods

Contact Info

Product

Resources

About