Multiscale rescaled range analysis of EEG recordings in sevoflurane anesthesia

Liang, Zhenhu; Duan, Li; Ouyang, Gaoxiang; Wang, Yinghua; Voss, Logan J.; Sleigh, Jamie; Li, Xiaoli

doi:10.1016/j.clinph.2011.08.027

Cited by 19 publications

(9 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

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.

Self Cite

250

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.

Self Cite

250

161

View full text Add to dashboard Cite

show abstract

“…The Hurst Exponent (also called Rescaled Range statistics) was used to measure the correlations within V [ 40 ]. The range R ( i ) was defined as [max( V ( i )-min( V ( i ))] and S ( i ) was

.…”

Section: Methodsmentioning

confidence: 99%

Integration of 24 Feature Types to Accurately Detect and Predict Seizures Using Scalp EEG Signals

Zhang

Shen

Liu

et al. 2018

Sensors

View full text Add to dashboard Cite

The neurological disorder epilepsy causes substantial problems to the patients with uncontrolled seizures or even sudden deaths. Accurate detection and prediction of epileptic seizures will significantly improve the life quality of epileptic patients. Various feature extraction algorithms were proposed to describe the EEG signals in frequency or time domains. Both invasive intracranial and non-invasive scalp EEG signals have been screened for the epileptic seizure patterns. This study extracted a comprehensive list of 24 feature types from the scalp EEG signals and found 170 out of the 2794 features for an accurate classification of epileptic seizures. An accuracy (Acc) of 99.40% was optimized for detecting epileptic seizures from the scalp EEG signals. A balanced accuracy (bAcc) was calculated as the average of sensitivity and specificity and our seizure detection model achieved 99.61% in bAcc. The same experimental procedure was applied to predict epileptic seizures in advance, and the model achieved Acc = 99.17% for predicting epileptic seizures 10 s before happening.

show abstract

“…The Isomap-based estimation was used to assess neurophysiological changes during anesthesia [22]. The Hurst exponent and the wavelet transform was applied in multiscale rescaled range analysis algorithms to measure the anesthetic drug effects on brain activity [23].…”

Section: E E E P R O O F Analysis Methods Based On Ensemble Empiricamentioning

confidence: 99%

Consciousness and Depth of Anesthesia Assessment Based on Bayesian Analysis of EEG Signals

Nguyen-Ky

Wen

2013

IEEE Trans. Biomed. Eng.

View full text Add to dashboard Cite

This study applies Bayesian techniques to analyze EEG signals for the assessment of the consciousness and depth of anesthesia (DoA). This method takes the limiting large-sample normal distribution as posterior inferences to implement the Bayesian paradigm. The maximum a posterior (MAP) is applied to denoise the wavelet coefficients based on a shrinkage function. When the anesthesia states change from awake to light, moderate, and deep anesthesia, the MAP values increase gradually. Based on these changes, a new function B(DoA) is designed to assess the DoA. The new proposed method is evaluated using anesthetized EEG recordings and BIS data from 25 patients. The Bland-Alman plot is used to verify the agreement of B(DoA) and the popular BIS index. A correlation between B(DoA) and BIS was measured using prediction probability P(K). In order to estimate the accuracy of DoA, the effect of sample n and variance τ on the maximum posterior probability is studied. The results show that the new index accurately estimates the patient's hypnotic states. Compared with the BIS index in some cases, the B(DoA) index can estimate the patient's hypnotic state in the case of poor signal quality.

show abstract

Multiscale rescaled range analysis of EEG recordings in sevoflurane anesthesia

Cited by 19 publications

References 40 publications

EEG entropy measures in anesthesia

EEG entropy measures in anesthesia

Integration of 24 Feature Types to Accurately Detect and Predict Seizures Using Scalp EEG Signals

Consciousness and Depth of Anesthesia Assessment Based on Bayesian Analysis of EEG Signals

Contact Info

Product

Resources

About