An Improved Detrended Moving-Average Method for Monitoring the Depth of Anesthesia

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

doi:10.1109/tbme.2010.2053929

Cited by 42 publications

(18 citation statements)

References 26 publications

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“…Kantelhardt et al, proposed the MDFA method to describe the non-stationary time series, which is based on a generalization DFA method (Kantelhardt et al, 2002 ). Nguyen-Ky et al, used the moving-average DFA method to monitoring the DoA and the results showed that DFA could accurately estimate a patient's hypnotic state (Nguyen-Ky et al, 2010a ).…”

Section: Materials and Statistical Methodsmentioning

confidence: 99%

EEG entropy measures in anesthesia

Liang

Wang

Sun

et al. 2015

Front. Comput. Neurosci.

252

161

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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.

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Section: Materials and Statistical Methodsmentioning

confidence: 99%

EEG entropy measures in anesthesia

Liang

Wang

Sun

et al. 2015

Front. Comput. Neurosci.

252

161

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“…For an accurate and reliable depth of anesthesia assessment, intensive research has been conducted, and various algorithms were developed. The latest methods includes Entropy [7], Detrended moving-average (DMA) [8], Isomap-based estimation [9], Empirical-mode decomposition (EMD) [3], and Bayesian [10]. Nguyen-Ky developed a new technique to rectify the problems of BIS by developing a wavelet-based depth of anesthesia (WDoA) with a help of discrete wavelet transform(DWT) and power spectral density function (PSD) [1].…”

Section: Introductionmentioning

confidence: 99%

Anesthesia Assessment Based on ICA Permutation Entropy Analysis of Two-Channel EEG Signals

Sivakumar

Tao

2019

Brain Informatics

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Inaccurate assessment may lead to inaccurate levels of dosage given to the patients that may lead to intraoperative awareness that is caused by under dosage during surgery or prolonged recovery in patients that is caused by over dosage after the surgery is done. Previous research and evidence show that assessing anesthetic levels with the help of electroencephalography (EEG) signals gives an overall better aspect of the patient's anesthetic state. This paper presents a new method to assess the depth of anesthesia (DoA) using Independent Component Analysis (ICA) and permutation entropy analysis. ICA is performed on two-channel EEG to reduce the noise then Wavelet and permutation entropy are applied on these channels to extract the features. A linear regression model was used to build the new DoA index using the selected features. The new index designed by proposed methods performs well under low signal quality and it was overall consistent in most of the cases where Bispectral index (BIS) may fail to provide any valid value.

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“…Fan et al [8] and Ferenets et al [9] adopted nonlinear methods to quantify EEG variations during anaesthesia. To overcome the difficulties of DFA in discriminating between wakefulness and deep anaesthesia, Nguyen-Ky et al [10] developed an improved detrended moving-average method, while Palendeng et al [11] used the phase and amplitude of EEG signals as measures of DoA.…”

Section: Introductionmentioning

confidence: 99%

Relative Wave Energy based Adaptive Neuro-Fuzzy Inference System model for the estimation of depth of anaesthesia

Benzy

Jasmin

Koshy

et al. 2018

J. Integr. Neurosci.

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Advances in medical research and intelligent modeling techniques have led to developments in anaesthesia management. The present study aims to estimate the depth of anaesthesia using cognitive signal processing and intelligent modeling techniques. The neurophysiological signal that reflects the cognitive state of anaesthetic drugs is the electroencephalogram signal. The information available from electroencephalogram signals during anaesthesia is extracted from the relative wave energy features of those signals. Discrete wavelet transform is used to decompose electroencephalogram signals into four levels and the relative wave energy is computed from approximate and detailed coefficients of the signal sub-bands. Relative wave energy is extracted to determine the degree of importance of different electroencephalogram frequency bands associated with different anaesthetic phases, for example, the awake, induction, maintenance, and recovery phases. The Kruskal-Wallis statistical test is applied to relative wave energy features to check the discriminative capability of the relative wave energy features classified as awake, light anaesthesia, moderate anaesthesia, and deep anaesthesia. A novel depth of anaesthesia index is generated by implementing an adaptive neuro-fuzzy inference system based on a fuzzy c-means clustering algorithm which uses relative wave energy features as inputs. Finally, the generated depth of anaesthesia index is compared with a commercially available depth of anaesthesia monitor, the Bispectral index. Keywords Electroencephalogram; relative wave energy; discrete wavelet; depth of anaesthesia; neurophysiological signal; adaptive neuro-fuzzy system

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An Improved Detrended Moving-Average Method for Monitoring the Depth of Anesthesia

Cited by 42 publications

References 26 publications

EEG entropy measures in anesthesia

EEG entropy measures in anesthesia

Anesthesia Assessment Based on ICA Permutation Entropy Analysis of Two-Channel EEG Signals

Relative Wave Energy based Adaptive Neuro-Fuzzy Inference System model for the estimation of depth of anaesthesia

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