Monitoring the depth of anaesthesia using Hurst exponent and Bayesian methods

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

doi:10.1049/iet-spr.2013.0113

Cited by 11 publications

(3 citation statements)

References 40 publications

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“…In recent years, several nonlinear methods based on single-channel EEG signals such as fractal dimension ( Li et al, 2017 ; Nguyen, Wen, & Li, 2010 ), Bayesian analysis ( Nguyen-Ky, Wen, & Li, 2013 , Nguyen-Ky, Wen, & Li, 2014 ), phase-rectified signal averaging ( Liu, Chen, Fan, Abbod, & Shieh, 2016 ; Liu, Chen, Fan, Abbod, & Shieh, 2017 ) and entropy ( Li & Wen, 2016 ; Liang et al, 2015 ; Shalbaf, Saffar, Sleigh, & Shalbaf, 2017 ; Shalbaf, Saffar, & Sleigh, 2020 ) have been proposed for DOA assessment. Today, Bispectral index (BIS) monitoring is the most commonly used single-channel EEG to measure DOA during general anesthesia and has been widely accepted for its accuracy.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Anesthesia Depth Using Effective Brain Connectivity Based on Transfer Entropy on EEG Signal

Sanjari

Shalbaf

et al. 2021

BCN

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Introduction: Ensuring an adequate Depth of Anesthesia (DOA) during surgery is essential for anesthesiologists. Since the effect of anesthetic drugs is on the central nervous system, brain signals such as Electroencephalogram (EEG) can be used for DOA estimation. Anesthesia can interfere among brain regions, so the relationship among different areas can be a key factor in the anesthetic process. Methods: In this paper, by combining the Wiener causality concept and the conditional mutual information, a nonlinear effective connectivity measure called Transfer Entropy (TE) is presented to describe the relationship between EEG signals at frontal and temporal regions from eight volunteers in three anesthetic states (awake, unconscious and recovery). This index is also compared with Granger causality and partial directional coherence methods as common effective connectivity indexes. Results: Based on a statistical analysis of the probability predictive value and Kruskal-Wallis statistical method, TE can effectively fallow the effect-site concentration of propofol and distinguish the anesthetic states well, and perform better than the other effective connectivity indexes. This index is also better than Bispectral Index (BIS) as commercial DOA monitor because of the faster response and higher correlation with the drug concentration effect-site, less irregularity in the unconscious state and better ability to distinguish three states of anesthestesia. Conclusion: TE index is a confident indicator for designing a new monitoring system of the two EEG channels for DOA estimation.

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Section: Introductionmentioning

confidence: 99%

Assessment of Anesthesia Depth Using Effective Brain Connectivity Based on Transfer Entropy on EEG Signal

Sanjari

Shalbaf

et al. 2021

BCN

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“…This transformation implicitly assumes that the dynamics of neural activity are stationary and linear, but do not consider the nonstationary and non-linear or chaotic behaviors in EEG signals [13]. To overcome these problems, many nonlinear analysis methods are proposed, for example, Lempel-Ziv complexity [14], detrended fluctuation analysis [15][16][17], fractal-scaling analysis [18], the Hurst exponent [19] and Poincaré plot [20], etc. In particular, entropies are another proposed nonlinear methods to quantify the regularity of EEG for estimating DOA, such as approximate entropy (ApEn) [21], sample entropy (SampEn) [22,23] and permutation entropy (PeEn) [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Electroencephalogram variability analysis for monitoring depth of anesthesia

et al. 2021

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In this paper, a new approach of extracting and measuring the variability in electroencephalogram (EEG) was proposed to assess the depth of anesthesia (DOA) under general anesthesia. The EEG variability (EEGV) was extracted as a fluctuation in time interval that occurs between two local maxima of EEG. Eight parameters related to EEGV were measured in time and frequency domains, and compared with state-of-the-art DOA estimation parameters, including sample entropy, permutation entropy, median frequency and spectral edge frequency of EEG. The area under the receiver-operator characteristics curve (AUC) and Pearson correlation coefficient were used to validate its performance on 56 patients. Our proposed EEGV-derived parameters yield significant difference for discriminating between awake and anesthesia stages at a significance level of 0.05, as well as improvement in AUC and correlation coefficient on average, which surpasses the conventional features of EEG in detection accuracy of unconscious state and tracking the level of consciousness. To sum up, EEGV analysis provides a new perspective in quantifying EEG and corresponding parameters are powerful and promising for monitoring DOA under clinical situations.

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“…Recently, methods for EEG signal analysis have developed very rapidly, and these methods have been widely used in DOA. Since the EEG signal is a nonlinear signal, the nonlinear method has been favored by researchers, such as Lyapunov exponent [11], L-Z complexity analysis [12], Bayesian method [13,14], Hilbert-Huang transform [15], recurrence analysis [16], Detrended Fluctuation Analysis (DFA) [17], and Entropy [18][19][20]. Although these methods can initially extract the characteristics of EEG signals, they have a common flaw: none of these parameters can independently monitor DOA.…”

Section: Introductionmentioning

confidence: 99%

Monitoring the depth of anesthesia using Autoregressive model and Sample entropy

Luo

et al. 2019

Preprint

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Anesthesia is an important part in modern surgery, and the way how to effectively monitor the depth of anesthesia (DOA) is core issue in the anesthesia work. Since anesthetics mainly affected the brain of patients, it is very effective to monitor DOA by electroencephalogram (EEG). This paper proposes a method for monitoring DOA using EEG. First, the sample entropy (SampEn) of EEG were calculated as a feature vector. Simultaneously, the Burg recursive algorithm was used to solve the autoregressive model (AR model) and AR coefficients were extracted as feature vectors. Later, according to the characteristics of uneven distribution of sample points, the weighted k-nearest neighbor (WKNN) classifier was selected. The Anesthesia was divided into awake, mild, moderate and deep by WKNN classifier. According to the results, the correlation coefficient between the SampEn of the EEG and Bispectral Index (BIS) is above 0.8. There is a correlation between the first five orders of AR coefficient and the BIS index, and the correlation of the second order reaches 0.8. Through the validation of 30 patients, this method can assessment of DOA effectively and accurately.

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Monitoring the depth of anaesthesia using Hurst exponent and Bayesian methods

Cited by 11 publications

References 40 publications

Assessment of Anesthesia Depth Using Effective Brain Connectivity Based on Transfer Entropy on EEG Signal

Assessment of Anesthesia Depth Using Effective Brain Connectivity Based on Transfer Entropy on EEG Signal

Electroencephalogram variability analysis for monitoring depth of anesthesia

Monitoring the depth of anesthesia using Autoregressive model and Sample entropy

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