Sample entropy analysis for the estimating depth of anaesthesia through human EEG signal at different levels of unconsciousness during surgeries

PeerJ
Quan LiuJiann-Shing Shieh

Abstract

Estimating the depth of anaesthesia (DoA) in operations has always been a challenging issue due to the underlying complexity of the brain mechanisms. Electroencephalogram (EEG) signals are undoubtedly the most widely used signals for measuring DoA. In this paper, a novel EEG-based index is proposed to evaluate DoA for 24 patients receiving general anaesthesia with different levels of unconsciousness. Sample Entropy (SampEn) algorithm was utilised in order to acquire the chaotic features of the signals. After calculating the SampEn from the EEG signals, Random Forest was utilised for developing learning regression models with Bispectral index (BIS) as the target. Correlation coefficient, mean absolute error, and area under the curve (AUC) were used to verify the perioperative performance of the proposed method. Validation comparisons with typical nonstationary signal analysis methods (i.e., recurrence analysis and permutation entropy) and regression methods (i.e., neural network and support vector machine) were conducted. To further verify the accuracy and validity of the proposed methodology, the data is divided into four unconsciousness-level groups on the basis of BIS levels. Subsequently, analysis of variance (ANOVA) was app...Continue Reading

References

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Citations

Jun 20, 2020·Neuroscience of Consciousness·Joel FrohlichMartin M Monti
Jun 14, 2019·Frontiers in Neural Circuits·Mattia ChiniSebastian H Bitzenhofer
Apr 23, 2020·Der Anaesthesist·J SassenscheidtJ C Kubitz
Dec 13, 2018·Entropy·Amir OmidvarniaGraeme Jackson
Jun 26, 2021·Royal Society Open Science·Thomas F VarleyAlice Patania

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Software Mentioned

SVM
ANN
SPSS Statistics
MEMD
MATLAB
Random Forest
SampEn
Borland C + + Builder
Random
ApEn

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