Extraction of anesthesia depth using self similarity and fluctuation analysis on the wavelet coefficients of EEG

Gifani, P.; Rabiee, Hamid R.; Hashemi, Mahmoud Reza; Ghanbari, Mahdi

doi:10.1049/ic:20050323

Cited by 4 publications

(1 citation statement)

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“…It quantitatively analyzes the complexity of EEG signals using a Lempelziv complexity (LZC) algorithm. Gifani et al [5] used detrended fluctuation analysis (DFA) to differentiate awake and anesthetized states. This was further demonstrated by Jospin et al [6] and Kaplan et al [7].…”

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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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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Epilepsy detection using Detrended Fluctuation Analysis

Shalbaf

Hosseini

Analoui

2009

2009 International Conference on Wavelet Analysis and Pattern Recognition

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Nonlinear Analysis of Anesthesia Dynamics by Fractal Scaling Exponent

Gifani

Rabiee

Hashemi

et al. 2006

2006 International Conference of the IEEE Engineering in Medicine and Biology Society

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The depth of anesthesia estimation has been one of the most research interests in the field of EEG signal processing in recent decades. In this paper we present a new methodology to quantify the depth of anesthesia by quantifying the dynamic fluctuation of the EEG signal. Extraction of useful information about the nonlinear dynamic of the brain during anesthesia has been proposed with the optimum Fractal Scaling Exponent. This optimum solution is based on the best box sizes in the Detrended Fluctuation Analysis (DFA) algorithm which have meaningful changes at different depth of anesthesia. The Fractal Scaling Exponent (FSE) Index as a new criterion has been proposed. The experimental results confirm that our new Index can clearly discriminate between aware to moderate and deep anesthesia levels. Moreover, it significantly reduces the computational complexity and results in a faster reaction to the transients in patients' consciousness levels in relations with the other algorithms.

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Extraction of anesthesia depth using self similarity and fluctuation analysis on the wavelet coefficients of EEG

Cited by 4 publications

References 0 publications

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

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

Epilepsy detection using Detrended Fluctuation Analysis

Nonlinear Analysis of Anesthesia Dynamics by Fractal Scaling Exponent

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