Monitoring depth of anesthesia using combination of EEG measure and hemodynamic variables

Shalbaf, Reza; Behnam, Hamid; Moghadam, H. Jelveh

doi:10.1007/s11571-014-9295-z

Cited by 58 publications

(32 citation statements)

References 44 publications

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“…As a noninvasive, simple, and relatively low cost approach, resting-state EEG indirectly measures brain neural electric activity from the scalp of the head. It is considered as an integrated appearance of different brain functions, such as depth of anesthesia (Shalbaf et al 2014), Parkinson's disease ), brain death , even under manual acupuncture (Pei et al 2014;Yi et al 2013). Despite it has been around for decades, using EEG as cognitive biomarker to detect and assess AD in individuals is a relatively new effort (Baker et al 2008;Czigler et al 2008;Fraga et al 2013;Hidasi et al 2007;Jelles et al 2008).…”

Section: Introductionmentioning

confidence: 99%

Power spectral density and coherence analysis of Alzheimer’s EEG

et al. 2014

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In this paper, we investigate the abnormalities of electroencephalograph (EEG) signals in the Alzheimer's disease (AD) by analyzing 16-scalp electrodes EEG signals and make a comparison with the normal controls. The power spectral density (PSD) which represents the power distribution of EEG series in the frequency domain is used to evaluate the abnormalities of AD brain. Spectrum analysis based on autoregressive Burg method shows that the relative PSD of AD group is increased in the theta frequency band while significantly reduced in the alpha2 frequency bands, particularly in parietal, temporal, and occipital areas. Furthermore, the coherence of two EEG series among different electrodes is analyzed in the alpha2 frequency band. It is demonstrated that the pair-wise coherence between different brain areas in AD group are remarkably decreased. Interestingly, this decrease of pairwise electrodes is much more significant in inter-hemispheric areas than that in intra-hemispheric areas. Moreover, the linear cortico-cortical functional connectivity can be extracted based on coherence matrix, from which it is shown that the functional connections are obviously decreased, the same variation trend as relative PSD. In addition, we combine both features of the relative PSD and the normalized degree of functional network to discriminate AD patients from the normal controls by applying a support vector machine model in the alpha2 frequency band. It is indicated that the two groups can be clearly classified by the combined feature. Importantly, the accuracy of the classification is higher than that of any one feature. The obtained results show that analysis of PSD and coherence-based functional network can be taken as a potential comprehensive measure to distinguish AD patients from the normal, which may benefit our understanding of the disease.

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Section: Introductionmentioning

confidence: 99%

Power spectral density and coherence analysis of Alzheimer’s EEG

et al. 2014

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“…SVM and ANN demonstrated similar capability of regression. Both models Li et al, 2008;Liu et al, 2016;Ripley & Ripley, 2001;Shalbaf et al, 2015), more research on the appropriate parameters may be required. Moreover, these learning models rely on large datasets to improve themselves.…”

Section: Discussionmentioning

confidence: 99%

Peer Review #1 of "Sample entropy analysis for the estimating depth of anaesthesia through human EEG signal at different levels of unconsciousness during surgeries (v0.1)"

2018

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Estimating of 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 the DoA. In this paper, a novel EEG-based index is proposed to evaluate the DoA for 24 patients receiving general anaesthesia with different levels of unconsciousness. Sample Entropy (SampEn) algorithm was 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 applied to the corresponding index (i.e., regression output). Results indicate that the correlation coefficient has improved to 0.72 ± 0.09 after filtering and to 0.90 ± 0.05 after regression from the initial values of 0.51 ± 0.17. Similarly, the final mean absolute error has dramatically declined to 5.22 ± 2.12. In addition, the ultimate AUC has increased to 0.98 ± 0.02, and the ANOVA analysis indicates that each of the four groups of different anaesthetic levels demonstrated significant difference from the nearest levels.Furthermore, the random forest output was extensively linear in relation to BIS, thus better DoA prediction accuracy. In conclusion, the proposed method provides a concrete basis for monitoring patients' anaesthetic level during surgeries.

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“…They applied an adaptive segmentation technique to extract nine time frequency features from each EEG segment, then a sequential fuzzy clustering algorithm was utilized to classify the extracted features into one of the anaesthetic states. Shalbaf et al [31], assessed the depth of anaesthesia using EEG signals. A permutation entropy and a sample entropy were extracted from the denoised EEG signals, and the features then were fed to an artificial neural network.…”

Section: Introductionmentioning

confidence: 99%

Robust approach to depth of anaesthesia assessment based on hybrid transform and statistical features

Diykh

Miften

Abdulla

et al. 2020

IET Science, Measurement & Technology

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To develop an accurate and efficient depth of anaesthesia (DoA) assessment technique that could help anaesthesiologists to trace the patient's anaesthetic state during surgery, a new automated DoA approach was proposed. It applied Wavelet-Fourier analysis (WFA) to extract the statistical characteristics from an anaesthetic EEG signal and to designed a new DoA index. In this proposed method, firstly, the wavelet transform was applied to a denoised EEG signal, and a Fast Fourier transform was then applied to the wavelet detail coefficient D3. Ten statistical features were extracted and analysed, and from these, five features were selected for designing a new index for the DoA assessment. Finally, a new DoA () was developed and compared with the most popular Bispectral index (BIS) monitor. The results from the testing set showed that there were very high correlations between the and the BIS index during the awake, light and deep anaesthetic stages. In the case of poor signal quality, the BIS index and the were also tested, and the obtained results demonstrated that the could indicate the DoA values, while the BIS failed to show valid outputs for those situations.. Auto-generated PDF by ReView IET Science, Measurement & Technology IET Submission R evised M ohammed.docxM ainDocument IET Review Copy Only 2 This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication in an issue of the journal. To cite the paper please use the doi provided on the Digital Library page.

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Monitoring depth of anesthesia using combination of EEG measure and hemodynamic variables

Cited by 58 publications

References 44 publications

Power spectral density and coherence analysis of Alzheimer’s EEG

Power spectral density and coherence analysis of Alzheimer’s EEG

Peer Review #1 of "Sample entropy analysis for the estimating depth of anaesthesia through human EEG signal at different levels of unconsciousness during surgeries (v0.1)"

Robust approach to depth of anaesthesia assessment based on hybrid transform and statistical features

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