EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks

Liu, Quan; Chen, Yi-Feng; Fan, Shou-Zen; Abbod, Maysam F.; Shieh, Jiann-Shing

doi:10.1155/2015/232381

Cited by 41 publications

(31 citation statements)

References 52 publications

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“…Previous studies have found distinct results for different scales in MSE and suggested that a single scale is not enough for the analysis of biological signals [36,38,40,54,113,127,128]. However, the interpretation of the biological mechanisms reflected by multiple scales has not been well-established.…”

Section: Entropy With Multiple Scales Corresponding To Various Rangesmentioning

confidence: 99%

Altered Brain Complexity in Women with Primary Dysmenorrhea: A Resting-State Magneto-Encephalography Study Using Multiscale Entropy Analysis

Low¹,

Kuo

Liu³

et al. 2017

Entropy

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How chronic pain affects brain functions remains unclear. As a potential indicator, brain complexity estimated by entropy-based methods may be helpful for revealing the underlying neurophysiological mechanism of chronic pain. In this study, complexity features with multiple time scales and spectral features were extracted from resting-state magnetoencephalographic signals of 156 female participants with/without primary dysmenorrhea (PDM) during pain-free state. Revealed by multiscale sample entropy (MSE), PDM patients (PDMs) exhibited loss of brain complexity in regions associated with sensory, affective, and evaluative components of pain, including sensorimotor, limbic, and salience networks. Significant correlations between MSE values and psychological states (depression and anxiety) were found in PDMs, which may indicate specific nonlinear disturbances in limbic and default mode network circuits after long-term menstrual pain. These findings suggest that MSE is an important measure of brain complexity and is potentially applicable to future diagnosis of chronic pain.

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Section: Entropy With Multiple Scales Corresponding To Various Rangesmentioning

confidence: 99%

Altered Brain Complexity in Women with Primary Dysmenorrhea: A Resting-State Magneto-Encephalography Study Using Multiscale Entropy Analysis

Low¹,

Kuo

Liu³

et al. 2017

Entropy

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“…These are three commonly used anaesthetics based on the same mechanisms of action via γ-aminobutyric acid type A (GABAA) receptor [41]. Previous studies have illustrated that propofol is not significantly different from sevoflurane or desflurane for general anaesthesia [42][43][44].…”

Section: Real Eeg Datasetmentioning

confidence: 99%

EEG artifacts reduction by multivariate empirical mode decomposition and multiscale entropy for monitoring depth of anaesthesia during surgery

Liu

Chen

Fan

et al. 2016

Med Biol Eng Comput

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Electroencephalography (EEG) has been widely utilized to measure the depth of anaesthesia (DOA) during operation. However, the EEG signals are usually contaminated by artifacts which have a consequence on the measured DOA accuracy. In this study, an effective and useful filtering algorithm based on multivariate empirical mode decomposition and multiscale entropy (MSE) is proposed to measure DOA. Mean entropy of MSE is used as an index to find artifacts-free intrinsic mode functions. The effect of different levels of artifacts on the performances of the proposed filtering is analysed using simulated data. Furthermore, 21 patients' EEG signals are collected and analysed using sample entropy to calculate the complexity for monitoring DOA. The correlation coefficients of entropy and bispectral index (BIS) results show 0.14 ± 0.30 and 0.63 ± 0.09 before and after filtering, respectively. Artificial neural network (ANN) model is used for range mapping in order to correlate the measurements with BIS. The ANN method results show strong correlation coefficient (0.75 ± 0.08). The results in this paper verify that entropy values and BIS have a strong correlation for the purpose of DOA monitoring and the proposed filtering method can effectively filter artifacts from EEG signals. The proposed method performs better than the commonly used wavelet denoising method. This study provides a fully adaptive and automated filter for EEG to measure DOA more accuracy and thus reduce risk related to maintenance of anaesthetic agents.

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“…That is why EEG signals have been widely used as a powerful tool to capture the information about anaesthetic depth. As a result, many techniques for DoA estimation have been developed based on EEG signals [16][17][18][19][20]. Among the many DoA monitoring devices, the Bispectral index (BIS), developed in 1992 by Aspect Medical Systems [21,22], is the most popular.…”

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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EEG Signals Analysis Using Multiscale Entropy for Depth of Anesthesia Monitoring during Surgery through Artificial Neural Networks

Cited by 41 publications

References 52 publications

Altered Brain Complexity in Women with Primary Dysmenorrhea: A Resting-State Magneto-Encephalography Study Using Multiscale Entropy Analysis

Altered Brain Complexity in Women with Primary Dysmenorrhea: A Resting-State Magneto-Encephalography Study Using Multiscale Entropy Analysis

EEG artifacts reduction by multivariate empirical mode decomposition and multiscale entropy for monitoring depth of anaesthesia during surgery

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

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