An intelligence Model Based on Brain Signals for Deep anesthesia and Awake stages classification
Monitoring The depth of anesthesia (DoA) is One of the current challenges in medicine. An accurate DoA
can deliver an adequate amount of anastatic medications that could reduce the risk of consciousness or
excessive anesthesia. In this study, an intelligent model classifies deep and awake stages from an
electroencephalogram (EEG) signal was used. It consists of two stages that are considered vital in
designing the DoA. A hierarchical dispersion entropy (HDE) was applied to de-noised EEG segments. In
this study, the EEG signal is decomposed into four experimentally levels. Then DE entropy features are
extracted from each band. An LS-SVM classifier is used to classify the extracted features into two
anesthetic states. We then compared the results of the Least Squares Support Vector Machines LS-SVM
classifier with other types of classifiers such as Multi-class-SVM and k-nearest. We found that the results
of our study were the best because the accuracy reached by us was (95.23%) higher than the other two
Gugino, L. D., Chabot, R. J., Prichep, L. S., John, E. R., Formanek, V., & Aglio, L. S. (2001).
Quantitative EEG changes associated with loss and return of consciousness in healthy adult volunteers
anaesthetized with propofol or sevoflurane. British journal of anaesthesia, 87(3), 421-428.
Avidan, M. S., Zhang, L., Burnside, B. A., Finkel, K. J., Searleman, A. C., Selvidge, J. A., ... & Evers, A.
S. (2008). Anesthesia awareness and the bispectral index. New England journal of medicine, 358(11),
Dahaba, A. A. (2005). Different conditions that could result in the bispectral index indicating an incorrect
hypnotic state. Anesthesia & Analgesia, 101(3), 765-773.
Glen, J. (2010). Use of audio signals derived from electroencephalographic recordings as a novel ‘depth
of anaesthesia’monitor. Medical hypotheses, 75(6), 547-549.
Schwilden, H. (2006). Concepts of EEG processing: from power spectrum to bispectrum, fractals,
entropies and all that. Best Practice & Research Clinical Anaesthesiology, 20(1), 31-48
Kim, D., Ahn, J.H., Heo, G. and Jeong, J.S., 2021. Comparison of Bispectral Index and Patient State
Index values according to recovery from moderate neuromuscular block under steady-state total
intravenous anesthesia. Scientific Reports, 11(1), pp.1-7.
Chen, Y.F., Fan, S.Z., Abbod, M.F., Shieh, J.S. and Zhang, M., 2021. Electroencephalogram variability
analysis for monitoring depth of anesthesia. Journal of Neural Engineering, 18(6), p.066015.
Chowdhury, M. R., Madanu, R., Abbod, M. F., Fan, S. Z., & Shieh, J. S. (2021). Deep learning via ECG
and PPG signals for prediction of depth of anesthesia. Biomedical Signal Processing and Control, 68,
Nguyen-Ky, T., Tuan, H.D., Savkin, A., Do, M.N. and Van, N.T.T., 2021. Real-Time EEG Signal
Classification for Monitoring and Predicting the Transition Between Different Anaesthetic States. IEEE
Transactions on Biomedical Engineering, 68(5), pp.1450-1458.
Li, R., Wu, Q., Liu, J., Wu, Q., Li, C. and Zhao, Q., 2020. Monitoring depth of anesthesia based on
hybrid features and recurrent neural network. Frontiers in neuroscience, 14, p.26.
Nguyen-Ky, T., Wen, P., & Li, Y. (2010). An improved detrended moving-average method for
monitoring the depth of anesthesia. IEEE Transactions on Biomedical Engineering, 57(10), 2369-2378.
Nguyen-Ky, T., Wen, P., & Li, Y. (2013). Consciousness and depth of anesthesia assessment based on
Bayesian analysis of EEG signals. IEEE Transactions on Biomedical Engineering, 60(6), 1488-1498.
Shalbaf, R., Behnam, H., & Jelveh Moghadam, H. (2015). Monitoring depth of anesthesia using combination
of EEG measure and hemodynamic variables. Cognitive Neurodynamics, 9(1), 41-51.
Liu, Q., Chen, Y. F., Fan, S. Z., Abbod, M. F., & Shieh, J. S. (2015). EEG signals analysis using multiscale
entropy for depth of anesthesia monitoring during surgery through artificial neural networks. Computational
and mathematical methods in medicine, 2015.
Jahanseir, M., Setarehdan, S. K., & Momenzadeh, S. (2018). Automatic anesthesia depth staging using
entropy measures and relative power of electroencephalogram frequency bands. Australasian Physical &
Engineering Sciences in Medicine, 41(4), 919-929.
Gu, Y., Liang, Z., & Hagihira, S. (2019). Use of multiple EEG features and artificial neural network to
monitor the depth of anesthesia. Sensors, 19(11), 2499.
Zhu, F. G., Luo, X. G., Hou, C. J., Huo, D. Q., & Dang, P. (2019). Monitoring the depth of anesthesia using
Autoregressive model and Sample entropy. bioRxiv, 634675.
Guo, C., Yu, J., Wu, L., Liu, Y., Jia, C., & Xie, Y. (2019). Analysis and feature extraction of EEG signals
induced by anesthesia monitoring based on wavelet transform. IEEE Access, 7, 41565-41575.
Buades, A., Coll, B., & Morel, J. M. (2005, June). A non-local algorithm for image denoising. In 2005 IEE
Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) (Vol. 2, pp. 60-65).
Azami, H., Bozorgtabar, B., & Shiroie, M. (2011). Automatic signal segmentation using the fractal dimension
and weighted moving average filter. Journal of Electrical & Computer science, 11(6), 8-15.
Jiang, Y.; Peng, C.K.; Xu, Y.S. Hierarchical entropy analysis for biological signals. J. Comput. Appl. Math.
, 236, 728–742.
Li, Y.; Xu, M.; Zhao, H.; Huang, W. Hierarchical fuzzy entropy and improved support vector machine based
binary tree approach for rolling bearing fault diagnosis. Mech. Mach. Theory 2016, 98, 114–132.
Chen, P., Zhao, X., & Jiang, H. (2021). A New Method of Fault Feature Extraction Based on Hierarchical
Dispersion Entropy. Shock and Vibration, 2021.
Luo, Songrong, Wenxian Yang, and Youxin Luo. "Fault diagnosis of a rolling bearing based on adaptive
sparest narrow-band decomposition and RefinedComposite multiscale dispersion entropy." Entropy 22.4
Suykens, J. A., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural
processing letters, 9(3), 293-300.
Diykh, M., Li, Y., Wen, P. and Li, T., 2018. Complex networks approach for depth of anesthesia
assessment. Measurement, 119, pp.178-189.
Liu, Q., Cai, J., Fan, S.Z., Abbod, M.F., Shieh, J.S., Kung, Y. and Lin, L., 2019. Spectrum analysis of
EEG signals using CNN to model patient’s consciousness level based on anesthesiologists’
experience. IEEE Access, 7, pp.53731-53742.
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