An intelligence Model Based on Brain Signals for Deep anesthesia and Awake stages classification

Authors

  • Department of biology, college of Science, University of Thi Qar, Nasiriya 64001, Iraq
  • Department of Computer Science, College of Education for Pure Science / University of Thi-Qar

DOI:

https://doi.org/10.32792/jeps.v12i2.165

Keywords:

Electroencephalogram, Deep Anesthesia, Depth of Anesthesia (DoA), Hierarchical Dispersion Entropy (HDE),, Classification,

Abstract

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
classifiers.

References

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),

-1108.

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).

IEEE.

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

(2020): 375

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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Published

2023-02-09

Issue

Vol. 12 No. 2 (2022): University of Thi-Qar Journal of education for pure science

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