Effect of Monosodium Glutamate (MSG) On Tissue and Function of Liver and Kidney and Body Weight in Male Albino Mice
Keywords:
HDE, EEG, SQI, Monitor the Depth of AnaesthesiaAbstract
Delivering a proper amount of anesthetic agent is a critical factor directly related to the
health status of patients under surgery. Commercially available tools for measuring the level of
anesthesia including Bispectral Index (BIS) were developed to monitor the depth of anesthesia
(DoA), however, its combination of black box algorithm with hardware has limitations in
algorithmic interoperability. In addition, many hospitals cannot afford it due to its high cost. This
research aims to develop an intelligent model that can determine the DoA based on a signal from a
single channel electroencephalograph (EEG). In order to split EEG signals, a sliding window is
utilized in a segmentation technique. On each EEG segment, a hierarchical dispersion entropy,
abbreviated as HDE, was calculated. The EEG signal is then retrieved with HDE characteristics
after it has been split into four levels. Several other statistical metrics, such as Q-Q plots,
regression coefficients, and correlation coefficients, are used in order to evaluate the suggested
model that is based on HDE in terms of the BIS index. In addition, the suggested model is tested
against the BIS index to determine how well it can predict the quality of the power signals. The
findings indicate that the suggested model demonstrates an earlier reaction compared with the BIS
index whenever the state of the patient transitions from deep to moderate anesthesia. The accuracy
of the model that was proposed in this research came in at 95 percent
References
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.
Monk TG, Saini V, Weldon BC, Sigl JC (2005) Anesthetic Management and OneYear Mortality
After Noncardiac Surgery. Anesthesia & Analgesia 100: 4-10.
Al-Kadi MI, Reaz MB, Ali MAM (2013) Evolution of Electroencephalogram Signal Analysis
Techniques during Anesthesia. Sensors 13: 6605-6635.
.Nguyen-Ky T, Wen P, Li Y (2009) Theoretical basis for identification of different anesthetic
states based on routinely recorded EEG during operation. Computers in Biology and Medicine 39:
-45.
Sebel PS, T Andrew B, Ghoneim MM, Rampil IJ, Padilla RE, et al. (2004) The incidence of
awareness during anesthesia: a multicenter United States study. Anesthesia & Analgesia 99: 833-
Johansen JW, Sebel PS (2000) Development and clinical application of electroencephalographic
bispectrum monitoring. Anesthesiology 93: 1336-1344.
Messner M, Beese U, Romstock J, Dinkel M, Tschaikowsky K (2003) The bispectral index
declines during neuromuscular block in fully awake persons. Anesthesia and Analgesia 97: 488-
Zanner R, Pilge S, Schneider G, Kreuzer M, Kochs EF (2006) Time delay of EEG index
calculation: analysis of Narcotrend, Bispectral and Cerebral State Index using recorded EEG
signals: A-107. European Journal of Anaesthesiology 23: 27.
Chen, S.J., Peng, C.J., Chen, Y.C., Hwang, Y.R., Lai, Y.S., Fan, S.Z. and Jen, K.K., 2016.
Comparison of FFT and marginal spectra of EEG using empirical mode decomposition to monitor
anesthesia. Computer methods and programs in biomedicine, 137, pp.77-85.
Benzy, V. K., & Jasmin, E. A. (2015). A combined wavelet and neural network based model for
classifying depth of anaesthesia. Procedia Computer Science, 46, 1610-1617.
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, 102663.
Najafabadi, M.M., Villanustre, F., Khoshgoftaar, T.M., Seliya, N., Wald, R. and Muharemagic,
E., 2015. Deep learning applications and challenges in big data analytics. Journal of big data, 2(1),
pp.1-21.
Johansen, Jay W. "Update on bispectral index monitoring." Best practice & research Clinical
anaesthesiology 20, no. 1 (2006): 81-99.
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, 102663
Li, T., & Wen, P. (2017). Depth of anaesthesia assessment using interval second-order difference
plot and permutation entropy techniques. IET Signal Processing, 11(2), 221-227.
Alsafy, Iman, and Mohammed Diykh. "Developing a robust model to predict depth of anesthesia
from single channel EEG signal." Physical and Engineering Sciences in Medicine (2022): 1-16.
Diykh, M., Li, Y., Wen, P. and Li, T., 2018. Complex networks approach for depth of anesthesia
assessment. Measurement, 119, pp.178-189.
Downloads
Issue
Section
License
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to Journal of education for Pure Science (Jeds), University of Thi-Qar as publisher of the journal.
Copyright encompasses exclusive rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms and any other similar reproductions, as well as translations. The reproduction of any part of this journal, its storage in databases and its transmission by any form or media, such as electronic, electrostatic and mechanical copies, photocopies, recordings, magnetic media, etc. , will be allowed only with a written permission from Journal of education for Pure Science (Jeds), University of Thi-Qar.
Journal of education for Pure Science (Jeds), University of Thi-Qar, the Editors and the Advisory International Editorial Board make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal. In any way, the contents of the articles and advertisements published in the Journal of education for Pure Science (Jeds), University of Thi-Qar are sole and exclusive responsibility of their respective authors and advertisers.
