Effect of Monosodium Glutamate (MSG) On Tissue and Function of Liver and Kidney and Body Weight in Male Albino Mice
DOI:
https://doi.org/10.32792/jeps.v12i2.215الكلمات المفتاحية:
HDE، EEG، SQI، Monitor the Depth of Anaesthesiaالملخص
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
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