Juniper Publishers - Monitoring the Depth of Anesthesia and Current Technology - - Journal of Anesthesia & Intensive Care Medicine (JAICM)

Juniper Publishers - Open Access Journal of Journal of Anesthesia & Intensive Care Medicine


Monitoring the Depth of Anesthesia and Current Technology

Authored by Daniel Alfonso Botero-Rosas

General anesthesia (GA) is defined as a drug-induced loss of consciousness during which patients are not arousable, even by painful stimulation [1]. GA takes an important role in surgical procedures where an anesthetic overdose may lead to drug-associated toxicities, coma and even death; on the other hand a light anesthetic dose may lead to the well-known event of intraoperative awareness, which can cause sleep disorders, depression, night terrors, hospitals fears and post-traumatic stress disorder [2-4]. In this context, monitoring depth of anesthesia has become an important issue in anesthesiology.
Electroencephalographic signal (EEG) reflects the activity of the central nervous system and it has been widely used for monitoring depth of anesthesia. In general terms, the EEG of an anesthetized patient changes from high frequency, low amplitude when awake to low frequency, high amplitude when anesthetized; it is also noted that, during the anesthesia procedure the degree of EEG disorder is reduced. Therefore, the concept of entropy was introduced in EEG signal processing. Entropy is related to the complexity of a signal, and has been considered a promising measure of states of consciousness [5]. State Entropy (SE) and Response Entropy (RE) are indices provided by Datex-Ohmeda S/5TM entropy module (General Electric, Finland), which is currently a reference in EEG monitoring during general anesthesia [6,7]. SE and RE are based on spectral entropy computation over the Fourier spectrum; a description of the algorithm applied is available elsewhere [8]. The M-Entropy module is considered a reference in monitoring the depth of anesthesia based on EEG analysis, particularly the Response Entropy (RE) index, was considered a better predictor of patient response to painful stimuli than the Bispectral index (BIS) [9] .


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