Research progress on the depth of anesthesia monitoring based on the electroencephalogram

Abstract

General anesthesia typically involves three key components: amnesia, analgesia, and immobilization. Monitoring the depth of anesthesia (DOA) during surgery is crucial for personalizing anesthesia regimens and ensuring precise drug delivery. Since general anesthetics act primarily on the brain, this organ becomes the target for monitoring DOA. Electroencephalogram (EEG) can record the electrical activity generated by various brain tissues, enabling anesthesiologists to monitor the DOA from real-time changes in a patient's brain activity during surgery. This monitoring helps to optimize anesthesia medication, prevent intraoperative awareness, and reduce the incidence of cardiovascular and other adverse events, contributing to anesthesia safety. Different anesthetic drugs exert different effects on the EEG characteristics, which have been extensively studied in commonly used anesthetic drugs. However, due to the limited understanding of the biological basis of consciousness and the mechanisms of anesthetic drugs acting on the brain, combined with the effects of various factors on existing EEG monitors, DOA cannot be accurately expressed via EEG. The lack of patient reactivity during general anesthesia does not necessarily indicate unconsciousness, highlighting the importance of distinguishing the mechanisms of consciousness and conscious connectivity when monitoring perioperative anesthesia depth. Although EEG is an important means of monitoring DOA, continuous optimization is necessary to extract characteristic information from EEG to monitor DOA, and EEG monitoring technology based on artificial intelligence analysis is an emerging research direction.

Keywords: consciousness; deep learning structure; electroencephalogram; the depth of anesthesia monitoring.

Figure 1

The states of connected consciousness, disconnected consciousness, and unconsciousness. Unresponsiveness does not necessarily mean unconsciousness, and patients who are unresponsive during general anesthesia may experience intraoperative awareness. During general anesthesia, patients may dream, but it is not related to the depth of anesthesia., , [Color figure can be viewed at wileyonlinelibrary.com]

Figure 2

Common electroencephalogram (EEG) waveforms and their clinical significance. Based on frequency and power, the EEG is categorized into distinct frequency bands, that is, δ, β, α, θ, and γ. Different waveforms represent different clinical meanings. [Color figure can be viewed at wileyonlinelibrary.com]

Figure 3

Electroencephalogram (EEG) traces (A) and spectrograms (B) characteristics of several commonly used anesthetics. The use of anesthetic drugs can cause significant changes in EEG oscillations. The EEG characteristics caused by several common types of anesthetic drugs have been studied. By using spectrograms, its features can be more clearly represented. Figure cited from Clinical Electroencephalography for Anesthesiologists Part I: Background and Basic Signatures. doi:10.1097/ALN.0000000000000841. [Color figure can be viewed at wileyonlinelibrary.com]

Figure 4

The relations between deep learning (DL), neural networks, machine learning (ML), and artificial intelligence (AI). An important branch of AI is ML. Different from traditional systems, it does not require explicit commands to draw conclusions. Instead, it automatically learns from data and improves from experience. DL is a subset of ML, which generates predictions from a data set using multilayer neural networks. DL includes convolutional neural networks (CNNs), artificial neural networks (ANNs), and recurrent neural networks (RNNs). Due to their different characteristics, they are applied in various clinical fields. [Color figure can be viewed at wileyonlinelibrary.com]