Monitoring the depth of anaesthesia

Abstract

One of the current challenges in medicine is monitoring the patients' depth of general anaesthesia (DGA). Accurate assessment of the depth of anaesthesia contributes to tailoring drug administration to the individual patient, thus preventing awareness or excessive anaesthetic depth and improving patients' outcomes. In the past decade, there has been a significant increase in the number of studies on the development, comparison and validation of commercial devices that estimate the DGA by analyzing electrical activity of the brain (i.e., evoked potentials or brain waves). In this paper we review the most frequently used sensors and mathematical methods for monitoring the DGA, their validation in clinical practice and discuss the central question of whether these approaches can, compared to other conventional methods, reduce the risk of patient awareness during surgical procedures.

Keywords: cognitive binding; consciousness; general anaesthesia; general anaesthesia monitors; soft sensors.

Figure 1.

The relationship between surgical stimuli, general anaesthetics and awareness.

Figure 2.

Key regions (shaded in colour) in the central nervous system that contribute to the state of consciousness.

Figure 3.

Relative amplitude changes in EEG frequency bands during pentobarbital (PB) and ketamine (K) anaesthesia in an animal model. Legend to colour coded EEG wave bands: black (δ), red (θ), blue (α), green (β) and violet (γ). The solid horizontal line denotes a deep general anaesthesia; the dotted horizontal line denotes a shallow general anaesthesia.

Figure 4.

The conceptual diagram of a DGA monitor.

Figure 5.

The block diagram of the BIS algorithm. Abbreviations: BSR (burst suppression ratio), electromyogram activity (EMG), FFT (fast Fourier transform).

Figure 6.

The block diagram of the Narcotrend algorithm. Abbreviations: BS (burst suppression), TD (time domain), FD (frequency domain), electromyogram activity (EMG).

Figure 7.

Block diagram of the algorithm of the AEP-monitor/2. Abbreviations: ARX (autoregressive models with exogenous input), BS (burst suppression), BSR (burst suppression ratio), BPF (band pass filter), EMG (electromyogram activity), AAI (AEP-ARXI), SNR (signal–to–noise ratio).

Figure 8.

The block diagram of the algorithm of the PSA 4,000 monitor. Abbreviations: BSR (burst suppression ratio), FFT (fast Fourier transform), PSI (patient state index).

Figure 9.

The block diagram of the IoC’s algorithm. Abbreviations: BSR (burst suppression ratio), FD (frequency domain), electromyography activity (EMG).

Figure 10.

Block diagram of the algorithm of the CS Monitor Abbreviations: BS (burst suppression), BSR (burst suppression ratio), FD (frequency domain), electromyogram activity (EMG), CSI (cerebral state index).

Figure 11.

Block diagram of the Entropy module algorithm. Abbreviations: BS (burst suppression), BPF (band pass filter), BSR (burst suppression ratio), FFT (fast Fourier transform).