Propofol and Remifentanil Differentially Modulate Frontal Electroencephalographic Activity

Liley, David T. J.; Sinclair, Nicholas C; Lipping, Tarmo; Heyse, Björn; Vereecke, Hugo; Struys, Michel

doi:10.1097/aln.0b013e3181e3d8a6

Cited by 48 publications

(70 citation statements)

References 66 publications

(77 reference statements)

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“…Various commercial and noncommercial depth of anesthesia monitoring approaches have been developed (Kissin, 2000;Struys et al, 2002;Jordan et al, 2006;Ferenets et al, 2007;Liley et al, 2010;Shalbaf et al, 2013;Shoushtarian et al, 2015b,a) that primarily 5 rely on extraction of features from the EEG to track anesthetic brain state. Despite significant history and recent work attempting to characterise the mulit-channel EEG and brain networks related to anesthesia in more detail (Cimenser et al, 2011;Lewis et al, 2012;Purdon et al, 2013;Kuhlmann et al, 2013;Lee et al, 2013), the international uptake of automated depth of anesthesia monitoring in the clinic is still lagging.…”

Section: Introductionmentioning

confidence: 99%

“…Model-based approaches involving autoregressive moving average (ARMA) modelling have been shown to be able to disentangle the effects of simultaneously applied anesthetic and analgesic (Liley et al, 2010;Shoushtarian et al, 15 2015b,a). Therefore model-based approaches present an interesting alternative to the more standard extraction of EEG features employed in depth of anesthesia monitoring (Kissin, 2000;Jordan et al, 2006;Ferenets et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…ARMA models provide a generic model-based benchmark and the Broersen technique for ARMA models is the primary estimation technique underlying the Brain anesthesia Response (BAR) monitor (Cortical Dynamics, Australia) (Liley et al, 2008(Liley et al, , 2010Shoushtarian et al, 2015b,a) developed in part by one of the authors. On the other hand, the HFD, when evaluated with the same dataset as used in this study, has been demonstrated to perform as well as or better than, three entropy-based features, two commercial entropy-based 70 features, and three features used in the most common commercial depth of anesthesia monitoring device, the BIS monitor (Covidian, Ireland) (Ferenets et al, 2007).…”

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confidence: 99%

“…Here HFD was calculated on the 6-47 Hz frequency band for finite length epochs using the method outlined by Ferenets et al Ferenets et al (2007). The data analysed here has been used in previous studies on depth of anesthesia monitoring (Ferenets et al, 2007;Liley et al, 2010). Detailed information about the patient cohort and anesthetic protocols for this dataset can score, a subjective clinical measure of arousal, alertness and sedation (Chernik et al, 1990).…”

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confidence: 99%

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Neural mass model-based tracking of anesthetic brain states

et al. 2016

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Neural mass model-based tracking of brain states from electroencephalographic signals holds the promise of simultaneously tracking brain states while inferring underlying physiological changes in various neuroscientific and clinical applications. Here, neural mass model-based tracking of brain states using the unscented Kalman filter applied to estimate parameters of the Jansen-Rit cortical population model is evaluated through the application of propofol-based anesthetic state monitoring. In particular, 15 subjects underwent propofol anesthesia induction from awake to anesthetised while behavioural responsiveness was monitored and frontal electroencephalographic signals were recorded.The unscented Kalman filter Jansen-Rit model approach applied to frontal electroencephalography achieved reasonable testing performance for classification of the anesthetic brain state (sensitivity: 0.51; chance sensitivity: 0.17; nearest neighbor sensitivity 0.75) when compared to approaches based on linear (autoregressive moving average) modelling (sensitivity 0.58; nearest neighbor sensitivity: 0.91) and a high performing standard depth of anesthesia monitoring measure, Higuchi Fractal Dimension (sensitivity: 0.50; nearest neighbor sensitivity: 0.88). Moreover, it was found that the unscented Kalman filter based parameter estimates of the inhibitory postsynaptic potential amplitude varied in the physiologically expected direction with increases in propofol concentration, while the estimates of the inhibitory postsynaptic potential rate constant did not. These results combined with analysis of monotonicity of parameter estimates, error analysis of parameter estimates, and observability analysis of the Jansen-Rit model, along with considerations of extensions of the Jansen-Rit model, suggests that the Jansen-Rit model combined with unscented Kalman filtering provides a valuable benckmark for future real-time brain state tracking studies. This is especially true for studies of more complex, but still computationally efficient, neural models of anesthesia that can more accurately track the anesthetic brain state, while simultaneously inferring underlying physiological changes that can potentially provide useful clinical information.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Neural mass model-based tracking of anesthetic brain states

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“…Derived electroencephalogram measures: if we increase the level of Remifentanil the Cortical Input -a measure of the magnitude of cortical input -will significantly decrease. For quantifying the Propofol effect, one can use the Cortical State -a measure of the responsiveness of cortex -which is statistically independent of variations in the effect site Reminfentanil levels (Liley et al, 2010).…”

Section: Proposed Model For the Remifentanil Effectmentioning

confidence: 99%

Towards a Multivariable Model for Controlling the Depth of Anaesthesia using Propofol and Remifentanil

Ionescu

Nașcu

Keyser

2012

IFAC Proceedings Volumes

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In this paper is presented a brief state of art regarding the multivariable formulation for controlling the depth of anaesthesia by means of two intravenously administrated drugs, i.e. Propofol and Remifentanil. In a feasibility study of determining a suitable variable to quantify analgesia levels in patients undergoing cardiac surgery, the Bispectral index and an electromyogram-based surrogate variable are proposed as the controlled variables. The study is carried on in the context of implementing a multivariable predictive control algorithm. The simulation results show that such a paradigm is feasible, although it does not guarantee perfect knowledge of the analgesia level -in other words, the variable is not validated against typical evaluations of the pain levels (eg. clinical scores).

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Anesthesia, Neural Population Models of

Steyn-Ross

Sleigh

2022

Encyclopedia of Computational Neuroscience

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Propofol and Remifentanil Differentially Modulate Frontal Electroencephalographic Activity

Cited by 48 publications

References 66 publications

Neural mass model-based tracking of anesthetic brain states

Neural mass model-based tracking of anesthetic brain states

Towards a Multivariable Model for Controlling the Depth of Anaesthesia using Propofol and Remifentanil

Anesthesia, Neural Population Models of

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