Depth of anaesthesia monitoring: what’s available, what’s validated and what’s next?

Bruhn, Joergen; Myles, Paul S.; Sneyd, J. R.; Struys, Michel

doi:10.1093/bja/ael120

Cited by 277 publications

(211 citation statements)

References 78 publications

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“…Automated electroencephalography (EEG) based depth of anesthesia monitoring is a long-standing problem in the EEG literature (Bruhn et al, 2006;Voss & Sleigh, 2007;Palanca et al, 2009). 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.…”

Section: Introductionmentioning

confidence: 99%

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%

Neural mass model-based tracking of anesthetic brain states

et al. 2016

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“…The hypnotic effect can be assessed by indices derived from the EEG, a number of different methods are available as experimental or commercial devices [1][2][3][4][5][6] whereas the assessment of nociception is less accepted due to the complexity and interpretation of the measurement.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the qCON and qNOX indices for the assessment of unconsciousness level and noxious stimulation response during surgery

Melia¹,

Gabarron²,

Agustí

et al. 2016

J Clin Monit Comput

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Purpose. The objective of this work is to compare the behavior of two electroencephalogram (EEG) based indices after drug induction and during recovery of consciousness.Methods. Data was recorded from 140 patients scheduled for general anaesthesia with a combination of propofol and remifentanil. The qCON 2000 monitor (Quantium Medical, Barcelona, Spain) was used to calculate the qCON and qNOX. The fall times and the rise times were defined at the start and at the end of the surgery. Loss of response to verbal command and loss of eye-lash reflex were assessed during the transition from awake to anesthetized, defining the state of loss of consciousness (LOC). Movement as a response to laryngeal mask (LMA) insertion was interpreted as the response to the nociceptive stimuli. The patients were classified as movers or non-movers. The values of qCON and qNOX were statistically compared. Conclusion.The indices qCON and qNOX were able to detect differences between the times of actions of hypnotic and analgesic agents. The qCON showed faster decrease during induction while the qNOX showed a faster increase during recovery.

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“…Automatic FFT is applied during human surgeries and on a smaller scale during animal surgeries, where the raw EEG and its FFT are used to assess anaesthetic depth. Established anaesthesia monitors are used to assess depth of anaesthesia, but they differ in the algorithm used to analyse the EEG (Bruhn et al, 2006). To the authors' knowledge, only one of such monitors, namely the Index of Consciousness or IoC, has been used in a study concerning stunning in animals.…”

Section: Derivatives Of the Eegmentioning

confidence: 99%

Assessing unconsciousness in livestock at slaughter

Verhoeven¹

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Verhoeven, M.T.W. (2016). Assessing unconsciousness in livestock at slaughter. PhD thesis, Wageningen University, The Netherlands Assessing unconsciousness in livestock at different stages of the slaughter process is a legal requirement according to EU legislation. The assessment can be based on absence of readily observable indicators (behavioural indicators, physical signs and reflexes) or, under experimental conditions, by recording and subsequent assessment of brain activity as presented in an electroencephalogram (EEG). There is no consensus, however, to what extent different behavioural indicators, physical signs and reflexes accurately reflect unconsciousness. The aim of this thesis was to study the relationships between these readily observable indicators and EEG activity to provide information on 1. the exact point in time at which animals lose consciousness when subjected to different stunning and slaughter methods and 2. the validity of behavioural indicators, physical signs and reflexes used to assess unconsciousness at slaughter under commercial conditions. The results showed a large variation in time to loss of consciousness, based on EEG activity, both between stunning and slaughter procedures and amongst animals. Captive bolt stunned calves lost consciousness instantly following the stun. Loss of consciousness in pigs during CO 2 stunning varied from 21 to 61 s after start of the exposure. An increased CO 2 concentration decreased the time to loss of consciousness. Times to loss of consciousness in sheep slaughtered without stunning varied from 6 to 24 s. In cattle slaughtered without stunning, times to loss of consciousness varied from 14 s up to over two minutes. Following captive bolt stunning in calves, absence of reflexes indicated unconsciousness. When consciousness was lost gradually (e.g. CO 2 stunning and nonstunned slaughter) none of the readily observable indicators could identify the exact point in time at which animals lost consciousness. Absence of rhythmic breathing, corneal reflex-and eyelid reflex were valid indicators of unconsciousness following CO 2 stunning and non-stunned slaughter, but these indicators were quite conservative as they were initially absent long after EEG activity indicated unconsciousness. When presence of these indicators would require (re)stunning, many animals will have to be (re)stunned. The results also showed that under full commercial conditions, stunning effectiveness must be closely monitored by slaughter plant employees, since risks for stun failures are higher under commercial conditions compared to experimental conditions. CONTENTS

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Depth of anaesthesia monitoring: what’s available, what’s validated and what’s next?

Cited by 277 publications

References 78 publications

Neural mass model-based tracking of anesthetic brain states

Neural mass model-based tracking of anesthetic brain states

Comparison of the qCON and qNOX indices for the assessment of unconsciousness level and noxious stimulation response during surgery

Assessing unconsciousness in livestock at slaughter

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