Behavior of Entropy/Complexity Measures of the Electroencephalogram during Propofol-induced Sedation

Ferenets, Rain; Vanluchene, Ann; Lipping, Tarmo; Heyse, Björn; Struys, Michel

doi:10.1097/01.anes.0000264790.07231.2d

Cited by 66 publications

(73 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Several investigators have compared spectral entropy parameters with BIS using different anesthetic drugs during the period of induction of anesthesia [12][13][14], but their results were inconsistent. In the present study, the comparability of RE and SE, and BIS was investigated during the period of maintenance of the propfol-remifentanil anaesthesia, where the remifentanil concentration was kept constant, and fentanyl was added at expected painful periods.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Spectral Entropy and Bispectral Index Electroencephalography in Coronary Artery Bypass Graft Surgery

Baulig

Seifert

Schmid

et al. 2010

Journal of Cardiothoracic and Vascular Anesthesia

View full text Add to dashboard Cite

Objective: The study's aim was to compare Response (RE) and State Entropy (SE) with Bispectral Index (BIS) electroencephalography (EEG) as an alternative cerebral monitoring tool in patients scheduled for coronary artery bypass graft surgery.Design: Prospective, observational single-center study Setting: University HospitalParticipants: Thirty patients undergoing coronary artery bypass graft surgery receiving remifentanil-propofol anaesthesia.Interventions Surgery was performed with cardiopulmonary bypass (CPB) and cardiac arrest in fifteen patients, with CPB without cardiac arrest in nine patients and without CPB in six patients. Measurements and Main results RE, SE, BIS, burst suppression ratio (BSR) and frontalelectromyography (f-EMG) were detected simultaneously. RE and SE compared favorably to BIS and their correlations were strong (r 2 = 0.6, r 2 = 0.55, respectively). Mean bias of RE and BIS was -1.8, but limits of agreement were high (+20.5/-24.1). RE and SE tended to be lower than the BIS values in the CPB subgroups. Detection of BSR was similar with RE and SE, and the BIS. A strong correlation existed between BIS and f-EMG (r 2 = 0.62), in contrast to RE (r 2 = 0.45) and SE (r 2 =0.39). BIS monitoring was significantly more disturbed than RE and SE with 9.1 ±10.9% and 0.1±0.2% of the total anesthesia time, respectively. Neither implicit nor explicit memory was demonstrated.Conclusion RE and SE are comparable with the BIS but showed significantly less interference from f-EMG and superior resistance against artefacts. Thus, Spectral Entropy is more suitable than the BIS during propfol-remifentanil anesthesia in cardiac surgery patients. Words: 2482

show abstract

Section: Discussionmentioning

confidence: 99%

Comparison of Spectral Entropy and Bispectral Index Electroencephalography in Coronary Artery Bypass Graft Surgery

Baulig

Seifert

Schmid

et al. 2010

Journal of Cardiothoracic and Vascular Anesthesia

View full text Add to dashboard Cite

show abstract

“…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%

“…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%

See 1 more Smart Citation

Neural mass model-based tracking of anesthetic brain states

et al. 2016

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Researchers have demonstrated that LZC and ApEn can distinguish between awake and anaesthetized state of human subjects (Anderson, Barr, 2004, Bruhn, Bouillon, 2003, Ferenets et al , 2006, Ferenets et al , 2007, Hans, Dewandre, 2005, Kumar et al , 2007, Noh et al , 2006, Vakkuri, Yli-Hankala, 2004, Voss et al , 2006, Zhang, Roy, 2001). In sleep research ApEn was statistically significantly lower during Stage IV and higher during wake and REM sleep (Burioka et al , 2005, Papadelis et al , 2007.…”

Section: Clinical Application Of Nda Correlated With Unconsciousnessmentioning

confidence: 99%

Electroencephalography (EEG) and Unconsciousness

Wu¹,

Yuan²

2012

Advances in Clinical Neurophysiology

View full text Add to dashboard Cite

Behavior of Entropy/Complexity Measures of the Electroencephalogram during Propofol-induced Sedation

Abstract: Cutting off high frequencies from the electroencephalogram and increased remifentanil concentration deteriorate the performance of the electroencephalogram-based entropy/complexity measures as indicators of the depth of propofol sedation.

Cited by 66 publications

References 27 publications

Comparison of Spectral Entropy and Bispectral Index Electroencephalography in Coronary Artery Bypass Graft Surgery

Comparison of Spectral Entropy and Bispectral Index Electroencephalography in Coronary Artery Bypass Graft Surgery

Neural mass model-based tracking of anesthetic brain states

Electroencephalography (EEG) and Unconsciousness

Contact Info

Product

Resources

About