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Journal articles on the topic 'Burst-suppression'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Kranaster, Laura, Peter Plum, Carolin Hoyer, Alexander Sartorius, and Heiko Ullrich. "Burst Suppression." Journal of ECT 29, no. 1 (March 2013): 25–28. http://dx.doi.org/10.1097/yct.0b013e3182622c0e.

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2

Wennberg, Richard A. "Asynchronous burst suppression." Clinical Neurophysiology 111, no. 2 (February 2000): 367. http://dx.doi.org/10.1016/s1388-2457(99)00237-0.

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3

Cendes, F., E. Andermann, and F. Andermann. "Focal suppression-burst." Neurology 47, no. 2 (August 1, 1996): 613. http://dx.doi.org/10.1212/wnl.47.2.613.

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4

Urrego, Jose A., Stephen A. Greene, and Manuel J. Rojas. "Brain burst suppression activity." Psychology & Neuroscience 7, no. 4 (June 2014): 531–43. http://dx.doi.org/10.3922/j.psns.2014.4.12.

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5

Niedermeyer, Ernst. "The Burst-Suppression Electroencephalogram." American Journal of Electroneurodiagnostic Technology 49, no. 4 (December 2009): 333–41. http://dx.doi.org/10.1080/1086508x.2009.11079736.

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6

Niedermeyer, E., David L. Sherman, Romergryko J. Geocadin, H. Christian Hansen, and Daniel F. Hanley. "The Burst-Suppression Electroencephalogram." Clinical Electroencephalography 30, no. 3 (July 1999): 99–105. http://dx.doi.org/10.1177/155005949903000305.

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7

Litscher, G., and G. Schwarz. "Burst-Suppression-Erkennung beim pEEG - Detection of Burst Suppression in the pEEG." Biomedizinische Technik/Biomedical Engineering 42, no. 1-2 (1997): 12–15. http://dx.doi.org/10.1515/bmte.1997.42.1-2.12.

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8

Chemali, Jessica, ShiNung Ching, Patrick L. Purdon, Ken Solt, and Emery N. Brown. "Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression." Journal of Neural Engineering 10, no. 5 (September 10, 2013): 056017. http://dx.doi.org/10.1088/1741-2560/10/5/056017.

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9

Wendling, Woodrow W., Bonita M. Shapiro, Frank J. Ammaturo, Dong Chen, Peter S. Pham, Satoshi Furukawa, and Christer Carlsson. "ETOMIDATE FOR ELECTROENCEPHALOGRAPHIC BURST SUPPRESSION." Journal of Neurosurgical Anesthesiology 9, no. 4 (October 1997): 387. http://dx.doi.org/10.1097/00008506-199710000-00049.

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Zeiler, Frederick A., Eva Akoth, Lawrence M. Gillman, and Michael West. "Burst Suppression for ICP Control." Journal of Intensive Care Medicine 32, no. 2 (July 9, 2016): 130–39. http://dx.doi.org/10.1177/0885066615593939.

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Background: The goal of our study was to perform a systematic review of the literature to determine the effect that burst suppression has on intracranial pressure (ICP) control. Methods: All articles from MEDLINE, BIOSIS, EMBASE, Global Health, Scopus, Cochrane Library, the International Clinical Trials Registry Platform (inception to January 2015), reference lists of relevant articles, and gray literature were searched. The strength of evidence was adjudicated using both the Oxford and the Grading of Recommendation Assessment Development and Education (GRADE) methodology. Results: Seven articles were considered for review. A total of 108 patients were studied, all receiving burst suppression therapy. Two studies failed to document a decrease in ICP with burst suppression therapy. There were reports of severe hypotension and increased infection rates with barbiturate-based therapy. Etomidate-based suppressive therapy was linked to severe renal dysfunction. Conclusions: There currently exists both Oxford level 2b and GRADE C evidence to support that achieving burst suppression reduces ICP, and also has no effect on ICP, in severe traumatic brain injury. The literature suggests burst suppression therapy may be useful for ICP reduction in certain cases, although these situations are currently unclear. In addition, the impact on patient functional outcome is unclear. Further prospective study is warranted.

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11

Amzica, Florin. "Basic physiology of burst-suppression." Epilepsia 50 (December 2009): 38–39. http://dx.doi.org/10.1111/j.1528-1167.2009.02345.x.

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12

Pan, Jie, Amputch Karukote, and Eri Shoji. "Clinical approach to burst-suppression pattern in intensive care unit: basic and updates." Southwest Respiratory and Critical Care Chronicles 8, no. 36 (October 3, 2020): 61–65. http://dx.doi.org/10.12746/swrccc.v8i36.761.

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A burst-suppression pattern is an electroencephalographic pattern characterized by a quasi-periodic high amplitude “burst” alternating with periods of low or flatline “suppression.” Recognizing and understanding this pattern is helpful for clinical management in intensive care units. Pathological burst-suppression is commonly seen in post cardiac arrest comatose patients. It can also be induced by anesthetics or hypothermia. A burst-suppression pattern in anoxic brain injury generally predicts a poor prognosis; however, exceptions do occur. Inducing burst-suppression by general anesthetics can be used to abort super-refractory status epilepticus. This article will discuss this unique EEG pattern, including basic mechanisms, related clinical conditions, and recent research updates.

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13

Bruhn, Jörgen, Heiko Röpcke, Benno Rehberg, Thomas Bouillon, and Andreas Hoeft. "Electroencephalogram Approximate Entropy Correctly Classifies the Occurrence of Burst Suppression Pattern as Increasing Anesthetic Drug Effect." Anesthesiology 93, no. 4 (October 1, 2000): 981–85. http://dx.doi.org/10.1097/00000542-200010000-00018.

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Background Approximate entropy, a measure of signal complexity and regularity, quantifies electroencephalogram changes during anesthesia. With increasing doses of anesthetics, burst-suppression patterns occur. Because of the high-frequency bursts, spectrally based parameters such as median electroencephalogram frequency and spectral edge frequency 95 do not decrease, incorrectly suggesting lightening of anesthesia. The authors investigated whether the approximate entropy algorithm correctly classifies the occurrence of burst suppression as deepening of anesthesia. Methods Eleven female patients scheduled for elective major surgery were studied. After propofol induction, anesthesia was maintained with isoflurane only. Before surgery, the end-tidal isoflurane concentration was varied between 0.6 and 1.3 minimum alveolar concentration. The raw electroencephalogram was continuously recorded and sampled at 128 Hz. Approximate entropy, electroencephalogram median frequency, spectral edge frequency 95, burst-suppression ratio, and burst-compensated spectral edge frequency 95 were calculated offline from 8-s epochs. The relation between burst-suppression ratio and approximate entropy, electroencephalogram median frequency, spectral edge frequency 95, and burst-compensated spectral edge frequency 95 was analyzed using Pearson correlation coefficient. Results Higher isoflurane concentrations were associated with higher burst-suppression ratios. Electroencephalogram median frequency (r = 0.34) and spectral edge frequency 95 (r = 0.29) increased, approximate entropy (r = -0.94) and burst-compensated spectral edge frequency 95 (r = -0.88) decreased with increasing burst-suppression ratio. Conclusion Electroencephalogram approximate entropy, but not electroencephalogram median frequency or spectral edge frequency 95 without burst compensation, correctly classifies the occurrence of burst-suppression pattern as increasing anesthetic drug effect.

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Pilge, S., D. Jordan, M. Kreuzer, E. F. Kochs, and G. Schneider. "Burst suppression-MAC and burst suppression-CP 50 as measures of cerebral effects of anaesthetics." British Journal of Anaesthesia 112, no. 6 (June 2014): 1067–74. http://dx.doi.org/10.1093/bja/aeu016.

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Nita, DA, M. Moldovan, R. Sharma, S. Avramescu, H. Otsubo, and CD Hahn. "B.02 Burst-suppression EEG is reactive to photic stimulation in comatose children with acquired brain injury." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 43, S2 (June 2016): S9. http://dx.doi.org/10.1017/cjn.2016.61.

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Background: Burst-suppression is an electroencephalographic pattern observed during coma and reflects severe encephalopathy. We investigated the reactivity of burst-suppression to photic stimulation in children with acquired brain injury. Methods: Intensive care unit electroencephalographic monitoring recordings containing burst-suppression were obtained from 5 comatose children with acquired brain injury of various etiologies. Intermittent photic stimulation was performed at 1 Hz for 1 minute to assess reactivity. We quantified reactivity by measuring the change in the burst ratio (fraction of time in burst) following photic stimulation. Results: Photic stimulation evoked bursts in all patients, resulting in a transient increase in the burst ratio, while the mean heart rate remained unchanged. The regression slope of the change in burst ratio, referred to as the standardized burst ratio reactivity, correlated with subjects’ Glasgow Coma Scale scores. Conclusions: Reactivity of the burst-suppression pattern to photic stimulation occurs across diverse coma etiologies. Standardized burst ratio reactivity appears to reflect coma severity. Measurement of burst ratio reactivity may represent a simple bedside tool to monitor coma severity in critically ill children.

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Lee, Jaeyun, Woo-Jin Song, Hyang Woon Lee, and Hyun-Chool Shin. "Novel Burst Suppression Segmentation in the Joint Time-Frequency Domain for EEG in Treatment of Status Epilepticus." Computational and Mathematical Methods in Medicine 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/2684731.

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We developed a method to distinguish bursts and suppressions for EEG burst suppression from the treatments of status epilepticus, employing the joint time-frequency domain. We obtained the feature used in the proposed method from the joint use of the time and frequency domains, and we estimated the decision as to whether the measured EEG was a burst segment or suppression segment by the maximum likelihood estimation. We evaluated the performance of the proposed method in terms of its accordance with the visual scores and estimation of the burst suppression ratio. The accuracy was higher than the sole use of the time or frequency domains, as well as conventional methods conducted in the time domain. In addition, probabilistic modeling provided a more simplified optimization than conventional methods. Burst suppression quantification necessitated precise burst suppression segmentation with an easy optimization; therefore, the excellent discrimination and the easy optimization of burst suppression by the proposed method appear to be beneficial.

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17

Jung, Dahye, Sungwon Yang, Min Soo Lee, and Yoonki Lee. "Remifentanil Alleviates Propofol-Induced Burst Suppression without Affecting Bispectral Index in Female Patients: A Randomized Controlled Trial." Journal of Clinical Medicine 8, no. 8 (August 8, 2019): 1186. http://dx.doi.org/10.3390/jcm8081186.

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The bispectral index is affected by various factors, such as noxious stimuli and other drugs, such as muscle relaxants. The burst suppression ratio from bispectral index monitoring is correlated with electroencephalographic burst suppression, which is associated with deep anesthesia, metabolic disorders, and brain injury. We assessed patients undergoing total intravenous anesthesia and examined the effects of remifentanil on the bispectral index, burst suppression ratio, and hemodynamic changes immediately after loss of consciousness with propofol. Seventy American Society of Anesthesiologists physical status class I and II Korean female patients scheduled for general anesthesia were administered propofol with an effect-site concentration of 5 μg/mL, using a target-controlled infusion (TCI). After losing consciousness, patients received either saline or remifentanil at an effect-site concentration of 5 ng/mL for 10 min. During this period, we recorded the bispectral index values, including burst suppression ratio, blood pressure, and heart rate. With remifentanil infusion, burst suppression ratios were lower (p < 0.01) but bispectral values were not different. The burst suppression ratio was significantly different at 6, 7, 8, and 10 min after remifentanil infusion (p < 0.05). In female patients with propofol-induced unconsciousness, remifentanil alleviated the burst suppression ratio without affecting the bispectral value.

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Bassolé, Prisca-Rolande, Adjaratou Dieynabou Sow, Moustapha Ndiaye, Amadou Gallo Diop, and Mouhamadou Mansour Ndiaye. "Encéphalopathies épileptogènes précoces avec suppression burst." Revue Neurologique 174 (April 2018): S14. http://dx.doi.org/10.1016/j.neurol.2018.01.031.

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19

Amzica, Florin. "What does burst suppression really mean?" Epilepsy & Behavior 49 (August 2015): 234–37. http://dx.doi.org/10.1016/j.yebeh.2015.06.012.

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20

Jäntti, V., K. Eriksson, K. Hartikainen, and G. Baer. "Epileptic EEG Discharges During Burst Suppression*." Neuropediatrics 25, no. 05 (October 1994): 271–73. http://dx.doi.org/10.1055/s-2008-1073036.

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21

Afra, Pegah, Verena Clarissa Samara, Lilly Fagatele, and Bola Adamolekun. "A case of ictal burst-suppression." Epilepsy & Behavior Case Reports 11 (2019): 73–76. http://dx.doi.org/10.1016/j.ebcr.2018.11.005.

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22

Weissenborn, K., H. Wilkens, E. Hausmann, and P. H. Degen. "Burst suppression EEG with baclofen overdose." Clinical Neurology and Neurosurgery 93, no. 1 (January 1991): 77–80. http://dx.doi.org/10.1016/0303-8467(91)90015-h.

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23

Sperling, Michael R., W. Jann Brown, and Paul H. Crandall. "Focal burst-suppression induced by thiopental." Electroencephalography and Clinical Neurophysiology 63, no. 3 (March 1986): 203–8. http://dx.doi.org/10.1016/0013-4694(86)90086-6.

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24

Grigg-Damberger, Madeleine M., Steven B. Coker, Carey L. Halsey, and Craig L. Anderson. "Neonatal burst suppression: Its developmental significance." Pediatric Neurology 5, no. 2 (March 1989): 84–92. http://dx.doi.org/10.1016/0887-8994(89)90032-5.

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25

Ching, ShiNung, Max Y. Liberman, Jessica J. Chemali, M. Brandon Westover, Jonathan D. Kenny, Ken Solt, Patrick L. Purdon, and Emery N. Brown. "Real-time Closed-loop Control in a Rodent Model of Medically Induced Coma Using Burst Suppression." Anesthesiology 119, no. 4 (October 1, 2013): 848–60. http://dx.doi.org/10.1097/aln.0b013e31829d4ab4.

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Abstract Background: A medically induced coma is an anesthetic state of profound brain inactivation created to treat status epilepticus and to provide cerebral protection after traumatic brain injuries. The authors hypothesized that a closed-loop anesthetic delivery system could automatically and precisely control the electroencephalogram state of burst suppression and efficiently maintain a medically induced coma. Methods: In six rats, the authors implemented a closed-loop anesthetic delivery system for propofol consisting of: a computer-controlled pump infusion, a two-compartment pharmacokinetics model defining propofol’s electroencephalogram effects, the burst-suppression probability algorithm to compute in real time from the electroencephalogram the brain’s burst-suppression state, an online parameter-estimation procedure and a proportional-integral controller. In the control experiment each rat was randomly assigned to one of the six burst-suppression probability target trajectories constructed by permuting the burst-suppression probability levels of 0.4, 0.65, and 0.9 with linear transitions between levels. Results: In each animal the controller maintained approximately 60 min of tight, real-time control of burst suppression by tracking each burst-suppression probability target level for 15 min and two between-level transitions for 5–10 min. The posterior probability that the closed-loop anesthetic delivery system was reliable across all levels was 0.94 (95% CI, 0.77–1.00; n = 18) and that the system was accurate across all levels was 1.00 (95% CI, 0.84–1.00; n = 18). Conclusion: The findings of this study establish the feasibility of using a closed-loop anesthetic delivery systems to achieve in real time reliable and accurate control of burst suppression in rodents and suggest a paradigm to precisely control medically induced coma in patients.

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Liou, Jyun-You, Eliza Baird-Daniel, Mingrui Zhao, Andy Daniel, Catherine A. Schevon, Hongtao Ma, and Theodore H. Schwartz. "Burst suppression uncovers rapid widespread alterations in network excitability caused by an acute seizure focus." Brain 142, no. 10 (August 22, 2019): 3045–58. http://dx.doi.org/10.1093/brain/awz246.

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Abstract Burst suppression is an electroencephalogram pattern of globally symmetric alternating high amplitude activity and isoelectricity that can be induced by general anaesthetics. There is scattered evidence that burst suppression may become spatially non-uniform in the setting of underlying pathology. Here, we induced burst suppression with isoflurane in rodents and then created a neocortical acute seizure focus with injection of 4-aminopyridine (4-AP) in somatosensory cortex. Burst suppression events were recorded before and after creation of the focus using bihemispheric wide-field calcium imaging and multielectrode arrays. We find that the seizure focus elicits a rapid alteration in triggering, initiation, and propagation of burst suppression events. Compared with the non-seizing brain, bursts are triggered from the thalamus, initiate in regions uniquely outside the epileptic focus, elicit marked increases of multiunit activity and propagate towards the seizure focus. These findings support the rapid, widespread impact of focal epilepsy on the extended brain network.

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Spalletti, M., R. Carraia, M. Scarpino, C. Cossu, A. Ammannati, A. Peris, S. Valente, A. Grippo, and A. Amantini. "34. Burst-suppression with highly epileptiform bursts and with identical bursts: two subtypes within burst-suppression pattern." Clinical Neurophysiology 127, no. 12 (December 2016): e331. http://dx.doi.org/10.1016/j.clinph.2016.10.046.

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Beau, F. E. N. Le, and B. E. Alger. "Transient Suppression of GABAA-Receptor–Mediated IPSPs After Epileptiform Burst Discharges in CA1 Pyramidal Cells." Journal of Neurophysiology 79, no. 2 (February 1, 1998): 659–69. http://dx.doi.org/10.1152/jn.1998.79.2.659.

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Le Beau, F.E.N. and B. E. Alger. Transient suppression ofGABAA-receptor–mediated IPSPs after epileptiform burst discharges in CA1 pyramidal cells. J. Neurophysiol. 79: 659–669, 1998. Epileptiform burst discharges were elicited in CA1 hippocampal pyramidal cells in the slice preparation by perfusion with Mg2+-free saline. Intracellular recordings revealed paroxysmal depolarization shifts (PDSs) that either occurred spontaneously or were evoked by stimulation of Schaffer collaterals. These bursts involved activation of N-methyl-d-aspartate receptors because burst discharges were reduced or abolished by dl-2-amino-5-phosphonovaleric acid. Bath application of carbachol caused an increase in spontaneous activity that was predominantly due to γ-aminobutyric acid-A-receptor–mediated spontaneous inhibitory postsynaptic potentials (sIPSPs). A marked reduction in sIPSPs (31%) was observed after each epileptiform burst discharge, which subsequently recovered to preburst levels after ∼4–20 s. This sIPSP suppression was not associated with any change in postsynaptic membrane conductance. A suppression of sIPSPs also was seen after burst discharges evoked by brief (100–200 ms) depolarizing current pulses. N-ethylmaleimide, which blocks pertussis-toxin–sensitive G proteins, significantly reduced the suppression of sIPSPs seen after a burst response. When increases in intracellular Ca2+ were buffered by intracellular injection of ethylene glycol bis(β-aminoethyl)ether- N,N,N′,N′-tetraacetic acid, the sIPSP suppression seen after a single spontaneous or evoked burst discharge was abolished. Although we cannot exclude other Ca2+-dependent mechanisms, this suppression of sIPSPs shared many of the characteristics of depolarization-induced suppression of inhibition (DSI) in that it involved activation of G proteins and was dependent on increases in intracellular calcium. These findings suggest that a DSI-like process may be activated by the endogenous burst firing of CA1 pyramidal neurons.

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Lipping, T., P. Loula, V. Jäntti, and A. Yli-Hankala. "DC-level Detection of Burst-Suppression EEG." Methods of Information in Medicine 33, no. 01 (1994): 35–38. http://dx.doi.org/10.1055/s-0038-1634968.

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Abstract:The EEG signal is usually recorded with low time constant analog prefilters to avoid low frequency artefacts. During this kind of recording the frequency components below the cutoff frequency of the analog prefilter (usually below about 1 to 3 Hz) are lost. By visual examination of some experimental recordings taken with a higher time constant, it was noticed that during burst-suppression EEG the DC-level of the signal rises sharply when the burst begins and falls when the burst ends. Thus, a burst actually consists of a mixed frequency discharge on a pulse-like DC-shift. We developed a filter algorithm to estimate the change in the DC-level during bursts as accurately as possible.

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30

Pedemonte, Juan C., George S. Plummer, Shubham Chamadia, Joseph J. Locascio, Eunice Hahm, Breanna Ethridge, Jacob Gitlin, et al. "Electroencephalogram Burst-suppression during Cardiopulmonary Bypass in Elderly Patients Mediates Postoperative Delirium." Anesthesiology 133, no. 2 (April 22, 2020): 280–92. http://dx.doi.org/10.1097/aln.0000000000003328.

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Background Intraoperative burst-suppression is associated with postoperative delirium. Whether this association is causal remains unclear. Therefore, the authors investigated whether burst-suppression during cardiopulmonary bypass (CPB) mediates the effects of known delirium risk factors on postoperative delirium. Methods This was a retrospective cohort observational substudy of the Minimizing ICU [intensive care unit] Neurological Dysfunction with Dexmedetomidine-induced Sleep (MINDDS) trial. The authors analyzed data from patients more than 60 yr old undergoing cardiac surgery (n = 159). Univariate and multivariable regression analyses were performed to assess for associations and enable causal inference. Delirium risk factors were evaluated using the abbreviated Montreal Cognitive Assessment and Patient-Reported Outcomes Measurement Information System questionnaires for applied cognition, physical function, global health, sleep, and pain. The authors also analyzed electroencephalogram data (n = 141). Results The incidence of delirium in patients with CPB burst-suppression was 25% (15 of 60) compared with 6% (5 of 81) in patients without CPB burst-suppression. In univariate analyses, age (odds ratio, 1.08 [95% CI, 1.03 to 1.14]; P = 0.002), lowest CPB temperature (odds ratio, 0.79 [0.66 to 0.94]; P = 0.010), alpha power (odds ratio, 0.65 [0.54 to 0.80]; P < 0.001), and physical function (odds ratio, 0.95 [0.91 to 0.98]; P = 0.007) were associated with CPB burst-suppression. In separate univariate analyses, age (odds ratio, 1.09 [1.02 to 1.16]; P = 0.009), abbreviated Montreal Cognitive Assessment (odds ratio, 0.80 [0.66 to 0.97]; P = 0.024), alpha power (odds ratio, 0.75 [0.59 to 0.96]; P = 0.025), and CPB burst-suppression (odds ratio, 3.79 [1.5 to 9.6]; P = 0.005) were associated with delirium. However, only physical function (odds ratio, 0.96 [0.91 to 0.99]; P = 0.044), lowest CPB temperature (odds ratio, 0.73 [0.58 to 0.88]; P = 0.003), and electroencephalogram alpha power (odds ratio, 0.61 [0.47 to 0.76]; P < 0.001) were retained as predictors in the burst-suppression multivariable model. Burst-suppression (odds ratio, 4.1 [1.5 to 13.7]; P = 0.012) and age (odds ratio, 1.07 [0.99 to 1.15]; P = 0.090) were retained as predictors in the delirium multivariable model. Delirium was associated with decreased electroencephalogram power from 6.8 to 24.4 Hertz. Conclusions The inference from the present study is that CPB burst-suppression mediates the effects of physical function, lowest CPB temperature, and electroencephalogram alpha power on delirium. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

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Cinar, Nilgun, Sevki Sahin, Meral Bozdemir, Sibel Karsidag, and Selçuk Simsek. "Hanging-induced burst suppression pattern in EEG." Journal of Emergencies, Trauma, and Shock 5, no. 4 (2012): 347. http://dx.doi.org/10.4103/0974-2700.102408.

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32

An, D. S., D. Straumann, and H. G. Wieser. "‘One-way asynchrony’ of burst-suppression activity." Neurophysiologie Clinique/Clinical Neurophysiology 26, no. 5 (January 1996): 329–34. http://dx.doi.org/10.1016/s0987-7053(97)85100-3.

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Hartikainen, K., M. Rorarius, and V. Jäntti. "Reactivity of EEG burst suppression during anaesthesia." International Journal of Psychophysiology 25, no. 1 (January 1997): 28. http://dx.doi.org/10.1016/s0167-8760(97)85407-4.

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34

Hartikainen, K. M., M. Rorarius, J. J. Perakyla, P. J. Laippala, and V. Jantti. "Cortical Reactivity During Isoflurane Burst-Suppression Anesthesia." Anesthesia & Analgesia 81, no. 6 (December 1995): 1223–28. http://dx.doi.org/10.1097/00000539-199512000-00018.

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35

Pourmand, Rahman. "Burst-Suppression Pattern with Unusual Clinical Correlates." Clinical Electroencephalography 25, no. 4 (October 1994): 160–63. http://dx.doi.org/10.1177/155005949402500410.

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Jäntti, V., G. Baer, A. Yli-Hankala, M. Hämäläinen, and R. Hari. "MEG burst suppression in an anaesthetized dog." Acta Anaesthesiologica Scandinavica 39, no. 1 (January 1995): 126–28. http://dx.doi.org/10.1111/j.1399-6576.1995.tb05603.x.

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Labar, D. R., S. Hosain, and R. Fraser. "Reply from the Authors: Focal suppression-burst." Neurology 47, no. 2 (August 1, 1996): 613. http://dx.doi.org/10.1212/wnl.47.2.613-a.

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HARTIKAINEN, K. M., M. RORARIUS, J. J. PER??KYL??, P. J. LAIPPALA, and V. J??NTTI. "Cortical Reactivity During Isoflurane Burst-Suppression Anesthesia." Survey of Anesthesiology 41, no. 1 (February 1997): 56???57. http://dx.doi.org/10.1097/00132586-199702000-00048.

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Ching, S., P. L. Purdon, S. Vijayan, N. J. Kopell, and E. N. Brown. "A neurophysiological-metabolic model for burst suppression." Proceedings of the National Academy of Sciences 109, no. 8 (February 7, 2012): 3095–100. http://dx.doi.org/10.1073/pnas.1121461109.

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Beydoun, Ahmad, Catherine E. Yen, and Ivo Drury. "Variance of interburst intervals in burst suppression." Electroencephalography and Clinical Neurophysiology 79, no. 6 (December 1991): 435–39. http://dx.doi.org/10.1016/0013-4694(91)90162-w.

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Hartikainen, K. M., M. Rorarius, J. J. Perakyla, P. J. Laippala, and V. Jantti. "Cortical Reactivity During Isoflurane Burst-Suppression Anesthesia." Anesthesia & Analgesia 81, no. 6 (December 1995): 1223–28. http://dx.doi.org/10.1213/00000539-199512000-00018.

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Warner, David S., Seiji Takaoka, Bo Wu, Paula S. Ludwig, Robert D. Pearlstein, Ann D. Brinkhous, and Franklin MD Dexter. "Electroencephalographic Burst Suppression Is Not Required to Elicit Maximal Neuroprotection from Pentobarbital in a Rat Model of Focal Cerebral Ischemia." Anesthesiology 84, no. 6 (June 1, 1996): 1475–84. http://dx.doi.org/10.1097/00000542-199606000-00024.

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Background Barbiturates have previously been demonstrated to reduce focal cerebral ischemic brain damage. However, the dose of drug required to elicit maximal neuroprotection has not been defined. The authors' hypothesized that doses of pentobarbital substantially lower than those required to cause electroencephalographic burst suppression would result in maximal magnitudes of reduction of cerebral infarct volume. Methods Wistar rats underwent 90 min of filament occlusion of the middle cerebral artery while either awake (control), or anesthetized with intravenous sodium pentobarbital administered to preserve an active electroencephalogram (15-23 mg.kg-1.h-1) or a pattern of burst suppression (45-60 mg.kg-1.h-1; n = 17). During ischemia and for the first 6 h of recirculation, brain temperature was rigorously controlled at 38.0 +/- 0.2 degrees C. Rats were allowed a recovery interval of 7 days after which neurologic function and cerebral infarct volume were assessed. In nonischemic rats undergoing a similar anesthetic protocol, the cerebral metabolic rate of glucose utilization was measured at each anesthetic depth. Results Relevant physiologic values were similar between groups. Total infarct volume (mean +/- SD) was smaller in the active electroencephalogram group than in the control group (124 +/- 68 mm3 versus 163 +/- 66 mm3; P < 0.05). Increasing the dose of pentobarbital (burst suppression) did not further decrease infarct volume (128 +/- 54 mm3). Neurologic score and infarct volume were positively correlated (P < 0.001). Cerebral metabolic rate of glucose utilization was reduced by 56% in the burst suppression group versus 43% in the active electroencephalogram pentobarbital group (P < 0.001). Conclusions Sodium pentobarbital administered at either dose (active electroencephalogram or burst suppression) resulted in an approximately equal to 25% reduction of cerebral infarct size, indicating that burst suppression is not required to elicit maximal neuroprotective efficacy.

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Bauer, Reinhard, Bernd Walter, Frank Füchtner, Rainer Hinz, Hiroto Kuwabara, and Peter Brust. "Reduced regional CMRGluc suppression in newborn piglets during thiopental-induced burst suppression." Journal of Cerebral Blood Flow & Metabolism 25, no. 1_suppl (August 2005): S69. http://dx.doi.org/10.1038/sj.jcbfm.9591524.0069.

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Vanluchene, Ann L. G., Hugo Vereecke, Olivier Thas, Eric P. Mortier, Steven L. Shafer, and Michel M. R. F. Struys. "Spectral Entropy as an Electroencephalographic Measure of Anesthetic Drug Effect." Anesthesiology 101, no. 1 (July 1, 2004): 34–42. http://dx.doi.org/10.1097/00000542-200407000-00008.

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Background The authors compared the behavior of two calculations of electroencephalographic spectral entropy, state entropy (SE) and response entropy (RE), with the A-Line ARX Index (AAI) and the Bispectral Index (BIS) and as measures of anesthetic drug effect. They compared the measures for baseline variability, burst suppression, and prediction probability. They also developed pharmacodynamic models relating SE, RE, AAI, and BIS to the calculated propofol effect-site concentration (Ceprop). Methods With institutional review board approval, the authors studied 10 patients. All patients received 50 mg/min propofol until either burst suppression greater than 80% or mean arterial pressure less than 50 mmHg was observed. SE, RE, AAI, and BIS were continuously recorded. Ceprop was calculated from the propofol infusion profile. Baseline variability, prediction of burst suppression, prediction probability, and Spearman rank correlation were calculated for SE, RE, AAI, and BIS. The relations between Ceprop and the electroencephalographic measures of drug effect were estimated using nonlinear mixed effect modeling. Results Baseline variability was lowest when using SE and RE. Burst suppression was most accurately detected by spectral entropy. Prediction probability and individualized Spearman rank correlation were highest for BIS and lowest for SE. Nonlinear mixed effect modeling generated reasonable models relating all four measures to Ceprop. Conclusions Compared with BIS and AAI, both SE and RE seem to be useful electroencephalographic measures of anesthetic drug effect, with low baseline variability and accurate burst suppression prediction. The ability of the measures to predict Ceprop was best for BIS.

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Liang, Zhenhu, Yinghua Wang, Yongshao Ren, Duan Li, Logan Voss, Jamie Sleigh, and Xiaoli Li. "Detection of Burst Suppression Patterns in EEG Using Recurrence Rate." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/295070.

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Burst suppression is a unique electroencephalogram (EEG) pattern commonly seen in cases of severely reduced brain activity such as overdose of general anesthesia. It is important to detect burst suppression reliably during the administration of anesthetic or sedative agents, especially for cerebral-protective treatments in various neurosurgical diseases. This study investigates recurrent plot (RP) analysis for the detection of the burst suppression pattern (BSP) in EEG. The RP analysis is applied to EEG data containing BSPs collected from 14 patients. Firstly we obtain the best selection of parameters for RP analysis. Then, the recurrence rate (RR), determinism (DET), and entropy (ENTR) are calculated. Then RR was selected as the best BSP index one-way analysis of variance (ANOVA) and multiple comparison tests. Finally, the performance of RR analysis is compared with spectral analysis, bispectral analysis, approximate entropy, and the nonlinear energy operator (NLEO). ANOVA and multiple comparison tests showed that the RR could detect BSP and that it was superior to other measures with the highest sensitivity of suppression detection (96.49%, P=0.03). Tracking BSP patterns is essential for clinical monitoring in critically ill and anesthetized patients. The purposed RR may provide an effective burst suppression detector for developing new patient monitoring systems.

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Ma, Owen, Amy Z. Crepeau, Arindam Dutta, and Daniel W. Bliss. "Anticipating Postoperative Delirium During Burst Suppression Using Electroencephalography." IEEE Transactions on Biomedical Engineering 67, no. 9 (September 2020): 2659–68. http://dx.doi.org/10.1109/tbme.2020.2967693.

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Entholzner, E. K., B. Mackensen, F. H??nel, D. Droese, S. Hargasser, and E. Kochs. "ISOFLURANE CONCENTRATION FOR INDUCTION OF EEG BURST SUPPRESSION." Journal of Neurosurgical Anesthesiology 8, no. 4 (October 1996): 350. http://dx.doi.org/10.1097/00008506-199610000-00127.

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Hosain, S., L. P. Burton, R. Fraser, and D. Labar. "Focal suppression-burst on electrocorticography after temporal lobectomy." Neurology 45, no. 12 (December 1, 1995): 2276–78. http://dx.doi.org/10.1212/wnl.45.12.2276.

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Wang, Huiliang, Weile Zhang, and Yinghao Ge. "Burst-Interference Suppression Based on Space-Time Processing." IEEE Access 6 (2018): 2712–19. http://dx.doi.org/10.1109/access.2017.2784901.

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M, Udo, Illievic h, Wolfgang Petricek, Wolfgang Schramm, Martha Weindlmayr-Goettel, Thomas Czech, and Christian K. Spiss. "Electroencephalographic Burst Suppression by Propofol Infusion in Humans." Anesthesia & Analgesia 77, no. 1 (July 1993): 155???160. http://dx.doi.org/10.1213/00000539-199307000-00030.

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