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Emery N. Brown - One of the best experts on this subject based on the ideXlab platform.
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Low Frontal Alpha Power Is Associated With the Propensity for Burst Suppression: An Electroencephalogram Phenotype for a "Vulnerable Brain".
Anesthesia and analgesia, 2020Co-Authors: Yu Raymond Shao, Emery N. Brown, Pegah Kahali, Hao Deng, Timothy T. Houle, Christopher Colvin, Bradford C. Dickerson, Patrick L. PurdonAbstract:Background A number of recent studies have reported an association between intraoperative Burst Suppression and postoperative delirium. These studies suggest that anesthesia-induced Burst Suppression may be an indicator of underlying brain vulnerability. A prominent feature of electroencephalogram (EEG) under propofol and sevoflurane anesthesia is the frontal alpha oscillation. This frontal alpha oscillation is known to decline significantly during aging and is generated by prefrontal brain regions that are particularly prone to age-related neurodegeneration. Given that Burst Suppression and frontal alpha oscillations are both associated with brain vulnerability, we hypothesized that anesthesia-induced frontal alpha power could also be associated with Burst Suppression. Methods We analyzed EEG data from a previously reported cohort in which 155 patients received propofol (n = 60) or sevoflurane (n = 95) as the primary anesthetic. We computed the EEG spectrum during stable anesthetic maintenance and identified whether or not Burst Suppression occurred during the anesthetic. We characterized the relationship between Burst Suppression and alpha power using logistic regression. We proposed 5 different models consisting of different combinations of potential contributing factors associated with Burst Suppression: (1) a Base Model consisting of alpha power; (2) an Extended Mechanistic Model consisting of alpha power, age, and drug dosing information; (3) a Clinical Confounding Factors Model consisting of alpha power, hypotension, and other confounds; (4) a Simplified Model consisting only of alpha power and propofol bolus administration; and (5) a Full Model consisting of all of these variables to control for as much confounding as possible. Results All models show a consistent significant association between alpha power and Burst Suppression while adjusting for different sets of covariates, all with consistent effect size estimates. Using the Simplified Model, we found that for each decibel decrease in alpha power, the odds of experiencing Burst Suppression increased by 1.33-fold. Conclusions In this study, we show how a decrease in anesthesia-induced frontal alpha power is associated with an increased propensity for Burst Suppression, in a manner that captures individualized information above and beyond a patient's chronological age. Lower frontal alpha band power is strongly associated with higher propensity for Burst Suppression and, therefore, potentially higher risk of postoperative neurocognitive disorders. We hypothesize that low frontal alpha power and increased propensity for Burst Suppression together characterize a "vulnerable brain" phenotype under anesthesia that could be mechanistically linked to brain metabolism, cognition, and brain aging.
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a hidden semi markov model for estimating Burst Suppression eeg
International Conference of the IEEE Engineering in Medicine and Biology Society, 2019Co-Authors: Sourish Chakravarty, Taylor E Baum, Pegah Kahali, Emery N. BrownAbstract:Burst Suppression is an electroencephalogram (EEG) pattern associated with profoundly inactivated brain states characterized by cerebral metabolic depression. This pattern is distinguished by short-duration band-limited electrical activity (Bursts) interspersed between relatively near-isoelectric periods (Suppressions). Prior work in neurophysiology suggests that Burst and Suppression segments are respectively associated with consumption and regeneration of adenosine triphosphate resource in cortical networks. This indicates that once a Suppression (or, Burst) segment begins, the propensity to switch out of the state gradually increases with duration spent in the state. Prior EEG monitoring frameworks that track the brain state during Burst Suppression by tracking the estimated fraction of time spent in Suppression, relative to Bursts, do not incorporate this information. In this work, we incorporate this information within a hidden semi-Markov model (HSMM) wherein two states (Burst & Suppression) stochastically switch between each other using sojourn-time dependent transition probabilities. We demonstrate the HSMM’s utility in analyzing clinical data by estimating the state probabilities, the optimal state sequence, and the brain’s metabolic activation level characterized by parameters governing sojourn-time dependence in transition probabilities. The HSMM-based approach proposed here provides a novel statistical framework that advances the state-of-the-art in analyzing Burst Suppression EEG.
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EMBC - A hidden semi-Markov model for estimating Burst Suppression EEG
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Inte, 2019Co-Authors: Sourish Chakravarty, Taylor E Baum, Pegah Kahali, Emery N. BrownAbstract:Burst Suppression is an electroencephalogram (EEG) pattern associated with profoundly inactivated brain states characterized by cerebral metabolic depression. This pattern is distinguished by short-duration band-limited electrical activity (Bursts) interspersed between relatively near-isoelectric periods (Suppressions). Prior work in neurophysiology suggests that Burst and Suppression segments are respectively associated with consumption and regeneration of adenosine triphosphate resource in cortical networks. This indicates that once a Suppression (or, Burst) segment begins, the propensity to switch out of the state gradually increases with duration spent in the state. Prior EEG monitoring frameworks that track the brain state during Burst Suppression by tracking the estimated fraction of time spent in Suppression, relative to Bursts, do not incorporate this information. In this work, we incorporate this information within a hidden semi-Markov model (HSMM) wherein two states (Burst & Suppression) stochastically switch between each other using sojourn-time dependent transition probabilities. We demonstrate the HSMM’s utility in analyzing clinical data by estimating the state probabilities, the optimal state sequence, and the brain’s metabolic activation level characterized by parameters governing sojourn-time dependence in transition probabilities. The HSMM-based approach proposed here provides a novel statistical framework that advances the state-of-the-art in analyzing Burst Suppression EEG.
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Robust control of Burst Suppression for medical coma.
Journal of neural engineering, 2015Co-Authors: M. Brandon Westover, Shinung Ching, Patrick L. Purdon, Seong-eun Kim, Emery N. BrownAbstract:Objective. Medical coma is an anesthetic-induced state of brain inactivation, manifest in the electroencephalogram by Burst Suppression. Feedback control can be used to regulate Burst Suppression, however, previous designs have not been robust. Robust control design is critical under real-world operating conditions, subject to substantial pharmacokinetic and pharmacodynamic parameter uncertainty and unpredictable external disturbances. We sought to develop a robust closed-loop anesthesia delivery (CLAD) system to control medical coma. Approach. We developed a robust CLAD system to control the Burst Suppression probability (BSP). We developed a novel BSP tracking algorithm based on realistic models of propofol pharmacokinetics and pharmacodynamics. We also developed a practical method for estimating patient-specific pharmacodynamics parameters. Finally, we synthesized a robust proportional integral controller. Using a factorial design spanning patient age, mass, height, and gender, we tested whether the system performed within clinically acceptable limits. Throughout all experiments we subjected the system to disturbances, simulating treatment of refractory status epilepticus in a real-world intensive care unit environment. Main results. In 5400 simulations, CLAD behavior remained within specifications. Transient behavior after a step in target BSP from 0.2 to 0.8 exhibited a rise time (the median (min, max)) of 1.4 [1.1, 1.9] min; settling time, 7.8 [4.2, 9.0] min; and percent overshoot of 9.6 [2.3, 10.8]%. Under steady state conditions the CLAD system exhibited a median error of 0.1 [�0.5, 0.9]%; inaccuracy of 1.8 [0.9, 3.4]%; oscillation index of 1.8 [0.9, 3.4]%; and maximum instantaneous propofol dose of 4.3 [2.1, 10.5] mg kg �1 . The maximum hourly propofol dose was 4.3 [2.1, 10.3] mg kg �1 h �1 . Performance fell within clinically acceptable limits for all measures. Significance. A CLAD system designed using robust control theory achieves clinically acceptable performance in the presence of realistic unmodeled disturbances and in spite of realistic model uncertainty, while maintaining infusion rates within acceptable safety limits.
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The human Burst Suppression electroencephalogram of deep hypothermia
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2015Co-Authors: M. Brandon Westover, Shinung Ching, Emery N. Brown, Sydney S Cash, Oluwaseun Akeju, Vishakhadatta M. Kumaraswamy, Eric T. Pierce, Ronan Kilbride, Patrick L. PurdonAbstract:Abstract Objective Deep hypothermia induces ‘Burst Suppression’ (BS), an electroencephalogram pattern with low-voltage ‘Suppressions’ alternating with high-voltage ‘Bursts’. Current understanding of BS comes mainly from anesthesia studies, while hypothermia-induced BS has received little study. We set out to investigate the electroencephalogram changes induced by cooling the human brain through increasing depths of BS through isoelectricity. Methods We recorded scalp electroencephalograms from eleven patients undergoing deep hypothermia during cardiac surgery with complete circulatory arrest, and analyzed these using methods of spectral analysis. Results Within patients, the depth of BS systematically depends on the depth of hypothermia, though responses vary between patients except at temperature extremes. With decreasing temperature, Burst lengths increase, and Burst amplitudes and lengths decrease, while the spectral content of Bursts remains constant. Conclusions These findings support an existing theoretical model in which the common mechanism of Burst Suppression across diverse etiologies is the cyclical diffuse depletion of metabolic resources, and suggest the new hypothesis of local micro-network dropout to explain decreasing Burst amplitudes at lower temperatures. Significance These results pave the way for accurate noninvasive tracking of brain metabolic state during surgical procedures under deep hypothermia, and suggest new testable predictions about the network mechanisms underlying Burst Suppression.
Shinung Ching - One of the best experts on this subject based on the ideXlab platform.
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Robust control of Burst Suppression for medical coma.
Journal of neural engineering, 2015Co-Authors: M. Brandon Westover, Shinung Ching, Patrick L. Purdon, Seong-eun Kim, Emery N. BrownAbstract:Objective. Medical coma is an anesthetic-induced state of brain inactivation, manifest in the electroencephalogram by Burst Suppression. Feedback control can be used to regulate Burst Suppression, however, previous designs have not been robust. Robust control design is critical under real-world operating conditions, subject to substantial pharmacokinetic and pharmacodynamic parameter uncertainty and unpredictable external disturbances. We sought to develop a robust closed-loop anesthesia delivery (CLAD) system to control medical coma. Approach. We developed a robust CLAD system to control the Burst Suppression probability (BSP). We developed a novel BSP tracking algorithm based on realistic models of propofol pharmacokinetics and pharmacodynamics. We also developed a practical method for estimating patient-specific pharmacodynamics parameters. Finally, we synthesized a robust proportional integral controller. Using a factorial design spanning patient age, mass, height, and gender, we tested whether the system performed within clinically acceptable limits. Throughout all experiments we subjected the system to disturbances, simulating treatment of refractory status epilepticus in a real-world intensive care unit environment. Main results. In 5400 simulations, CLAD behavior remained within specifications. Transient behavior after a step in target BSP from 0.2 to 0.8 exhibited a rise time (the median (min, max)) of 1.4 [1.1, 1.9] min; settling time, 7.8 [4.2, 9.0] min; and percent overshoot of 9.6 [2.3, 10.8]%. Under steady state conditions the CLAD system exhibited a median error of 0.1 [�0.5, 0.9]%; inaccuracy of 1.8 [0.9, 3.4]%; oscillation index of 1.8 [0.9, 3.4]%; and maximum instantaneous propofol dose of 4.3 [2.1, 10.5] mg kg �1 . The maximum hourly propofol dose was 4.3 [2.1, 10.3] mg kg �1 h �1 . Performance fell within clinically acceptable limits for all measures. Significance. A CLAD system designed using robust control theory achieves clinically acceptable performance in the presence of realistic unmodeled disturbances and in spite of realistic model uncertainty, while maintaining infusion rates within acceptable safety limits.
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The human Burst Suppression electroencephalogram of deep hypothermia
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2015Co-Authors: M. Brandon Westover, Shinung Ching, Emery N. Brown, Sydney S Cash, Oluwaseun Akeju, Vishakhadatta M. Kumaraswamy, Eric T. Pierce, Ronan Kilbride, Patrick L. PurdonAbstract:Abstract Objective Deep hypothermia induces ‘Burst Suppression’ (BS), an electroencephalogram pattern with low-voltage ‘Suppressions’ alternating with high-voltage ‘Bursts’. Current understanding of BS comes mainly from anesthesia studies, while hypothermia-induced BS has received little study. We set out to investigate the electroencephalogram changes induced by cooling the human brain through increasing depths of BS through isoelectricity. Methods We recorded scalp electroencephalograms from eleven patients undergoing deep hypothermia during cardiac surgery with complete circulatory arrest, and analyzed these using methods of spectral analysis. Results Within patients, the depth of BS systematically depends on the depth of hypothermia, though responses vary between patients except at temperature extremes. With decreasing temperature, Burst lengths increase, and Burst amplitudes and lengths decrease, while the spectral content of Bursts remains constant. Conclusions These findings support an existing theoretical model in which the common mechanism of Burst Suppression across diverse etiologies is the cyclical diffuse depletion of metabolic resources, and suggest the new hypothesis of local micro-network dropout to explain decreasing Burst amplitudes at lower temperatures. Significance These results pave the way for accurate noninvasive tracking of brain metabolic state during surgical procedures under deep hypothermia, and suggest new testable predictions about the network mechanisms underlying Burst Suppression.
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Propofol and sevoflurane induce distinct Burst Suppression patterns in rats
Frontiers in systems neuroscience, 2014Co-Authors: Jonathan D. Kenny, Shinung Ching, Emery N. Brown, M. Brandon Westover, Ken SoltAbstract:Burst Suppression is an EEG pattern characterized by alternating periods of high-amplitude activity (Bursts) and relatively low amplitude activity (Suppressions). Burst Suppression can arise from several different pathological conditions, as well as from general anesthesia. Here we review current algorithms that are used to quantify Burst Suppression, its various etiologies, and possible underlying mechanisms. We then review clinical applications of anesthetic-induced Burst Suppression. Finally, we report the results of our new study showing clear electrophysiological differences in Burst Suppression patterns induced by two common general anesthetics, sevoflurane and propofol. Our data suggest that the circuit mechanisms that generate Burst Suppression activity may differ among general anesthetics.
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EMBC - A mean field model for neural-metabolic homeostatic coupling in Burst Suppression.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Inte, 2014Co-Authors: Sensen Liu, Shinung ChingAbstract:Burst Suppression is an inactivated brain state in which the electroencephalogram is characterized by intermittent periods of isoelectric quiescence. Recent modeling studies have suggested an important role for brain metabolic processes in governing the very slow time scales that underlie the duration of Bursts and Suppressions. In these models, a reduction in metabolism leads to substrate depletion and consequent Suppression of action potential firing. Such a mechanism accounts for the appearance of Burst Suppression when metabolism is directly down-regulated. However, in many cases such as general anesthesia, metabolic down-regulation occurs in part as a homeostatic consequence of reduced neuronal activity. Here, we develop a mean-field model for neuronal activity with metabolic homeostatic mechanisms. We show that with such mechanisms, a simple reduction in neuronal activity due, for example, to increased neuronal inhibition, will give rise to bistability due to a bifurcation in the combined neuronal and metabolic dynamics. The model reconciles a purely metabolic mechanism for Burst Suppression with one that includes important dynamical feedback from the neuronal activity itself. The resulting fast-slow dynamical description forms a useful model for further development of novel methods for managing Burst Suppression clinically.
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Burst Suppression probability algorithms: state-space methods for tracking EEG Burst Suppression
Journal of neural engineering, 2013Co-Authors: Jessica J. Chemali, Shinung Ching, Patrick L. Purdon, Ken Solt, Emery N. BrownAbstract:Objective. Burst Suppression is an electroencephalogram pattern in which Bursts of electrical activity alternate with an isoelectric state. This pattern is commonly seen in states of severely reduced brain activity such as profound general anesthesia, anoxic brain injuries, hypothermia and certain developmental disorders. Devising accurate, reliable ways to quantify Burst Suppression is an important clinical and research problem. Although thresholding and segmentation algorithms readily identify Burst Suppression periods, analysis algorithms require long intervals of data to characterize Burst Suppression at a given time and provide no framework for statistical inference. Approach. We introduce the concept of the Burst Suppression probability (BSP) to define the brain’s instantaneous propensity of being in the suppressed state. To conduct dynamic analyses of Burst Suppression we propose a state-space model in which the observation process is a binomial model and the state equation is a Gaussian random walk. We estimate the model using an approximate expectation maximization algorithm and illustrate its application in the analysis of rodent Burst Suppression recordings under general anesthesia and a patient during induction of controlled hypothermia. Main result. The BSP algorithms track Burst Suppression on a second-to-second time scale, and make possible formal statistical comparisons of Burst Suppression at different times. Significance. The state-space approach suggests a principled and informative way to analyze Burst Suppression that can be used to monitor, and eventually to control, the brain states of patients in the operating room and in the intensive care unit. (Some figures may appear in colour only in the online journal)
Patrick L. Purdon - One of the best experts on this subject based on the ideXlab platform.
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Low Frontal Alpha Power Is Associated With the Propensity for Burst Suppression: An Electroencephalogram Phenotype for a "Vulnerable Brain".
Anesthesia and analgesia, 2020Co-Authors: Yu Raymond Shao, Emery N. Brown, Pegah Kahali, Hao Deng, Timothy T. Houle, Christopher Colvin, Bradford C. Dickerson, Patrick L. PurdonAbstract:Background A number of recent studies have reported an association between intraoperative Burst Suppression and postoperative delirium. These studies suggest that anesthesia-induced Burst Suppression may be an indicator of underlying brain vulnerability. A prominent feature of electroencephalogram (EEG) under propofol and sevoflurane anesthesia is the frontal alpha oscillation. This frontal alpha oscillation is known to decline significantly during aging and is generated by prefrontal brain regions that are particularly prone to age-related neurodegeneration. Given that Burst Suppression and frontal alpha oscillations are both associated with brain vulnerability, we hypothesized that anesthesia-induced frontal alpha power could also be associated with Burst Suppression. Methods We analyzed EEG data from a previously reported cohort in which 155 patients received propofol (n = 60) or sevoflurane (n = 95) as the primary anesthetic. We computed the EEG spectrum during stable anesthetic maintenance and identified whether or not Burst Suppression occurred during the anesthetic. We characterized the relationship between Burst Suppression and alpha power using logistic regression. We proposed 5 different models consisting of different combinations of potential contributing factors associated with Burst Suppression: (1) a Base Model consisting of alpha power; (2) an Extended Mechanistic Model consisting of alpha power, age, and drug dosing information; (3) a Clinical Confounding Factors Model consisting of alpha power, hypotension, and other confounds; (4) a Simplified Model consisting only of alpha power and propofol bolus administration; and (5) a Full Model consisting of all of these variables to control for as much confounding as possible. Results All models show a consistent significant association between alpha power and Burst Suppression while adjusting for different sets of covariates, all with consistent effect size estimates. Using the Simplified Model, we found that for each decibel decrease in alpha power, the odds of experiencing Burst Suppression increased by 1.33-fold. Conclusions In this study, we show how a decrease in anesthesia-induced frontal alpha power is associated with an increased propensity for Burst Suppression, in a manner that captures individualized information above and beyond a patient's chronological age. Lower frontal alpha band power is strongly associated with higher propensity for Burst Suppression and, therefore, potentially higher risk of postoperative neurocognitive disorders. We hypothesize that low frontal alpha power and increased propensity for Burst Suppression together characterize a "vulnerable brain" phenotype under anesthesia that could be mechanistically linked to brain metabolism, cognition, and brain aging.
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Robust control of Burst Suppression for medical coma.
Journal of neural engineering, 2015Co-Authors: M. Brandon Westover, Shinung Ching, Patrick L. Purdon, Seong-eun Kim, Emery N. BrownAbstract:Objective. Medical coma is an anesthetic-induced state of brain inactivation, manifest in the electroencephalogram by Burst Suppression. Feedback control can be used to regulate Burst Suppression, however, previous designs have not been robust. Robust control design is critical under real-world operating conditions, subject to substantial pharmacokinetic and pharmacodynamic parameter uncertainty and unpredictable external disturbances. We sought to develop a robust closed-loop anesthesia delivery (CLAD) system to control medical coma. Approach. We developed a robust CLAD system to control the Burst Suppression probability (BSP). We developed a novel BSP tracking algorithm based on realistic models of propofol pharmacokinetics and pharmacodynamics. We also developed a practical method for estimating patient-specific pharmacodynamics parameters. Finally, we synthesized a robust proportional integral controller. Using a factorial design spanning patient age, mass, height, and gender, we tested whether the system performed within clinically acceptable limits. Throughout all experiments we subjected the system to disturbances, simulating treatment of refractory status epilepticus in a real-world intensive care unit environment. Main results. In 5400 simulations, CLAD behavior remained within specifications. Transient behavior after a step in target BSP from 0.2 to 0.8 exhibited a rise time (the median (min, max)) of 1.4 [1.1, 1.9] min; settling time, 7.8 [4.2, 9.0] min; and percent overshoot of 9.6 [2.3, 10.8]%. Under steady state conditions the CLAD system exhibited a median error of 0.1 [�0.5, 0.9]%; inaccuracy of 1.8 [0.9, 3.4]%; oscillation index of 1.8 [0.9, 3.4]%; and maximum instantaneous propofol dose of 4.3 [2.1, 10.5] mg kg �1 . The maximum hourly propofol dose was 4.3 [2.1, 10.3] mg kg �1 h �1 . Performance fell within clinically acceptable limits for all measures. Significance. A CLAD system designed using robust control theory achieves clinically acceptable performance in the presence of realistic unmodeled disturbances and in spite of realistic model uncertainty, while maintaining infusion rates within acceptable safety limits.
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The human Burst Suppression electroencephalogram of deep hypothermia
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2015Co-Authors: M. Brandon Westover, Shinung Ching, Emery N. Brown, Sydney S Cash, Oluwaseun Akeju, Vishakhadatta M. Kumaraswamy, Eric T. Pierce, Ronan Kilbride, Patrick L. PurdonAbstract:Abstract Objective Deep hypothermia induces ‘Burst Suppression’ (BS), an electroencephalogram pattern with low-voltage ‘Suppressions’ alternating with high-voltage ‘Bursts’. Current understanding of BS comes mainly from anesthesia studies, while hypothermia-induced BS has received little study. We set out to investigate the electroencephalogram changes induced by cooling the human brain through increasing depths of BS through isoelectricity. Methods We recorded scalp electroencephalograms from eleven patients undergoing deep hypothermia during cardiac surgery with complete circulatory arrest, and analyzed these using methods of spectral analysis. Results Within patients, the depth of BS systematically depends on the depth of hypothermia, though responses vary between patients except at temperature extremes. With decreasing temperature, Burst lengths increase, and Burst amplitudes and lengths decrease, while the spectral content of Bursts remains constant. Conclusions These findings support an existing theoretical model in which the common mechanism of Burst Suppression across diverse etiologies is the cyclical diffuse depletion of metabolic resources, and suggest the new hypothesis of local micro-network dropout to explain decreasing Burst amplitudes at lower temperatures. Significance These results pave the way for accurate noninvasive tracking of brain metabolic state during surgical procedures under deep hypothermia, and suggest new testable predictions about the network mechanisms underlying Burst Suppression.
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EMBC - Spatial variation in automated Burst Suppression detection in pharmacologically induced coma
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Inte, 2015Co-Authors: Durga Jonnalagadda, Patrick L. Purdon, Emery N. Brown, Valdery Moura, M. Brandon WestoverAbstract:Burst Suppression is actively studied as a control signal to guide anesthetic dosing in patients undergoing medically induced coma. The ability to automatically identify periods of EEG Suppression and compactly summarize the depth of coma using the Burst Suppression probability (BSP) is crucial to effective and safe monitoring and control of medical coma. Current literature however does not explicitly account for the potential variation in Burst Suppression parameters across different scalp locations. In this study we analyzed standard 19-channel EEG recordings from 8 patients with refractory status epilepticus who underwent pharmacologically induced Burst Suppression as medical treatment for refractory seizures. We found that although Burst Suppression is generally considered a global phenomenon, BSP obtained using a previously validated algorithm varies systematically across different channels. A global representation of information from individual channels is proposed that takes into account the Burst Suppression characteristics recorded at multiple electrodes. BSP computed from this representative Burst Suppression pattern may be more resilient to noise and a better representation of the brain state of patients. Multichannel data integration may enhance the reliability of estimates of the depth of medical coma.
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spatial variation in automated Burst Suppression detection in pharmacologically induced coma
International Conference of the IEEE Engineering in Medicine and Biology Society, 2015Co-Authors: Durga Jonnalagadda, Patrick L. Purdon, Emery N. Brown, Valdery Moura, Brandon M WestoverAbstract:Burst Suppression is actively studied as a control signal to guide anesthetic dosing in patients undergoing medically induced coma. The ability to automatically identify periods of EEG Suppression and compactly summarize the depth of coma using the Burst Suppression probability (BSP) is crucial to effective and safe monitoring and control of medical coma. Current literature however does not explicitly account for the potential variation in Burst Suppression parameters across different scalp locations. In this study we analyzed standard 19-channel EEG recordings from 8 patients with refractory status epilepticus who underwent pharmacologically induced Burst Suppression as medical treatment for refractory seizures. We found that although Burst Suppression is generally considered a global phenomenon, BSP obtained using a previously validated algorithm varies systematically across different channels. A global representation of information from individual channels is proposed that takes into account the Burst Suppression characteristics recorded at multiple electrodes. BSP computed from this representative Burst Suppression pattern may be more resilient to noise and a better representation of the brain state of patients. Multichannel data integration may enhance the reliability of estimates of the depth of medical coma.
Ken Solt - One of the best experts on this subject based on the ideXlab platform.
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Electroencephalogram dynamics during general anesthesia predict the later incidence and duration of Burst-Suppression during cardiopulmonary bypass.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2018Co-Authors: George S. Plummer, Ken Solt, Eunice Hahm, Jacob Gitlin, Reine Ibala, Hao Deng, Kenneth Shelton, Oluwaseun AkejuAbstract:Abstract Objective Electroencephalogram Burst-Suppression during general anesthesia is associated with post-operative delirium (POD). Whether Burst-Suppression causes POD or merely reflects susceptibility to POD is unclear. We hypothesized decreased intraoperative alpha (8–12 Hz) and beta (13–33 Hz) power prior to the occurrence of Burst-Suppression in susceptible patients. Methods We analyzed intraoperative electroencephalogram data of cardiac surgical patients undergoing cardiopulmonary bypass (CPB). We detected the incidence and duration of CPB Burst-Suppression with an automated Burst-Suppression detection algorithm. We analyzed EEG data with multitaper spectral estimation methods. We assessed associations between patient characteristics and Burst-Suppression using Binomial and Zero-inflated Poisson Regression Models. Results We found significantly decreased alpha and beta power (7.8–22.95 Hz) in the CPB Burst-Suppression cohort. The odds ratio for the association between point estimates for alpha and beta power (7.8–22.95 Hz) and the incidence of Burst-Suppression was 0.88 (95% CI: 0.79–0.98). The incidence rate ratio for the association between point estimates for power between the alpha and beta range and the duration of Burst-Suppression was 0.89 (95% CI: 0.84–0.93). Conclusion Decreased intra-operative power within the alpha and beta range was associated with susceptibility to Burst-Suppression during CPB. Significance This dynamic may be used to develop principled neurophysiological-based approaches to aid the preemptive identification and targeted care of POD vulnerable patients.
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Propofol and sevoflurane induce distinct Burst Suppression patterns in rats
Frontiers in systems neuroscience, 2014Co-Authors: Jonathan D. Kenny, Shinung Ching, Emery N. Brown, M. Brandon Westover, Ken SoltAbstract:Burst Suppression is an EEG pattern characterized by alternating periods of high-amplitude activity (Bursts) and relatively low amplitude activity (Suppressions). Burst Suppression can arise from several different pathological conditions, as well as from general anesthesia. Here we review current algorithms that are used to quantify Burst Suppression, its various etiologies, and possible underlying mechanisms. We then review clinical applications of anesthetic-induced Burst Suppression. Finally, we report the results of our new study showing clear electrophysiological differences in Burst Suppression patterns induced by two common general anesthetics, sevoflurane and propofol. Our data suggest that the circuit mechanisms that generate Burst Suppression activity may differ among general anesthetics.
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Burst Suppression probability algorithms: state-space methods for tracking EEG Burst Suppression
Journal of neural engineering, 2013Co-Authors: Jessica J. Chemali, Shinung Ching, Patrick L. Purdon, Ken Solt, Emery N. BrownAbstract:Objective. Burst Suppression is an electroencephalogram pattern in which Bursts of electrical activity alternate with an isoelectric state. This pattern is commonly seen in states of severely reduced brain activity such as profound general anesthesia, anoxic brain injuries, hypothermia and certain developmental disorders. Devising accurate, reliable ways to quantify Burst Suppression is an important clinical and research problem. Although thresholding and segmentation algorithms readily identify Burst Suppression periods, analysis algorithms require long intervals of data to characterize Burst Suppression at a given time and provide no framework for statistical inference. Approach. We introduce the concept of the Burst Suppression probability (BSP) to define the brain’s instantaneous propensity of being in the suppressed state. To conduct dynamic analyses of Burst Suppression we propose a state-space model in which the observation process is a binomial model and the state equation is a Gaussian random walk. We estimate the model using an approximate expectation maximization algorithm and illustrate its application in the analysis of rodent Burst Suppression recordings under general anesthesia and a patient during induction of controlled hypothermia. Main result. The BSP algorithms track Burst Suppression on a second-to-second time scale, and make possible formal statistical comparisons of Burst Suppression at different times. Significance. The state-space approach suggests a principled and informative way to analyze Burst Suppression that can be used to monitor, and eventually to control, the brain states of patients in the operating room and in the intensive care unit. (Some figures may appear in colour only in the online journal)
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A brain-machine interface for control of Burst Suppression in medical coma
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013Co-Authors: Maryam M. Shanechi, Jessica J. Chemali, Ken Solt, Max Liberman, Emery N. BrownAbstract:Burst Suppression is an electroencephalogram (EEG) marker of profound brain inactivation and unconsciousness and consists of Bursts of electrical activity alternating with periods of isoelectricity called Suppression. Burst Suppression is the EEG pattern targeted in medical coma, a drug-induced brain state used to help recovery after brain injuries and to treat epilepsy that is refractory to conventional drug therapies. The state of coma is maintained manually by administering an intravenous infusion of an anesthetic, such as propofol, to target a pattern of Burst Suppression on the EEG. The coma often needs to be maintained for several hours or days, and hence an automated system would offer significant benefit for tight control. Here we present a brain-machine interface (BMI) for automatic control of Burst Suppression in medical coma that selects the real-time drug infusion rate based on EEG observations and can precisely control the Burst Suppression level in real time in rodents. We quantify the Burst Suppression level using the Burst Suppression probability (BSP), the brain's instantaneous probability of being in the suppressed state, and represent the effect of the anesthetic propofol on the BSP using a two-dimensional linear compartment model that we fit in experiments. We compute the BSP in real time from the EEG segmented into a binary time-series by deriving a two-dimensional state-space algorithm. We then derive a stochastic controller using both a linear-quadratic-regulator strategy and a model predictive control strategy. The BMI can promptly change the level of Burst Suppression without overshoot or undershoot and maintains precise control of time-varying target levels of Burst Suppression in individual rodents in real time.
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EMBC - A brain-machine interface for control of Burst Suppression in medical coma
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Inte, 2013Co-Authors: Maryam M. Shanechi, Jessica J. Chemali, Ken Solt, Max Y. Liberman, Emery N. BrownAbstract:Burst Suppression is an electroencephalogram (EEG) marker of profound brain inactivation and unconsciousness and consists of Bursts of electrical activity alternating with periods of isoelectricity called Suppression. Burst Suppression is the EEG pattern targeted in medical coma, a drug-induced brain state used to help recovery after brain injuries and to treat epilepsy that is refractory to conventional drug therapies. The state of coma is maintained manually by administering an intravenous infusion of an anesthetic, such as propofol, to target a pattern of Burst Suppression on the EEG. The coma often needs to be maintained for several hours or days, and hence an automated system would offer significant benefit for tight control. Here we present a brain-machine interface (BMI) for automatic control of Burst Suppression in medical coma that selects the real-time drug infusion rate based on EEG observations and can precisely control the Burst Suppression level in real time in rodents. We quantify the Burst Suppression level using the Burst Suppression probability (BSP), the brain's instantaneous probability of being in the suppressed state, and represent the effect of the anesthetic propofol on the BSP using a two-dimensional linear compartment model that we fit in experiments. We compute the BSP in real time from the EEG segmented into a binary time-series by deriving a two-dimensional state-space algorithm. We then derive a stochastic controller using both a linear-quadratic-regulator strategy and a model predictive control strategy. The BMI can promptly change the level of Burst Suppression without overshoot or undershoot and maintains precise control of time-varying target levels of Burst Suppression in individual rodents in real time.
Jessica J. Chemali - One of the best experts on this subject based on the ideXlab platform.
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Burst Suppression probability algorithms: state-space methods for tracking EEG Burst Suppression
Journal of neural engineering, 2013Co-Authors: Jessica J. Chemali, Shinung Ching, Patrick L. Purdon, Ken Solt, Emery N. BrownAbstract:Objective. Burst Suppression is an electroencephalogram pattern in which Bursts of electrical activity alternate with an isoelectric state. This pattern is commonly seen in states of severely reduced brain activity such as profound general anesthesia, anoxic brain injuries, hypothermia and certain developmental disorders. Devising accurate, reliable ways to quantify Burst Suppression is an important clinical and research problem. Although thresholding and segmentation algorithms readily identify Burst Suppression periods, analysis algorithms require long intervals of data to characterize Burst Suppression at a given time and provide no framework for statistical inference. Approach. We introduce the concept of the Burst Suppression probability (BSP) to define the brain’s instantaneous propensity of being in the suppressed state. To conduct dynamic analyses of Burst Suppression we propose a state-space model in which the observation process is a binomial model and the state equation is a Gaussian random walk. We estimate the model using an approximate expectation maximization algorithm and illustrate its application in the analysis of rodent Burst Suppression recordings under general anesthesia and a patient during induction of controlled hypothermia. Main result. The BSP algorithms track Burst Suppression on a second-to-second time scale, and make possible formal statistical comparisons of Burst Suppression at different times. Significance. The state-space approach suggests a principled and informative way to analyze Burst Suppression that can be used to monitor, and eventually to control, the brain states of patients in the operating room and in the intensive care unit. (Some figures may appear in colour only in the online journal)
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a closed loop anesthetic delivery system for real time control of Burst Suppression
Journal of Neural Engineering, 2013Co-Authors: Max Y. Liberman, Jessica J. Chemali, Shinung Ching, Emery N. BrownAbstract:Objective. There is growing interest in using closed-loop anesthetic delivery (CLAD) systems to automate control of brain states (sedation, unconsciousness and antinociception) in patients receiving anesthesia care. The accuracy and reliability of these systems can be improved by using as control signals electroencephalogram (EEG) markers for which the neurophysiological links to the anesthetic-induced brain states are well established. Burst Suppression, in which Bursts of electrical activity alternate with periods of quiescence or Suppression, is a well-known, readily discernible EEG marker of profound brain inactivation and unconsciousness. This pattern is commonly maintained when anesthetics are administered to produce a medically-induced coma for cerebral protection in patients suffering from brain injuries or to arrest brain activity in patients having uncontrollable seizures. Although the coma may be required for several hours or days, drug infusion rates are managed inefficiently by manual adjustment. Our objective is to design a CLAD system for Burst Suppression control to automate management of medically-induced coma. Approach. We establish a CLAD system to control Burst Suppression consisting of: a two-dimensional linear system model relating the anesthetic brain level to the EEG dynamics; a new control signal, the Burst Suppression probability (BSP) defining the instantaneous probability of Suppression; the BSP filter, a state-space algorithm to estimate the BSP from EEG recordings; a proportional–integral controller; and a system identification procedure to estimate the model and controller parameters. Main results. We demonstrate reliable performance of our system in simulation studies of Burst Suppression control using both propofol and etomidate in rodent experiments based on Vijn and Sneyd, and in human experiments based on the Schnider pharmacokinetic model for propofol. Using propofol, we further demonstrate that our control system reliably tracks changing target levels of Burst Suppression in simulated human subjects across different epidemiological profiles. Significance. Our results give new insights into CLAD system design and suggest a control-theory framework to automate second-to-second control of Burst Suppression for management of medically-induced coma.
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A brain-machine interface for control of Burst Suppression in medical coma
2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2013Co-Authors: Maryam M. Shanechi, Jessica J. Chemali, Ken Solt, Max Liberman, Emery N. BrownAbstract:Burst Suppression is an electroencephalogram (EEG) marker of profound brain inactivation and unconsciousness and consists of Bursts of electrical activity alternating with periods of isoelectricity called Suppression. Burst Suppression is the EEG pattern targeted in medical coma, a drug-induced brain state used to help recovery after brain injuries and to treat epilepsy that is refractory to conventional drug therapies. The state of coma is maintained manually by administering an intravenous infusion of an anesthetic, such as propofol, to target a pattern of Burst Suppression on the EEG. The coma often needs to be maintained for several hours or days, and hence an automated system would offer significant benefit for tight control. Here we present a brain-machine interface (BMI) for automatic control of Burst Suppression in medical coma that selects the real-time drug infusion rate based on EEG observations and can precisely control the Burst Suppression level in real time in rodents. We quantify the Burst Suppression level using the Burst Suppression probability (BSP), the brain's instantaneous probability of being in the suppressed state, and represent the effect of the anesthetic propofol on the BSP using a two-dimensional linear compartment model that we fit in experiments. We compute the BSP in real time from the EEG segmented into a binary time-series by deriving a two-dimensional state-space algorithm. We then derive a stochastic controller using both a linear-quadratic-regulator strategy and a model predictive control strategy. The BMI can promptly change the level of Burst Suppression without overshoot or undershoot and maintains precise control of time-varying target levels of Burst Suppression in individual rodents in real time.
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EMBC - A brain-machine interface for control of Burst Suppression in medical coma
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Inte, 2013Co-Authors: Maryam M. Shanechi, Jessica J. Chemali, Ken Solt, Max Y. Liberman, Emery N. BrownAbstract:Burst Suppression is an electroencephalogram (EEG) marker of profound brain inactivation and unconsciousness and consists of Bursts of electrical activity alternating with periods of isoelectricity called Suppression. Burst Suppression is the EEG pattern targeted in medical coma, a drug-induced brain state used to help recovery after brain injuries and to treat epilepsy that is refractory to conventional drug therapies. The state of coma is maintained manually by administering an intravenous infusion of an anesthetic, such as propofol, to target a pattern of Burst Suppression on the EEG. The coma often needs to be maintained for several hours or days, and hence an automated system would offer significant benefit for tight control. Here we present a brain-machine interface (BMI) for automatic control of Burst Suppression in medical coma that selects the real-time drug infusion rate based on EEG observations and can precisely control the Burst Suppression level in real time in rodents. We quantify the Burst Suppression level using the Burst Suppression probability (BSP), the brain's instantaneous probability of being in the suppressed state, and represent the effect of the anesthetic propofol on the BSP using a two-dimensional linear compartment model that we fit in experiments. We compute the BSP in real time from the EEG segmented into a binary time-series by deriving a two-dimensional state-space algorithm. We then derive a stochastic controller using both a linear-quadratic-regulator strategy and a model predictive control strategy. The BMI can promptly change the level of Burst Suppression without overshoot or undershoot and maintains precise control of time-varying target levels of Burst Suppression in individual rodents in real time.
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Real-Time Measurement and Closed Loop Control of Burst Suppression for Management of Medical Coma
Transactions of Japanese Society for Medical and Biological Engineering, 2013Co-Authors: Shinung Ching, Jessica J. Chemali, Patrick L. Purdon, Ken Solt, Max Y. Liberman, Brandon Westover, Emery N. BrownAbstract:This talk will provide an overview of recent results on the design of a closed-loop anesthesia delivery (CLAD) system for controlling Burst Suppression for management of medical coma. I. EXTENDED ABSTRACT Medical coma is a state of profound unconsciousness and brain inactivation induced to treat status epilepticus – unrelenting seizures – and to facilitate recovery following traumatic brain injuries [1], [2]. Typically, an anesthetic drug such as propofol is titrated to achieve a specific clinical target that indicates a coma-like state of large-scale brain inactivation. The standard approach is to monitor the patient’s brain activity with the electroencephalogram (EEG) and use a specified level of Burst Suppression as an electrophysiological target. Burst Suppression is an EEG pattern indicating a state of highly reduced electrical and metabolic activity in the brain defined by periods of high-voltage Bursts that alternate with flatline periods termed Suppressions [3]. Burst Suppression can be controlled systematically, once the state is achieved, as the level of Suppression can be increased or decreased by adjusting the infusion rate of the anesthetic. In general, there are no established guidelines for specifying the level of Burst Suppression. A target level is agreed upon by the intensive care unit staff and control of the level is undertaken by continually monitoring the EEG and manually adjusting the drug infusion rate. A common goal of medical coma is to maintain a stable state of reduced brain activity and metabolism for 12 to 48 hours, a period significantly longer than any human operator can be expected to maintain tight control by visually monitoring the EEG and manually changing the infusion rate of the anesthetic. Defining a precise, quantitative target for the level of Burst Suppression and designing an automated system for maintaining that level would be a more prudent approach. Automation could be achieved by implementing a closed loop anesthesia delivery system. *This work has been supported by NIH DP1-OD003646 (to ENB), DP2OD006454 (to PLP) and K08-GM094394 (to KS). S.C. holds a Career Award at the Scientific Interface from the Burroughs-Wellcome Fund Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital & Harvard Medical School, Boston, MA Department of Brain and Cognitive Science, Massachusetts Institute of Technology, Cambridge, MA Harvard-Massachusetts Institute of Technology Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology, Cambridge, MA The essential elements of a CLAD system are: a specific control or target criterion; a means of measuring or estimating the value of the criterion in real time from EEG recordings; and a controller that adjusts the instantaneous dosing of the anesthetic based on the difference between the current value of the criterion and its target value. We present a CLAD system to automatically and precisely control the EEG state of Burst Suppression and efficiently maintain a medically-induced coma. Specifically, we constructed a CLAD system to control Burst Suppression reliably and accurately in real-time in both simulation [4] and in a rodent model using EEG and a computer-controlled infusion of propofol [5]. The CLAD system uses a two-compartment pharmacokinetics model to characterize the effect of propofol on the EEG. We introduce the Burst Suppression probability (BSP) algorithm [6], [7] to compute from the EEG in real time the instantaneous probability of the brain being in a state of Suppression. We estimate the parameters of a pharmacokinetics model online and use them to define a proportional-integral (PI) controller. To assess performance of our CLAD system, we establish new statistical criteria to assess reliability and accuracy at individual target levels of Burst Suppression and a Bayesian statistical approach to assess reliability and accuracy across target levels and animals. We illustrate the application of our CLAD system by demonstrating precise control of Burst Suppression in individual rats. Finally, we discuss outstanding problems and forthcoming work related to translating the CLAD system for use in human patients.
