Book contents
- Frontmatter
- Contents
- List of contributors
- Preface
- Introduction
- Inference and learning in latent Markov models
- Part I State space methods for neural data
- Part II State space methods for clinical data
- Bayesian nonparametric learning of switching dynamics in cohort physiological time series: application in critical care patient monitoring
- Identifying outcome-discriminative dynamics in multivariate physiological cohort time series
- A dynamic point process framework for assessing heartbeat dynamics and cardiovascular functions
- Real-time segmentation and tracking of brain metabolic state in ICU EEG recordings of burst suppression
- Signal quality indices for state space electrophysiological signal processing and vice versa
- index
- References
Real-time segmentation and tracking of brain metabolic state in ICU EEG recordings of burst suppression
from Part II - State space methods for clinical data
Published online by Cambridge University Press: 05 October 2015
- Frontmatter
- Contents
- List of contributors
- Preface
- Introduction
- Inference and learning in latent Markov models
- Part I State space methods for neural data
- Part II State space methods for clinical data
- Bayesian nonparametric learning of switching dynamics in cohort physiological time series: application in critical care patient monitoring
- Identifying outcome-discriminative dynamics in multivariate physiological cohort time series
- A dynamic point process framework for assessing heartbeat dynamics and cardiovascular functions
- Real-time segmentation and tracking of brain metabolic state in ICU EEG recordings of burst suppression
- Signal quality indices for state space electrophysiological signal processing and vice versa
- index
- References
Summary
Introduction
Burst suppression – a discontinuous electroencephalographic (EEG) pattern in which flatline (suppression) and higher voltage (burst) periods alternate systematically but with variable burst and suppression durations (see Figure 14.1) – is a state of profound brain inactivation. Burst suppression is inducible by high doses of most anesthetics (Clark & Rosner 1973) or in profound hypothermia (e.g. used for cerebral protection in cardiac bypass surgeries) (Stecker et al. 2001); may occur pathologically in patients with coma after cardiac arrest or trauma as a manifestation of diffuse cortical hypoxicischemic injury (Young 2000), or in a form of early infantile encephalopathy (“Othahara syndrome”) (Ohtahara & Yamatogi 2006); and as a non-pathological finding in the EEGs of premature infants known as “trace alternant” or “trace discontinu.” The fact that these diverse etiologies produce similar brain activity have led to the current consensus view that (i) burst suppression reflects the operation of a low-order dynamic process which persists in the absence of higher-level brain activity, and (ii) there may be a common pathway to the state of brain inactivation.
Four cardinal phenomenological features of burst suppression have been established through a variety of EEG and neurophysiological studies (Akrawi et al. 1996; Amzica 2009; Ching et al. 2012). First, burst onsets are generally spatially synchronous (i.e., bursts begin and end nearly simultaneously across the entire scalp), except in cases of large-scale cortical deafferentation (Niedermeyer 2009), in which cases regional differences in blood supply and autoregulation may prevent the uniformity typically associated with burst suppression. A caveat here is related to recent evidence that suggests that, on a local circuit level, the onset of bursts may exhibit significant heterogeneity (Lewis et al. 2013). Second, the fraction of time spent in suppression– classically quantified using the burst suppression ratio (BSR) – increases monotonically with the level of brain inactivation. For example, the BSR increases with increasing doses of anesthetic or hypothermia, eventually reaching 100% as the EEG becomes isoelectric (flatline).
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- Information
- Advanced State Space Methods for Neural and Clinical Data , pp. 330 - 344Publisher: Cambridge University PressPrint publication year: 2015
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