Real-Time Segmentation and Tracking of Brain Metabolic State in ICU
EEG Recordings of Burst Suppression
M. Brandon Westover7, ShiNung Ching1,3,4, Mouhsin M. Shafi7, Sydney S. Cash7and Emery N. Brown1,4,5,6
Abstract—We provide a method for estimating brain
metabolic state based on a reduced-order model of EEG burst
suppression. The model, derived from previously suggested
biophysical mechanisms of burst suppression, describes impor-
tant electrophysiological features and provides a direct link
to cerebral metabolic rate. We design and fit the estimation
method from EEG recordings of burst suppression from a
neurological intensive care unit and test it on real and synthetic
Burst suppression is an electroencephalographic (EEG)
pattern in which periods of high voltage activity (bursts)
alternate with periods of isoelectric quiescence (suppression)
(see Figure 1). It is characteristic of a profoundly inacti-
vated brain and occurs in conditions such as deep general
anesthesia , hypothermia  and coma . That these
different conditions lead to seemingly similar brain activity
suggests that burst suppression is the result of a fundamental,
low-order process that is prominent when higher-level brain
activity is depressed.
The main features of burst suppression have been well de-
scribed , , . Classically, burst suppression is thought
to be a global state where bursts begin and end nearly
simultaneously across the entire scalp. It is different from
typical faster EEG oscillatory patterns, in that suppression
epochs can be very irregular and may last several seconds.
Importantly, burst suppression is not a homogeneous state
but, instead, varies continuously as a function of brain inacti-
vation. As the brain becomes progressively more inactivated,
the amount of suppression, relative to the amount of burst,
increases. This variation has been traditionally quantified
with the burst suppression ratio , which measures the
amount of suppression in a sliding window of EEG data.
Recent research on the burst suppression probability 
*This work has been supported by NIH DP1-OD003646 (to ENB).
SC holds a Career Award at the Scientific Interface from the Burroughs-
1Department of Anesthesia, Critical Care and Pain Medicine, Mas-
sachusetts General Hospital & Harvard Medical School, Boston, MA
3Department of Brain and Cognitive Science, Massachusetts Institute of
Technology, Cambridge, MA
4Department of Electrical and Systems Engineering, Washington Univer-
sity in St. Louis, St. Louis, MO, USA
5Harvard-Massachusetts Institute of Technology Division of Health Sci-
ences and Technology, Massachusetts Institute of Technology, Cambridge,
6Institute for Medical Engineering and Sciences, Massachusetts Institute
of Technology, Cambridge, MA
7Department of Neurology, Massachusetts General Hospital & Harvard
Medical School, Boston, MA
Example of burst suppression. (A) Continuous EEG activity, (B)
(BSP) has provided a statistically rigorous, and window-free,
approach to estimating the burst suppression state.
Here, we introduce a method for estimating not simply
burst suppression, but the underlying brain metabolic state.
Our method is based on a recent nonlinear, biophysical
model , which attributes the parametric increase in sup-
pression duration with brain inactivation to decreases in brain
We begin by characterizing the relationship between brain
metabolic state and observable EEG features, namely the
lengths and variability of bursts and suppressions. We then
introduce and fit a reduced state-space model of burst
suppression to recordings from neurological intensive care
unit (ICU) patients. From this model, we demonstrate the
inference of the underlying metabolic state.
The remainder of this paper is organized as follows.
Section II provides a brief background on the biophysical
mechanisms of burst suppression and the resulting models.
Section III introduces the reduced state-space model and
methods for metabolic state inference. Brief conclusions are
formulated in Section IV.
A. Neurophysiology of Burst Suppression
Although many features of burst suppression have been
described, the neurophysiological mechanisms that are re-
sponsible for creating it are less well understood. In the
context of general anesthesia, the early work by Steriade 
helped establish certain neural correlates of burst suppres-
sion, describing the participation of different cell types in
bursts and suppressions, though an underlying mechanism
was not suggested. Other studies  have suggested that
burst suppression involves enhanced excitability in cortical
networks, and have implicated fluctuations in calcium as
related to the alternations between bursts and suppressions.
B. Existing Models of Burst Suppression
A unifying biophysical model for burst suppression – one
that accounts for its characteristics, and also its range of
etiologies – was recently proposed . The key insight of
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through the course of each burst, leading to suppression. During suppression
ATP gradually recovers until, eventually, activity begins again
ATP-based mechanism for burst suppression. ATP is depleted
the model is that each of the conditions associated with burst
suppression (general anesthesia, hypoxic/ischemic coma, hy-
pothermia) is associated with decreased cerebral metabolic
rate of oxygen (CMRO). The model links this decrease in
CMRO to deficiencies in ATP (adenosine triphosphate, the
energetic substrate for neuronal activity) production in corti-
cal networks (see Figure 2). The termination of each burst is
a reflection of ATP consumption due to the neuronal activity
underlying fast EEG oscillations, whereas suppressions are
governed by the slow dynamics of ATP regeneration.
This model provided an explanation for why three cardinal
features of burst suppression – the spatial synchrony of bursts
onsets across the scalp, the increase in suppression durations
with increasing brain inactivation, and the long timescales
of suppressions – can arise across its disparate etiologies.
The present paper is intended to provide a simplification
of the model in , and simultaneously, to describe a
fourth cardinal feature that was not previously explored,
namely the variability of burst lengths at different burst
suppression levels. This, in turn, enables the estimation of
brain metabolism (CMRO) from EEG recordings.
III. PROBABILISTIC MODELING AND
ESTIMATION OF BURST SUPPRESSION
A. Simplified Burst Suppression Model
Based on , we present a reduced order state-space
model for burst suppression governed by the following:
˙ a = kr(1 − a) − kcu(a).
Here, a(t) is the concentration of local ATP in a cortical
region, kcis the rate of ATP consumption during each burst,
kris the rate of ATP regeneration during each suppression,
and u(a) indicates whether burst activity can or cannot be
sustained. We select
meaning that burst activity can only be initiated when ATP
levels increase beyond the threshold α.
By fixing the parameter kc= 1, (1) can be rewritten as
˙ a > 0 and 0 ≤ a < α
˙ a = x(1 − a) − u(a),
where x, a value from 0 to 1, is the brain metabolic state.
A value of x = 0 corresponds to full CMRO (when ATP
regeneration equals consumption), while x = 1 is complete
(A) x = 0.8, (B) x = 0.1. Simulated EEG signal shown for schematic
Example of model output for different values of metabolic state.
Figure 3 illustrates the output of the model for two dif-
ferent values of x. When x is moderate, the model produces
epochs of burst and suppression that are commensurate in
length. When x is reduced to a low value, the bursts are much
shorter (due to more rapid consumption) and the suppressions
are longer (due to slower regeneration).
The model (1) offers increased analytical tractability as
compared to the full nonlinear model in . In particular,
we can derive explicit expressions for burst and suppression
lengths (LSand LB) at different metabolic state levels as:
LS(x) = −log ¯ α/x
LB(x) = −log
¯ α = 1 − α.
The burst suppression state itself can then be quantified in
terms of the suppression lengths, relative to the total length
of a burst-suppression cycle, specifically:
LS(x) + LB(x)=
log ¯ α
log ¯ α + log
Note that in practice, (6) can be estimated using the burst
suppression probability (BSP)  algorithm. Through (4)
and (6), we can estimate x based on measurement of burst
suppression and calculation of burst and suppression lengths
from the EEG.
B. Automatic EEG Segmentation
In order to infer the metabolic state in our model, we
must first establish a method to segment EEG recordings into
Segmented bursts (i.e., nt = 0) are shown in red, while suppressions
(i.e., nt = 1) are blue. (A,B) Patterns containing epileptiform spikes,
(C,D) Patterns with distinct bursts and suppressions, (E,F) Patterns with
less distinct bursts
Examples of ICU burst suppression with automatic segmentation.
bursts and suppressions. That is, if xt, t = 0,1,2,... is the
sampled EEG signal, then we must obtain a corresponding
binary series ntwhere nt= 1 if xtis in a suppression and
0 if it is in a burst.
While several algorithms have been developed for this
purpose , , we choose to use adaptive variance
thresholding as follows:
¯ yt= γxt+ (1 − γ)¯ yt−1
t= γ(xt− ¯ yt)2+ (1 − γ)s2
where γ is a tunable filter parameter and v2
amplitude threshold. We have applied this method to a variety
of EEG recordings of burst suppression from the neurological
ICU  and, as illustrated in Figure 4, it can reliably
segment the EEG into bursts and suppressions.
From the binary signal ntit is straightforward to obtain
empirical lengths of bursts and suppression (simply, the
lengths of consecutive 0s or 1s), facilitating estimation of
C. Inference of Metabolic State
In order to estimate the metabolic state x as a function
of time, and to account for anticipated stochastic effects in
burst and suppression lengths, we introduce a probabilistic
model as follows:
xt= min(max(xt−1+ vt,0),1),vt∼ N (0,σ)
This model is a rectified Gaussian random walk and, if σ
is suitably small, implies that x does not exhibit large and
sudden temporal changes.
We will, furthermore, make a Markovian assumption that
p(xt|x0,x1,...,xt−1) = p(xt|xt−1)
and, in particular, that
p(nt|H (n,L,x)) = p(nt|nt−1,Lt−1,xt−1),
where Lidenotes the length of the ithevent (either a burst
or suppression) and H(·) denotes the entire history.
What remains is to define the probabilities for continua-
p(nt= 1|nt−1= 1,Lt−1,xt−1)
p(nt= 0|nt−1= 0,Lt−1,xt−1)
p(nt= 1|nt−1= 0,Lt−1,xt−1)
p(nt= 0|nt−1= 1,Lt−1,xt−1)
Based on the characterization from (4) and (6), we choose to
model these probabilities using the Weibull hazard function
and its cumulative distribution function (CDF)
F (t;λ,θ) = 1 − exp
Note that (15) and (16) are common in medical survival
analysis and reliability engineering.
We proceed to fit (16) to the burst suppression level,
which can be well-estimated from the segmented EEG using
the burst suppression probability (BSP) algorithm . In
particular, we compute an empirical CDF for (13) and (14)
by finding, for each suppression and burst, the correspond-
ing BSP level. We then fit (16) to these CDFs using the
λ(BSP) = a1exp(BSP × b1),θ = c1
for bursts and
λ(BSP) = a2exp((1 − BSP) × b2),θ = c2
for suppressions. For this, we use a nonlinear least squares
numerical method over the free parameters ai,bi,ci. Figure
5 illustrates the empirical CDF for switching from the EEGs
of 20 ICU patients1and the resulting fit for two BSP levels.
In both cases, the functions (17)-(18), together with (16),
are able to closely match the empirical CDFs. Figure 6
illustrates these fits, as compared to the empirical CDFs for
switching, across the entire range of BSP values. As shown,
the resulting model characterization is close to what we find
from our data.
The one-to-one relationship (6) relates our continuation
and switching functions (for BSP) directly to metabolic state.
We can thus proceed to perform inference of the metabolic
state through a direct application of Bayes formula to (12).
We illustrate the estimation using synthetic data generated
from the model (1). Figure 7A illustrates the burst and
suppression output (nt) from the model when x(t) is a
realization of the random walk (10). Through (12)-(16), and
1These data were collected at the Massachusetts General Hospital as part
of routine clinical monitoring and with institutional review board approval.
Fig. 5. Download full-text
levels. (A) BSP of 0.2, (B) BSP of 0.7
Example of CDF for switching and resulting fits for two BSP
suppressions (A,B) and bursts (C,D). The fitted functions (B,D) closely
match the empirical CDFs (A,C). White indicates values close to 1 (high
probability of switching) whereas black indicates values close to 0 (low
probability of switching).
Empirical1and fit switching probability functions vs. BSP for
the fits of (17)-(18) obtained empirically from our ICU data
(i.e., Figure 6), we obtain the posterior probability density
function of metabolic state x at each point in time. The
mean of each distribution is the metabolic state estimate,
which is plotted in Figure 7C and compared with the true
value. Clearly, the estimate closely tracks the true value. One
feature of note is that the estimate does not immediately
change at each switch from burst to suppression. Instead, and
consistent with our model, it remains stable during each burst
and suppression until such time as its length is improbable
given the current BSP estimate.
We have provided a reduced-order model for burst sup-
pression that links the EEG directly to reductions in cerebral
metabolic rate. From this model, we developed a probabilis-
tic inference scheme to estimate brain metabolic state from
measured EEG activity. The resulting method was fit and
tested on EEG data gathered from patients in the neurological
ICU. We then tested the method on synthetic burst suppres-
sion data, showing correct inference of metabolic state.
Further testing is, of course, necessary to validate the
use of this method in the clinical setting. Nevertheless, the
model provides justification for the practice of pharmaco-
logically inducing burst suppression as a therapeutic target
for brain protection in neurological intensive care settings
such as unrelenting seizures (refractory status epilepticus),
severe traumatic brain injury, and in cardiac surgery during
suppression. (A) Simulated bursts and suppressions from (1), (B) Proba-
bility density function of metabolic state x estimated from (12)-(18) (and
corresponding fits). (C) Inferred x (red trace) as compared to the true value
used to generate (A) (blue trace).
Example of inference of metabolic state from simulated burst
circulatory arrest . The model and estimation scheme
may also help inform strategies for optimizing burst sup-
pression when using anesthetic drugs. An eventual goal is to
provide a neurophysiologically-principled basis for inferring
and tracking brain metabolism in the ICU or surgical settings.
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