J. Biomedical Science and Engineering, 2010, 3, 39-46
doi:10.4236/jbise.2010.31006 Published Online January 2010 (http://www.SciRP.org/journal/jbise/
Published Online January 2010 in SciRes. http://www.scirp.org/journal/jbise
Evaluation of EEG 2
-ratio and channel locations in
measuring anesthesia depth
Zhi-Bin Tan1, Le-Yi Wang1, George McKelvey2, Aliaksei Pustavoitau2, Guang-Xiang Yu2, Harold
Michael Marsh2, Hong Wang2
1Department of Electrical and Computer Engineering, Wayne State University, Detroit, USA;
2Department of Anesthesiology, Wayne State University, Detroit, USA.
Email: au6063@wayne.edu; lywang@wayne.edu; gmckelve@med.wayne.edu; yuguangxiang@gmail.com; hmarsh@med.wayne.edu;
Received 25 June 2009; revised 25 November 2009; accepted 30 November 2009.
In this paper, the ratio of powers in the frequency bands
of 2
waves in EEG signals (termed as the
-ratio) was introduced as a potential enhancem-
ent in measuring anesthesia depth. The 2
was compared to the relative
-ratio which had been
commercially used in the BIS monitor. Sensitivity and
reliability of the 2
-ratio and EEG measurement
locations were analyzed for their effectiveness in
measuring anesthesia depth during different stages of
propofol induced anesthesia (awake, induction,
maintenance, and emergence). The analysis indicated
that 1) the relative
-ratio and 2
-ratio derived
from the prefrontal, frontal, and the central cortex EEG
signals were of substantial sensitivity for capturing
anesthesia depth changes. 2) Certain channel positions
in the frontal part of the cortex, such as , had the
combined benefits of substantial sensitivity and noise
resistance. 3) The
-ratio captured the initial
excitation, while the relative
-ratio did not. 4) In
the maintenance and emergence stages, the 2
-ratio showed improved reliability. Implications: The
ratio of powers in EEG frequency bands 2
derived from the frontal cortex EEG channels has
combined benefits of substantial sensitivity and noise
resistance in measuring anesthesia depth.
Keywords: Anesthesia Depth; EEG (Electroencephalogram);
EEG Channels;
2-Ratio; Relative
In this paper, the ratio of powers in the frequency bands of
waves in EEG signals (termed as the
was introduced and evaluated as a potential enhancement
in measuring anesthesia depth, in comparison to the
-ratio which had been commercially used in
the BIS (Bi-spectrum Index) monitor (Aspect Medical
Systems). Sensitivity and reliability of the
and EEG measurement locations were analyzed for their
effectiveness in measuring anesthesia depth during four
stages of propofol induced anesthesia (awake, induction,
maintenance, and emergence).
Since the physiologic effects of anesthetic agents in
ether anesthesia were observed by John Snow in 1847
[1], characterizing, measuring, and continuously moni-
toring anesthesia depth have been pursued extensively.
Accurate monitoring of anesthesia depth can help to
avoid overdose of anesthetic agents, prevent intrao-
perative awareness, and assist the anesthesiologist in
anesthesia decisions and management. Case studies have
also indicated that objective monitoring of the anesthesia
depth can guide more precise administration of
anesthesia agents, and consequently can potentially
reduce drug costs, expedite post-anesthesia recovery, and
shorten hospital stay [2,3].
With the central nervous system (CNS) being the
target of anesthesia drugs, the electroencephalogram
(EEG) signal processing has naturally become the focus
for anesthesia depth monitoring [4,5]. The goal of all
these EEG processing methods was to generate some
parameters or scales, collectively called quantitative
EEG “indices,” that were clinically reliable as indicators
for anesthesia depth. It was widely believed that
anesthetics had effects on the EEG in multiple aspects:
such as amplitude, frequency, phase relation, frequency
band power transition, etc., [14]. Individual indicators,
such as spectral edge frequency, median frequency, band
power ratio, etc., demonstrated different levels of
capability, but individually did not provide completely
reliable descriptors of anesthesia depth [8]. In addition,
their sensitivity and reliability were influenced sig-
nificantly by the EEG signal channels and anesthesia
40 Z. B. Tan et al. / J. Biomedical Science and Engineering 3 (2010) 39-46
SciRes Copyright © 2010 JBiSE
F3 F2F1
20% 10%
Figure 1. Electrodes placement according to the
10-20 system. Electrode positions are denoted with
odd numbers for left electrodes, even numbers for
right, for the midline,
Fthe prefrontal,
frontal, the central,
the temporal, the
parietal, the occipital, and
the auricular.
Figure 2. Propofol administration and time interval for
each anesthesia stage.
stages. As a result, investigation of EEG parameters and
their combined utilities, EEG channel locations, and
anesthesia stages can potentially enhance our under-
standing of anesthesia depth monitoring and achieve a
better description of brain activities during anesthesia.
Five frequency bands were frequently identified for
the EEG signal: delta band or
(0. Hz), theta
band or
5 3.5
(3.5 Hz), alpha band or 7
beta-1 band or 1
(13 Hz), and beta-2 band or
(30 Hz) [2,17]. All five bands were influenced
by anesthesia agents [17]. The relative
-ratio was one
of the main parameters that were used jointly to produce
the BIS index in the BIS Monitor. It was defined as
[7], where
3047 /P
P denoted the average
spectral power in the frequency band from
in Hz.
In this paper, the 2/
-ratio was evaluated as a
potential candidate for enhancing depth measurements.
Data analysis was performed to evaluate benefits and
limitations of the
-ratio and EEG electrode
locations in relation to reliability in propofol induced
anesthesia depth measurements over different stages.
In this section, the rationale of introducing the
-ratio for anesthesia depth measurements was
explained. Then the methods of experiment setup, data
acquisition, signal preprocessing, and data analysis were
2.1. The 2
It is well understood that the state of human awareness is
associated with increased power in the higher frequency
bands (
and 2
) and decreased power in the lower
frequency bands (
bands). Consequently, it is
a sensible choice of using power ratios of high power
ranges to low power ranges as indicators of anesthesia
However, sensitivity of band powers to awareness and
alertness varies significantly. Dressler et al. introduced a
measure of discriminating capability for awareness and
alertness indications [5]. It employed a re-mapped
predicting probability, denoted by , as an indicator
for sensitivity of different frequency bins on awareness.
The higher the is, the better the discriminating
power of the frequency band has. In particular, the
average value within
band (Hz) was
shown to be much higher than that of the band between
15 to 20 Hz, which was a major part of the band
3.5 7
Hz) used in the BIS monitor.
Based on this observation, one objective of this study
was to introduce a different band power ratio: the ratio
of the 2
powers, which was defined as
. This is termed as the
47P(log 73.530
There are several potential advantages to this method:
1) The
2-ratio demonstrated more sensitivity to
changes in awakeness.
2) While EMG (electromyography) signals were often
considered as the main source of artifacts in EEG signals,
during anesthesia depth measurements EMG signals
could be a good indicator of awareness. In the frequency
band over 30 Hz, the EMG signal became more
dominant than EEG signals when the subjects became
responsive to environments. As a result, in the BIS
system and Datex Entropy Module, the frequency band
dominated by EMG was used to enhance sensitivity in
the development of the depth indicators [7,18].
One adversary impact of this approach was that EMG
frequencies extended to the alpha band, which was
covered in the relative
-ratio. This may reduce
sensitivity since the EMG increased power concentration
in both 30 47
and 11 22
. In contrast, the EMG had
Z. B. Tan et al. / J. Biomedical Science and Engineering 3 (2010) 39-46
SciRes Copyright © 2010 JBiSE
Figure 3. Comparison of the 2/
-ratio and relative
30 473.5 7
log(/ )PP
-ratio between two
different channel locations: Left plot for the channel and right plot for the channel; top for the
304711 22
log(/ )PP
-ratio and
bottom for the relative
-ratio. Five subjects were coded as S1-S5 in the legends. S3 trajetories were highlighted to show
trajectories of one subject. The same parameters from were more sensitive than those from .
Figure 4. Comparison of the trend patterns between the
-ratio and relative
30 473.5 7
log(/ )PP
in the emergence stage of the 5 subjects
(coded as ). The slopes of the
(log P
-ratio curves were
steeper than those of the relative
-ratio curves in this stage.
little effect in the
range. As the
power was used
in the 2/
-ratio, this could become more effective
than the relative
2.2. Data Collection and Analysis
2.2.1. Data Acquisition
EEG signals from 16 channels were recorded by using
the 16-channel Nolan Mindset-16 EEG data acquisition
equipment (Nolan Computer Systems, L.L.C.). Each
subject worn an electrode cap with electrodes arranged
according to the international 10-20 system, see Figure
1. The subjects were fit with one of the three sizes of the
caps. For improved electrode contact and impedance,
each electrode adaptor on the cap was injected with a
chloride-free gel of electrolyte. The electrodes were then
42 Z. B. Tan et al. / J. Biomedical Science and Engineering 3 (2010) 39-46
SciRes Copyright © 2010 JBiSE
Table 1. Averaged parameter ranges of different
channels during the induction and emergence
induction emergence
Fp1 226.09 129.19
Fp2 213.42 126.26
F7 134.28 75.81
F3 102.62 149.19
F4 289.76 153.22
F8 208.33 116.68
T3 40.27 16.55
C3 120.97 46.85
C4 115.19 47.81
T4 52.45 22.97
T5 22.85 15.97
P3 61.25 27.79
P4 68.13 27.67
T6 39.20 23.54
O1 62.88 26.32
O2 54.83 27.35
Table 2. Averaged SNRs(dB) of different channels in
different stages.
Fp1 6.45 14.03 31.90 22.51
Fp2 8.19 14.41 33.64 19.17
F7 10.32 14.01 30.41 17.14
F3 10.74 13.79 33.38 23.54
F4 14.54 16.44 31.36 27.86
F8 10.83 15.86 30.71 18.03
T3 13.71 11.94 29.09 11.52
C3 13.47 14.06 30.32 18.50
C4 16.07 15.42 31.42 18.73
T4 13.29 13.24 33.88 12.43
T5 15.92 12.29 29.07 9.77
P3 17.09 13.87 31.53 15.68
P4 19.68 14.96 31.43 15.15
T6 17.81 13.05 29.78 11.72
O1 16.98 11.20 29.05 11.17
O2 17.53 12.09 27.10 11.10
connected to the adaptor. The machine performed an
impedance test first and was ready for recording after the
test. Nolan Mindset was connected to a host computer
system. The Nolan software runed on the host computer
and recorded simultaneously the 16 channel EEG signals
with a time reference. Although the Nolan software was
capable of performing limited data analysis and display,
our data analysis was performed with special programed
The 16 channels were divided equally for the left side
p, , , , , , , ) and
the right side (,
, , , , , ,
). The reference montage was used. As a result, the
additional reference electrodes were placed with
(near the left ear) as the reference for the left-side
electrodes and 2
(near the right ear) as the reference
for the right-side electrodes.
2.2.2. Subjects and Anesthesia
The results presented in this paper were based on the
EEG recordings from 5 young healthy male volunteers.
As a pilot study, the sample size was small. The study
was conducted in the Receiving Hospital, Detroit,
Michigan, USA, and received institutional approval. All
subjects were explained of the nature of the study and
consenting participants.
The BIS values were monitored by the BIS monitor.
Other physiological vital signs (blood pressures, heart
rate, oxygen saturation, etc.) were continuously mo-
nitored by the anesthesia monitor (S-5 Anesthesia
Monitor by Datex-Ohmeda, Inc.) during the entire
Propofol infusion rates ranged from170200
minkgg //
. The data collection procedure was divided
into four separated stages, which are shown in Figure 2:
1) Awake Stage1 (15 minutes):
The subject was conscious and instructed to close their
eyes and be calm and inactive. Facial and body move-
ments were observed in this stage. No anesthesia drugs
were administered.
2) Induction Stage (15 minutes):
Propofol infusion started at the beginning of the
induction stage. During this stage, propofol infusion
rates were adjusted to achieve a BIS value to the desired
levels (between 30 and 50). Towards the end of this
15-minute period, the BIS values of all the subjects
became stable. This stage was characterized by sub-
stantial changes of anesthesia depth towards its steady-
state values. Occasional facial and body movements
3) Maintenance Stage (30 minutes):
Propofol infusion was maintained for 30 minutes to
sustain the desired BIS level and depth of anesthesia.
This stage was characterized by relatively stable BIS
values, no drug rate adjustment, and no body move-
4) Emergence Stage (15 minutes or longer):
This stage started when propofol administration ceased
with the subject gradually recovering to become awake.
Due to differences in recovery speed, the duration
1It was sometimes called the control stage. For terminology consistenc
it was called awake stage throughout this paper, as shown in Figure 2.
Z. B. Tan et al. / J. Biomedical Science and Engineering 3 (2010) 39-46
SciRes Copyright © 2010 JBiSE
Figure 5. Variances of powers in the original EEG epochs in the Fp1 channel.
Variance spikes due to artifact contamination in some epochs are indicated in
rectangular frames.
varied. Body movements became gradually apparent in
the recovery process of this stage.
2.2.3. Signal Preprocessing
The raw EEG signals were digitized with sampling
frequency 256 Hz. The 60 Hz power contamination was
visible in the recorded EEG signals. To reduce noise
effects, the original EEG data were manually cleared of
highly visible artifacts (eye movements, body move-
ments, equipment disturbance, cable movements, etc.).
Then, a low-pass filter with cutoff frequency of 47 Hz
was designed to filter out the 60 Hz power line
disturbances before data analysis.
2.2.4. Data Analysis
EEG epochs of 2560 data points (10 sec.) were used for
generating one parameter point of the
2-ratio (and
the relative
-ratio) as follows: the 10-second interval
was divided into 4 overlapping segments of 4 seconds
each:[0,4],. The spectrum of each seg-
ment was estimated by Welch's method [19]. The
resulting spectra of the four segments were averaged to
generate one spectrum. This approach reduced
zero-mean independent random sensor noises.
Then, the powers of , and were
extracted from the resulting spectra to form a value point
of the
2-ratio (and similarly the relative
for the EEG epoch. This process was repeated for the
entire EEG recording, except for epochs that were
removed due to visible artifacts. To further reduce
random fluctuations, the
2-ratios and relative
-ratios over a moving window of length 10 parameter
points were averaged to produce the final data points for
The trajectories of the
and the relative
)/(log 73.54730PP
-ratio from the
)/ 221147
P(log 30
EEG electrode for 5 patients were plotted in
Figure 3, where a patient was only indexed by a code
h as 1S, with data from one patt (code 3S)
highlighted with green color. B)
2211 were negative values, as
shown in the
/(log 3.4730PP
Plog 47
-axis of the plots. While the awake,
induction, and maintenance stages had fixed lengths, the
duration of the emergence stage was variable, with an
arrow showing the time when the subject became fully
tage was slighan its designated
m the data.
The trajectories were divided by the four stages
marked by “awareness (for awake stage),” “induction,”
“maintenance,” and “emergence.” Due to the removal of
the corrupted and other unusable data points, the length
of each stly shorter th
The following results were derived fro
3.1. Initial Depth Surge Detection
It was common that a patient responded to initial
44 Z. B. Tan et al. / J. Biomedical Science and Engineering 3 (2010) 39-46
SciRes Copyright © 2010 JBiSE
anesthesia infusion with a surge of excitement for a short
time. Capturing this initial phase of response was an
indication of causal dependence of the pater on
anesthesia depth. Figure 3 showed that the
2- ratio
demonstrated an initial su in each patient in indurgection,
while neither the relative
-ratio nor the B
3.2. Sensitivity of EEG Parameters to Anesthesia
IS did.
Depth Changes
For the induction and emergence stages in which
anesthesia depth changed greatly, the sensitivity of a
parameter to depth change was evaluated by either the
difference between its maximum and minimum values or
by the slope of the parameter trajectory during the stage.
This was accomplished by extracting the trend patterns
using the least-squares curve fitting with a secondr
polynomial modelThe trend patterns of the 2/
ratio and relative
-ratio in the emergence stage were
d in Figure 4. The larger s of the showe slope
2-ratio curves implied that the
2-ratio was
more sensitive to the dep changeth due ring threcovery
process than the relative
Sensitivity of EEG Signals from Different 3.3.
Channels to Anesthesia Depth Changes
It was visually apparentfrom Figure 3 that the
parameters derived from 4
responded tdepth chan-
ges more pronounced than those from 1O. This was
further verified by comparing the sensitivity of EEG
parameters from the frontal region to that of the
posterior region. Table 1 listed the parameter ranges in
the induction and emergence stages (the awake and
maintenance stages are irrelevant since they have nearly
constant depths) in all 16
table were calculated by =rangemaximumminimum
nels. Thtries of the chane en
2-ratio in the respective stages. Then the ranges
were averaged over the five subjects. In both stages, the
highest sensites occurrediviti at tht channels,e fron
especially the 4
3.4. Noise Resistance of EEG Channels
The amplitudes of EEG signals were noticeably lower
than noises. When an epoch of the EEG signal was
contaminated by artifacts, the variance of the power in
this epoch changed markedly from the average of recent
previous ones. This artifact detection method was used
in the Narcotrend monitor of anesthesia depth [20].
Figure 5 illustrated the variance spikes caused by arti-
cording channels, the signal-to-noise ratio was
cts, from one subject and channel 1Fp .
To quantify the noise resistance capability of different
EEG re
oise Free SignalPower
SNR oise Power
in which the powers were calculated over one given
stage. The larger the SNR, the stronger the ability of the
channel was to resist noise. Table 2 detailed the SNRs of
the 16 channels in each stage, averaged over the five
In the awake stage, patients were alert with eye and
facial movements, leading to very small SNRs. In the
maintenance stage (in deep anesthesia), SNRs were high
across all recording channels. These two stages were not
essential in comparison. In the induction and emergence
stages, in which anesthesia depth changed most dra-
matically, the SNRs in the frontal channels, especially
the 4
channel, had the highest value of all the 16
channels monitored.
A variety of methods and commercial devices for
measuring the depth of anesthesia based on EEG signals
have been developed [6,7,8,9,10,11,12,13]. At present,
the underlying mechanism of effects of anesthesia agents
on the CNS is not well understood. The main approaches
of EEG signal processing utilized empirical methods to
relate EEG parameters to the drug effects by experi-
ments and statistical analysis. Currently, there are a few
anesthesia depth monitors in the market, such as the BIS
monitor (Aspect Medical Systems, Inc.) and the Entropy
Module (GE Medical Systems, Inc.) [14]. Both monitors
used Pre-frontal EEG signals. These monitors provided
quite reliable monitoring capability in deep anesthesia.
On the other hand, due to disturbances from facial and
body movements, their reliability during induction and
recovery stages and in ICU (intensive care units)
applications remained to be enhanced [15,16].
4.1. Summary of Main Findings
This study was focused on potential utility of the
2-ratio in improvement of anesthesia depth
monitoring. Due to its close similarity to the relative
-ratio which was commercially used in the BIS
monitor, our study was focused on characteristic
comparison between the
2-ratio and the relative
-ratio. In particular, it highlighted the following
preliminary findings: 1) The relative
-ratio and
2-ratio derived from the prefrontal, frontal, and the
central cortex EEG signals were of substantial sensitivity
in capturing anesthesia depth changes. However, these
parameters from posterior area EEG signals did not
provide sufficient sensitivity to measure anesthesia depth
variations. 2) Certain channel positions in the frontal
part of the cortex had the combined benefits of
substantial sensitivity and noise resistance, particularly
Z. B. Tan et al. / J. Biomedical Science and Engineering 3 (2010) 39-46
SciRes Copyright © 2010 JBiSE
in regards to facial and eye movements which were
major artifacts in EEG signals. 3) In the induction stage,
there was a well-recorded short period (within the initial
several minutes of drug administration) of initial
excitation in most patients due to initial response to drug,
evidenced by patient movements and other responses.
The relative
-ratio did not capture this short surge in
EEG activity, which may also explain lack of indication
of this phenomenon in the BIS monitor. The
captured this in all five subjects. 4) In the maintenance
and emergence stages, the
2-ratio showed smaller
sample variances than those of the relative
indicating an improved reliability. In fact, in some
patients the relative
-ratio showed similar values
between a fully awake state (at the end of emergence
stage) and deep anesthesia of the same patient. A trend
data fitting showed that the
2-ratio seemed to be
more reliable in providing a more consistent trend of
anesthesia depth during the maintenance and emergence
4.2. Discussions
Since the BIS monitor was the first anesthesia depth
monitor on the market and has been used extensively in
operating rooms, the fundamental parameter in the BIS
monitor, the relative
-ratio, was used as the main
reference standard. On the other hand, there were many
modifications in the BIS algorithm that were apart from
and in addition to the relative
-ratio. These modi-
fications were needed before a reasonable compa- rison
could be made between the (modified)
2-ratio and
BIS measurements. To make such a comparison more
relevant, the actual BIS reading was not used, but rather
the relative
-ratio was extrated from the raw EEG data
in BIS recording. Our findings were in the following key
aspects of anesthesia depth monitoring.
The relative
-ratio and the
2-ratio demonstrated
different sensitivities in distinct anesthesia stages. In the
induction stage, the relative
-ratio fell down faster than
2-ratio. On the other hand, the
captured the initial surge of alertness from anesthesia
drugs, but was subject to a delay and less sensitivity in
anesthesia depth monitoring. This observation suggested a
potential combined utility of the two parameters in the
induction stage. During deep anesthesia (in the
maintenance stage), both
2-ratio and relative
-ratio had substantial reliability. In the emergence stage,
2-ratio was more responsive to the depth
changes during recovery than the
Figures 3 showed the traces of the relative
2-ratio that were derived from the 4
channels, respectively. The results demonstrated that
both the relative
-ratio and
2-ratio tracked
anesthesia depth changes with substantial sensitivity
when they were computed from the frontal channels
such as the 4
EEG. However, parameters derived
from the temporal, parietal and occipital regions were of
little utility. For example, the same parameters that were
computed from the posterior area channels such as the
EEG did not provide sufficient discriminating
capability. In particular, in Table 1 the highest sensi-
tivities, both in the induction and emergence stages,
occurred in the channel
. This result rendered the
channel EEG signals most sensitive to the
influence of anesthesia agents. On the other hand, the
parameters derived from the channels 1,
, and , were sufficient to make them
candidates for depth measurements. From the data
collected in this study, it appeared that the EEG signals
recorded from the frontal and central channels may best
describe the brain activities during anesthesia.
The noise resistance capability was also distinct
among different channels. An analysis of data from
Table 2 revealed that in the awareness and induction
stages, due to facial, eye, and body movements, the EEG
signals suffered from large artifacts, represented by
lower SNRs. Within the front and central channels (that
provide substantial sensitivity for depth measurements),
channel had nearly the largest SNR. In the
maintenance stage, as artifacts became very small all
channels displayed similar SNRs. During the emergence
stage, the front and central channels were relatively
noise resistent. Still the 4
channel demonstrated
nearly the largest SNR.
This analysis suggests that the non-prefrontal channels
such as 4
may be a sound candidate for a better
tradeoff between signal sensitivity to the depth changes
and noise resistance capability. This may be especially
useful as a potential remedy to the typical cases of BIS
reliability in ICU (Intensive Care Unit) settings where
noise artifacts make the BIS index far less reliable than
in deep anesthesia patients.
It was cautioned that the above discussions and
findings were based on a very small sample of five
subjects, and as a result, they should be viewed as initial
investigation and promising potential benefits of the
2-ratio. To substantiate the findings, a much larger
sample of subjects must be conducted. This was the goal
of our next study. The ultimate objective, if the findings
were substantiated in the subsequent studies, was to
integrate the improved EEG signal processing technique
to enhance the existing anesthesia depth monitoring
accuracy and reliability.
The BIS monitor used prefrontal EEG channels. This
46 Z. B. Tan et al. / J. Biomedical Science and Engineering 3 (2010) 39-46
SciRes Copyright © 2010
had the advantage of easiness in applications since the
prefrontal EEG electrodes did not contact hairs. Howe-
ver, the BIS monitor had the issue of reliability. This
problem was particularly acute in ICU applications since
the patients were under low anesthesia sedation.
Consequently, muscle movements were far more
frequent than in deep anesthesia. The frontal region was
affected much less by EMG signals, offering a more
reliable location for EEG measurements. It was possible
that by using the frontal channels, one may be able to
enhance reliability substantially. The tradeoff between
monitor performance and easiness in usage needs to be
further studied.
The above findings implied some possibilities in
improving anesthesia depth monitoring: 1) Use better
EEG channels at the front locations, rather than
prefrontal locations; 2) Use a combined parameter, such
as a weighted sum of the
2-ratio and relative
-ratio, in the induction stage: Initially more weight on
2-ratio to capture the initial surge of awareness,
then changes gradually to the relative
-ratio to take
advantages of larger response sensitivity (hence a larger
signal). 3) Use the
2-ratio to replace the relative
-ratio in the maintenance and emergence stages.
The authors would like to acknowledge contributions from Drs.
Stephan James and Andrew Cherro for performing anesthesia
administration during data collections.
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