J. Biomedical Science and Engineering, 2009, 2, 9-15
Published Online February 2009 in SciRes. http://www.scirp.org/journal/jbise JBiSE
Assessment of depth of anesthesia using principal
component analysis
Mina Taheri1, Behzad Ahmadi2, Rassoul Amirfattahi3 and Mojtaba Mansouri4
1,2,3Digital Signal Processing Research Lab, Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran
1,2,3,4Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran. Correspondence should be addressed to Mina
Taheri (mh.taheri@ec.iut.ac.ir)
Received June 9th, 2008; revised October 12th, 2008; accepted November 13th, 2008
A new approach to estimating level of uncon-
sciousness based on Principal Component
Analysis (PCA) is proposed. The Electroen-
cephalogram (EEG) data was captured in both
Intensive Care Unit (ICU) and operating room,
using different anesthetic drugs. Assuming the
central nervous system as a 20-tuple source,
window length of 20 seconds is applied to EEG.
The mentioned window is considered as 20
nonoverlapping mixed-signals (epoch). PCA
algorithm is applied to these epochs, and larg-
est remaining eigenvalue (LRE) and smallest
remaining eigenvalue (SRE) were extracted.
Correlation between extracted parameters (LRE
and SRE) and depth of anesthesia (DOA) was
measured using Prediction probability (PK). The
results show the superiority of SRE than LRE in
predicting DOA in the case of ICU and isoflurane,
and the slight superiority of LRE than SRE in
propofol induction. Finally, a mixture model
containing both LRE and SRE could predict
DOA as well as Relative Beta Ratio (RBR), which
expresses the high capability of the proposed
PCA based method in estimating DOA.
Keywords: Bispectral Index, Depth of Anesthe-
sia, Eignevalue Decomposition, Principal Com-
ponent Analysis
To provide optimal working conditions for surgeons in
the operating room as well as ensuring patient’s safety,
an anesthesiologist’s effort is absolutely essential. How-
ever, patient awareness during surgery with the rate of
1:1000 [1] and over dosing with anesthetic agents is of
major clinical concerns of anesthesia. Therefore, the ne-
cessity to assess and monitor the depth of anesthesia
(DOA) is obvious. In conventional methods, DOA is
measured based on the monitoring of several physio-
logical signals such as respiration pattern, blood pressure,
body temperature, tearing, sweating and heart rate [1],
even though these signals are affected indirectly by an-
esthetic agents. On the other hand, these agents have
significant effects on the electroencephalogram (EEG)
A large amount of information can be extracted from
EEG waveform based on different signal processing
methods. Ability of this information to predict DOA de-
pends on the variation of its value in different levels of
anesthesia. In general, the goal is to produce a unit-less
EEG-based index that monotonically quantifies DOA.
Several methods are available that have recently been
reviewed by Freye et al. [2] and Jameson et al. [3].
The earliest methods were based on the FFT analysis
of EEG signals. These approaches tend to find parame-
ters that describe spectrum characteristics. Peak power
frequency (PPF), median power frequency (MPF), and
spectral edge frequency (SEF) have been the first de-
scriptors in this field. Another parameter extracted from
spectrum was the ratio of power in two empirically de-
rived frequency bands [4]. In a work presented by Traast
et al. [5] the power of EEG in different frequency bands
was determined and the results indicate pronounced
changes in EEG during emergence from propo-
fol/sufentanil total intravenous anesthesia.
Zikov et al [6] proposed a wavelet based anesthetic
value for central nervous system monitoring (WAVCNS)
that quantifies the depth of consciousness between
awake and isoelectric state. Their proposed technique is
based on the analysis of the single-channel (frontal) EEG
signal using stationary wavelet transform (SWT). The
wavelet coefficients calculated from the EEG are pooled
into a statistical representation which is then compared
to well-defined awake and isoelectric states. Presenting a
clinical study, they compared this technique with BIS
monitor (Aspect Medical Systems, MA) as a reference
and showed that they are well correlated (r=0.969). Fur-
thermore, WAVCNS had a faster algorithm than BIS and
was well suited for use as a feedback sensor in advisory
systems and closed-loop control schemes.
Ferenets et al [7] analyzed the performance of several
new measures based on the regularity and complexity of
the EEG signal. These measures consist of spectral en-
SciRes Copyright © 2009
10 M. Taheri et al. / J. Biomedical Science and Engineering 2 (2009) 9-15
SciRes Copyright © 2009 JBiSE
tropy (SpEn), approximate entropy (ApEn), and Higuchi
fractal dimension (HFD) and Lempel-Ziv complexity
(LZC). Their results show superior ability of the men-
tioned measures to predict DOA. Due to the arguments
presented in their paper it is not feasible to point out “the
best” EEG measure for the assessment of the depth of
sedation, their results indicate that the measures sensitive
to both the power spectrum as well as the amplitude distri-
bution, i.e., the ApEn, LZC and HFD, perform slightly bet-
ter than the other two tested measures. In the case of their
tested measures, they recommend window length of 20 s.
Application of neural networks (NN) in estimating
DOA is reviewed by Robert et al [8]. They examined a
large number of EEG derived parameters as NN inputs
including spectral, entropy, complexity, bicoherence,
wavelet transformation derived, autoregressive modeling
and hemodynamic parameters as well as a great NN to-
pology such as MLP and Self-Organizing networks. Fi-
nally, they recommended a two hidden layers MLP
model or an ART model in which their weights are con-
tinuously updated after training phase. Moreover the use
of qualitative parameters, besides quantitative ones, as
network inputs is recommended. In a recent work by
Lalitha et al [9] non-linear chaotic features and neural
network classifiers are used to detect anesthetic depth
levels. Chaotic features consist of correlation dimension
(CD), Lyapunov exponent (LE) and Hurst exponent (HE)
are used as features and two neural network models, i.e.,
multi-layer perceptron network (feed forward model)
and Elman network (feedback model) are used for clas-
sification. Their experimental results show that the
Lyapunov exponent feature with Elman network yields
an overall accuracy of 99% in detecting the anesthetic
depth levels.
According to various mentioned methods, different
EEG monitors have been developed. The Narcotrend™
monitor (Monitor Technik, Bad Bramsted, Germany) that
is based on pattern recognition of the raw EEG and clas-
sifies the EEG into different stages, introduces a dimen-
sionless Narcotrend™ index from 100 (awake) to 0
(electrical silence). The algorithm uses parameters such
as amplitude measures, autoregressive modeling, fast
Fourier transform (FFT) and spectral parameters [10].
The SEDLine™ EEG monitor capable of calculating of
PSI™ index uses the shift in power between the frontal
and occipital areas. The mathematical analysis includes
EEG power, frequency and coherence between bilateral
brain regions [11]. Datex-Ohmeda™ s/5 entropy Module
uses entropy of EEG waves to predict DOA [3] and fi-
nally BIS™ (Aspect Medical Systems, Newton, MA),
that is the first monitor in the marketplace and has be-
come the benchmark comparator for all other monitors,
introduces the BIS™ index (that is a unit-less number
between 100 and 0) as a DOA indicator based on com-
bination of spectral, bispectral and temporal analysis [4].
Approximately 450 peer-reviewed publications between
1990 and 2006 have examined the effectiveness, accu-
racy and usefulness, both clinical and economical, of the
BIS™ monitor [3].
The aim of Principal Component Analysis (PCA) is to
find source signals which are gaussian and uncorrelated.
PCA can be interpreted in terms of blind source separa-
tion methods inasmuch as PCA is like a version of ICA
in which the source signals are assumed to be gaussian.
In other words, PCA finds a matrix which transforms the
signal mixtures into a new set of uncorrelated signals.
Extracted signals are ordered via PCA according to their
variances (variance can be equated with power or ampli-
tude). Consequently, the function of PCA is more than sim-
ply finding a transformation of the signal mixtures [12].
PCA has been widely used in pattern recognition and
signal processing. The major applications and examples
are engineering and scientific disciplines, e.g., in data
compression, feature extraction, noise filtering, signal
restoration, and classification [13]. PCA is used widely
in data mining as a data reduction technique. In image
processing and computer vision, PCA representations
have been used for solving problems such as face and
object recognition, tracking, detection, background mod-
eling, parameterizing shape, appearance, and motion [14,
15]. In [16], the noise sensitivity, specificity and accu-
racy of the PCA method is evaluated by examining the
effect of noise, base-line wander and their combinations
on the characteristics of ECG for classification of true
and false peaks.
The most important biomedical application of ICA and
PCA is identifying different types of generators of the
EEG as well as identifying its magnetic counterpart
(MEG) [17]. MEG measurements give basically very
similar information to EEG, but with a higher spatial
resolution. MEG is mainly used for basic cognitive brain
research. Another contribution is noise cancellation for
brain signals such as electroencephalograms and magne-
toencephalograms (EEG/MEG). References [18,19] in-
troduced a new method to separate brain activity from
artifacts using ICA. The approach is based on the as-
sumption that the brain activity and the artifacts, e.g. eye
movements or blinks, or sensor malfunctions, are ana-
tomically and physiologically separate processes, and
this separation is reflected in the statistical independence
between the magnetic signals generated by those proc-
esses. In addition, ICA has been applied to problems in
fields as diverse as speech processing, brain imaging (e.g.,
fMRI and optical imaging [20]), electrical brain signals
(e.g., EEG signals), to extract features from a special array
of electroencephalographic electrodes. The ICA framework
can also be used for feature extraction from other kinds of
data, for example, color and stereo images [21,22].
Additionally, EEG from patients undergoing surgery
was collected. We introduce a novel method based on
PCA. Our concentration would be on eigenvalues and
eigenvectors. Finally, based on proper statistical methods
and our data bank, the correlation between the extracted
parameters and BIS index is observed. The reminder of
paper is organized as follows: In part 2, methods and
materials is described. The results and discussion are
presented in section 3, and section 4 contains the final
M. Taheri et al. / J. Biomedical Science and Engineering 2 (2009) 9-15 11
SciRes Copyright © 2009 JBiSE
In this section, the experiment, the data acquisition, and
the data analysis are described.
2.1. Patients
Following the approval of the ethical committee of the
medical school, eight coronary artery bypass graft sur-
gery candidates were selected (6 males, 2 females, of
average age 56.2 years and the average weight of 68.3kg)
and written informed consents were obtained from all
selected subjects. Inclusion criteria were absent of neu-
rological disorders such as cerebrovascular accidents and
convulsions. Preoperative neurological complications
(such as cerebral emboli and convulsion) caused exclu-
sion from the study. The anesthesiologist performed
Preoperative evaluation on the day before surgery. For
anxiolysis, the patients were premedicated by intramus-
cular morphine 0.1 mg/kg and promethazine 0.5 mg/kg,
30 minutes before transfer to operating room. After arri-
val in operating room, electrocardiogram, pulse oxy-
metry, depth of anesthesia, and invasive blood pressure
monitoring was established. The BIS-QUATTRO sen-
sorTM (Aspect Medical Systems, Newton, MA) applied
to the forehead of the patients before induction of anes-
thesia. Then 8 patients after preoxygenation with O2,
were anesthetized in the same manner by intravenous
thiopental sodium (5mg/kg), pancuronium bromide (0.1
mg/kg), fentanyl (5μg/kg), and lidocaine (1.5 mg/kg).
After the induction of anesthesia and until cardiopul-
monary bypass beginning, anesthesia continued by ad-
ministration of isoflurane (1 MAC), morphine (0.2
mg/kg) and O2 (100%). During coronary artery bypass
grafting under CPB, patients were anesthetized by pro-
pofol (50-150 μg/kg/min) under BIS control (40-60) and
O2 (80%). For organ protection during CPB, patients
were undergone mild hypothermia (31-33°C). After
coronary artery bypass grafting and patients rewarming
and obtaining standard CPB separation criteria, the pa-
tients gradually were weaned from CPB. After separation
from CPB, anesthesia was continued by isoflurane (1
MAC) and O2 (100%) administration to the end of sur-
gery. After surgery, patients were transported to ICU
under portable monitoring and manual ventilation. In the
ICU mechanical ventilation with 60% fractioned inspired
oxygen and standard homodynamic monitoring were
continued. In ICU and until complete recovery, the seda-
tive regimen was intravenous morphine (2mg) if needed.
In this study the raw EEG data and relative BIS index
were collected during whole period of operation from
operative room arrival to complete recovery in the inten-
sive care unit.
2.2. Data Acquisition
The EEG signal was collected by using a BIS-QUAT-
TRO Sensor™ that was composed of self- adhering
flexible bands holding four electrodes, applied to the
forehead with a frontal-temporal montage.
The used EEG lead was Fpz-At1, and the reference
lead was placed at FP1. The sensor was connected to a
BIS-X-P Monitor and all binary data packets containing
raw EEG data wave signals and BIS index which is con-
verted to binary format using an A/D converter operating
with 128 Hz sampling frequency were recorded via an
RS232 interface on a laptop using a Bi-spectrum ana-
lyzer developed with C++ Builder by Satoshi Hagihira
[23]. The algorithms that are presented in this study were
tested on these raw EEG signals.
The sensor was attached to the patient’s forehead at
the beginning of anesthesia and the data were collected
continuously until he/she awoke at ICU. Therefore, in
this study a large amount of EEG data with their BIS
index was collected for each patient. Although DOA is
an index beyond BIS index and BIS index needs to be
validated and processed, in this paper the BIS index is con-
sidered as DOA for simplicity. Some other events such as
changes of anesthesia regimen, intubations and applying
CPB and transferring to ICU were recorded. Because of
short acting time of thiopental sodium (approximately
15-20 sec), this part of EEG data was not analyzed.
2.3. Principal Component Analysis
PCA is a well-known technique in multivariate analysis
and data mining. One of the properties of PCA is Ei-
genvalue Decomposition. The aim of PCA is to derive a
relatively small number of decorrelated linear combina-
tions (principal components) of a set of random zero-
mean variables while also retaining the signal informa-
tion as much as possible [24].
Principal Components Analysis has the applications of
dimensionality reduction, determination of linear com-
binations of variables, feature selection, multidimen-
sional data visualization, and identification of underlying
Often components with the smallest variances called
minor components (MCs) are regarded as unimportant or
associated with noise, while those within which the input
data have the largest variances are regarded as important.
However, in some applications, the MCs are of the same
importance as the PCs, which is noteworthy here. In the
proposed algorithm the MCs reveal meaningful informa-
tion. In the case of feature extraction and dimension re-
duction, PCA proposes a method based on the eigen
structure of data covariance matrix. If signals are
zero-mean, the covariance and correlation matrices are
identical. Applying the PCA or equivalently Karhunen-
Loeve transform (KLT) as a technique for eigenvectors and
eigenvalues computation, the algorithm could be formu-
lated as follows. Let X the signal to be analyzed, then
ℜ∈Λ== )}()({ (1)
Where XX
XXE= is the covariance matrix of
zero-mean signal X and E is the expectation operator.
Also, },...,,{ 21 m
is a diagonal matrix con-
taining m eigenvalues and mm
vvvV ×
ℜ∈= ],...,[ 21 are
principal eigenvectors. Applying KLT as a linear trans-
formation, principal and minor components could be
12 M. Taheri et al. / J. Biomedical Science and Engineering 2 (2009) 9-15
SciRes Copyright © 2009 JBiSE
extracted as follows
SP = (2)
Where T
mkxkxkxX )](),...,(),([ 21
= is the zero-mean
input vector and T
nP kykykyy )](),....,(),([ 21
= is the
output vector called the vector of principal components
(PCs) and T
nm× is the set of sig-
nal subspace eigenvectors, with the orthonormal vectors
imiii vvvv],...,,[ 21
=. The vectors i
v are eigenvectors of
the covariance matrix, while the variances of the PCs
y are the corresponding principal eigenvalues. Minor
components are
NM = (3)
Where ],...,,[ 11 +−−
=nmmmN vvvV consists eigenvectors
associated with the smallest eigenvalues [21]. The basic
problem is the standard eigenvalue problem which can
be formulated by the equations
nivvR iiiXX ,...,2,1,
Where i
v are the eigenvectors and i
are the cor-
responding eigenvalues. Note that the above equation
can be written in matrix form Λ=VRVXX
In the standard numerical approach for extracting the
principal components, first the covariance matrix
R}{ T
XXE= is computed and then its eigenvectors
and corresponding eigenvalues are extracted by one of
the known numerical algorithms. However, if the input
data vectors have a large dimension, then the covariance
matrix XX
R becomes very large and it may be difficult
to compute the required eigenvectors [24].
A neural network approach with adaptive learning al-
gorithms enables us to find the eigenvectors and the as-
sociated eigenvalues directly from the input vectors
without a need to compute or estimate the very large
covariance matrix XX
. Such an approach will be espe-
cially useful for nonstationary input data, i.e., in cases of
tracking slow changes of correlations in the signals or in
updating eigenvectors with new samples.
Every neuron inside the human brain acts like a small
electric generator when it is active. If large numbers of
neurons become simultaneously active it is possible to
measure the resultant electrical effects at the scalp using
an array of electrodes. Our virtual assumption is to simu-
late the central nervous system (CNS) as a 20-tuple
source, which generate 20 signals. The EEG sensor at-
tached to patient forehead collect different 20-tuple mix-
tures of these sources. Our aim is to track small changes,
but due to time-domain nature of our analysis, small
window lengths are more preferable. Fortunately, this
would reduce the dimension. Nevertheless, large data
vectors made the covariance matrix XX
R very large.
Different window and epoch (each mixed signal is
named as an epoch) lengths have been investigated and
at the end, window length of 20 seconds was selected for
further analysis. After that, each window is divided into
20 equal and nonoverlapping epochs. So, epoch length is
equal to one second. The mentioned epochs are consid-
ered as 20 mixed signals. Then, PCA analysis and espe-
cially eigenvalue decomposition are applied to the elec-
troencephalogram (EEG). Thus, the covariance matrix
R}{ T
XXE= is computed and then its eigenvectors
and associated eigenvalues are determined. The extracted
eigenvalues presented an acceptable behavior in different
depths of anesthesia. Our concentration was put specifi-
cally on largest remaining eigenvalue (LRE) and small-
est remaining eigenvalue (SRE). The correlation between
DOA and LRE were measured with regression analysis.
The same was done for SRE and DOA.
2.4. Statistical Analysis
The coefficient of determination (R2) was calculated to
evaluate the performance of different parameters and
their combinations to predict DOA. Statistical signifi-
cance was assumed at probability levels of P0.05. Our
aim was to maximize the correlation between the meas-
ured sub- parameters (LRE and SRE) and BIS index, i.e.,
it is equivalent to nonlinear regression with ordinary
least squares. Also, the correlation between BIS index
and the extracted sub-parameters was investigated with
the model-independent Prediction Probability (Pk) [25].
As a nonparametric measure, the Pk is independent of
scale units and does not require knowledge of underlying
distributions or effort to linearize or otherwise transform
scales. A Pk value of 1 means that the predicting vari-
ables (LRE and SRE) always predict the value of the
predicted variable (e.g., BIS index) correctly. Pk value of
0.5 means that predictors predict no better than only by
chance. The Pk values were calculated on a spreadsheet
using the Excel 2003 software program and the
PKMACRO written by Warren Smith [25]. In the case of
inverse proportionality between indicator and indicated
parameters, the actual measured Pk value is 1-Pk.
Another statistical analysis used in this study was ordinal
logistic regression. This regression examines the relation-
ship between one or more predictors and an ordinal re-
sponse. The index that determines the efficiency of this
regression model is called “Concordant”, which shows the
percentage of values predicted successfully with the model.
The results were classified in drug groups (isoflurane
and propofol) and Intensive Care Unit (ICU). The corre-
lation between the extracted parameters and BIS index
(Bispectral index) is measured by means of the statistical
methods described in the previous section and the results
are presented.
3.1. SRE
First and foremost, it can be concluded that SRE is di-
rectly proportional to the BIS index. The scatterplots depicted
in Figure 1 and Figure 2 show the above assert clearly.
Figure 3 compares the efficiency of SRE and LRE in
predicting depth of anesthesia in different groups (ICU
and drugs). In this figure, concordant was used as a sta-
tistical measure.
M. Taheri et al. / J. Biomedical Science and Engineering 2 (2009) 9-15 13
SciRes Copyright © 2009 JBiSE
Smallest Remaining Eignevalue (SRE)
Bispectral index
Figure 1. BIS index versus Smallest Remaining Eigen-
value (SRE) in ICU
Smallest Remaining Eigenvalue (SRE)
Bispectral index
Figure 2. BIS index versus Smallest Remaining Eigen-
value (SRE) in propofol
Co ncordant
Pr opofolIsofluraneICU
LRE (Largest Remaining Eignevalue)
SRE (Smallest Remaining Eignevalue)
Figure 3. Concordant for different groups versus SRE and LRE
The results show the higher capability of SRE when
it is used as a measure of DOA in ICU, rather than
when it is used as measure of DOA in isoflurane and
propofol groups. For further analysis, prediction prob-
ability (PK) was used. PK values are presented as
mean ± STD in Table 1.
It should be noted that the Pk values are calculated for
the whole BIS index range and without being divided
into predetermined groups. This is the reason of the ex-
istence of smaller values in comparison with concordant
values. The values in Table 1 corroborate the results of
Figure 3.
Table 1. Prediction probabilities of different group for SRE
ICU 65.5±8 %
Isoflurane 63±8 %
Propofol 58.5±10 %
Largest Remaining Eigenvalue (LRE)
Bispectral index
Figure 4. BIS index versus Largest Remaining Eigen-
value (LRE) in ICU
Largest Remaining Eigenvalue (LRE)
Bispectral index
Figure 5. BIS index versus Largest Remaining Eigenvalue
(LRE) in isoflurane
Table 2. Prediction probabilities of different group for LRE
Different Groups Prediction Probability
ICU 60±6.97 %
Isoflurane 60.5±8 %
Propofol 62.5±9 %
3.2. LRE
LRE is inversely proportional to the BIS index, that is,
LRE increases with the increasing depth of anesthesia.
Scatterplots shown in Figure 4 and Figure 5 could prove
the above claim.
In order to compare the efficiency of LRE algorithm
in different groups we should refer to Figure 3. This
figure indicates that there is no obvious superiority in
any of these groups.
14 M. Taheri et al. / J. Biomedical Science and Engineering 2 (2009) 9-15
SciRes Copyright © 2009 JBiSE
In this case prediction probability (PK) provides us with
a more precise insight. PK values are presented as
mean±STD in Table 2. The measured PK values presented
in Table 2 confirm the result of Figure 3 for LRE. The
only extra information extracted from Table 2 is that in the
case of propofol induction the results are slightly better.
Finally, Figure 3 expresses the superiority of SRE
than LRE in predicting DOA in the case of ICU and
isoflurane induction, and the slight superiority of LRE
than SRE in propofol induction.
3.3. Relative Beta Ratio
Finally a mixture model including both LRE and SRE is
compared to the model containing Relative Beta Ratio
(RBR). RBR is calculated as
= (5)
Where, P30-47Hz and P11-20Hz indicate the power spectral
density in frequency ranges of 30-47 Hz and 11-20 Hz,
respectively. A mixture model is a model in which all of
the model parameters are involved in predicting the de-
sired index. For instance, in our mixture model, BIS in-
dex is predicted using both LRE and SRE parameters.
RBR is said to be the main parameter in calculating the
BIS index and is referred to as an effective and critical
parameter in predicting depth of anaesthesia [4]. Figure
6 reveals a high similarity between the proposed mixture
model containing both LRE and SRE parameter and the
main parameter of BIS monitor which is called RBR.
In this figure, the group containing isoflurane induc-
tion is omitted. Both of the parameters (LRE and SRE)
perform more satisfactorily in ICU than propofol induc-
A method based on PCA is proposed to estimating DOA.
The principal components are extracted and the related
eigenvalues are calculated as well. The smallest and larg-
est eigenvalues express a meaningful behavior due to the
changes of BIS index. So, the above parameters are se-
lected for estimating DOA.
LRE (Largest Remaining Eigenvalue)
RBR (Relative Beta Ratio)
SRE (Smallest Remaining Eigenvalue)
Figure 6. Comparison of RBR and proposal mixture
model based on concordant values
The main result of applying the foregoing algorithm is
that SRE is directly proportional to the BIS index. On the
other hand, LRE is inversely proportional to the BIS
index. All in all, the results show an acceptable correla-
tion between the extracted parameters and DOA. The
LRE and SRE are extremely capable of estimating DOA,
especially in ICU. This is due to the ability of PCA in
calculating the changes in signal energy and the changes
in signals complexity as well. On the other hand, it is
shown [7] that the EEG signal complexity changes
meanwhile patients level of consciousness vary. Thus,
PCA could be a powerful tool for predicting BIS index.
Except in propofol, the SRE parameter could predict
the BIS index better than LRE. Consequently, the mix-
ture model containing both LRE and SRE is approxi-
mately equal to a model containing RBR in predicting
BIS index.
Another point that should be mentioned is that the
original BIS is in fact much more than its components.
The elaborate artifact rejection algorithms as well as the
nature of the nonlinear function to combine the compo-
nents have an important impact on the original BIS value,
which were not considered in this study. Consequently,
in order to improve the accuracy of the depth of anesthe-
sia estimation, comparison against sedation scales (such
as OAA/S) and drug levels is needed. The reason is that
BIS is not equal to depth of anesthesia but needs to be
validated for DOA assessment itself.
The work reported is preliminary. Although the results
are significant, wide patient population is necessary for
better evaluation. In conclusion, the approach used in
this work based on the application of PCA could propose
the use of PCA in estimating DOA.
The authors would like to thank Dr. Satoshi Hagihira due to his notifi-
cation and problem solving in the application of Bispectrum Analyzer
and Dr. Warren Smith that made PKMACRO available.
[1] R. D. Miller, (2005) Miller’s Anesthesia, Sixth edition, Elsevier
Churchill Livingstone, 1227-1264.
[2] E. Freye and J. V. Levy, (2005) “Cerebral monitoring in the op-
erating room and the intensive care unit: An introductory for the
clinician and a guide for the novice wanting to open a window to
the brain. Part I: The electroencephalogram”, J Clin Monit Com-
put, 1-76.
[3] L. C. Jameson and T.B. Sloan, (2006) “Using EEG to monitor
anesthesia drug effects during surgery”, J Clin Monit Comput,
[4] I. J. Rampil, (1998) “A primer for EEG signal processing in an-
esthesia”, Anesthesiology, 980-1002.
[5] H. S. Traast and C. J. Kalkman, (1995) “Electroencephalographic
Characteristics of emergence from propofol/sufentanil total in-
travenous anesthesia”, Anesth Analg, 366-371.
[6] T. Zikov, S. Bibian, G. A. Dumont, M. Huzmezan, and C. R. Ries,
(2006) “Quantifying cortical activity during general anesthesia
using wavelet analysis”, IEEE Trans. Biomed. Eng., Vol. 53, No.
4, 617-632.
[7] R. Ferenets, T. Lipping, A. Anier, V. Jäntti, S. Melto, and S.
Hovilehto, (2006) “Comparison of entropy and complexity meas-
M. Taheri et al. / J. Biomedical Science and Engineering 2 (2009) 9-15 15
SciRes Copyright © 2009 JBiSE
ures for the assessment of depth of sedation”, IEEE Trans. Bio-
med. Eng., Vol. 53, No. 6, 1067-1077.
[8] C. Robert, P. Karasinski, C. D. Arreto, and J. F. Gaudy, (2002)
“Monitoring Anesthesia using neural networks: A survey”, J Clin.
Monit. Comput, Vol. 17, 259-267.
[9] V. Lalitha and C. Eswaran, (2007) “Automated Detection of
Anesthetic Depth Levels Using Chaotic Features with Artificial
Neural Networks”, J Med Syst, 445-452.
[10] R. Bender, B. Schultz, and U. Grouven, (1992) “Classification of
EEG signals into general stages of anesthesia in real time using
autoregressive models”, Conf Proc of the 16th Annual Confer-
ence of the Gesellschaft fur Klassifikatione, University of Dort-
[11] D. R. Drover, H. J. Lemmens, E. T. Pierce, et al, (2002) “Patient
State Index: titration of delivery and recovery from propofol,
alfentanil, and nitrous oxide anesthesia”, Anesthesiology, 82-89.
[12] J. V. Stone, (2004) Independent Component Analysis: A Tutorial
Introduction, Bradford Book.
[13] K. I. Diamantaras and S. Y. Kung, (1996) Principal Component
Neural Networks. Theory and Applications, Adaptive and Learn-
ing Systems for Signal Processing, Communications and Control,
John Wiley & Sons Inc., New York.
[14] M. Turk and A. Pentland, (1991) “Eigenfaces for recognition”,
Journal of Cognitive Neuroscience, 71-86.
[15] S. Y. Kung, K. Diamantaras, and J. Taur. (1991) “Neural net-
works for extracting pure/constrained/oriented principal compo-
nents. In J. R. Vaccaro, editor”, SVD and Signal Processing El-
-sevier Science, Amsterdam, 57-81.
[16] M. P. S. Chawla, (2008) “A comparative analysis of principal
component and independent component techniques for electro-
cardiograms”, Neural Computing & Applications.
[17] Hyv¨arinen, A., J. Karhunen, and E. Oja, (2001) Independent
Component Analysis., John Wiley & Sons Inc., New York.
[18] R. Vigário, V. Jousmäki, M. Hämäläinen, R. Hari, and E. Oja,
(1998) “Independent component analysis for identification of ar-
tifacts in magnetoencephalographic recordings”, In Advances in
Neural Information Processing Systems, MIT Press, Vol. 10,
[19] S. Makeig, A. J. Bell, T. P. Jung, and T. Sejnowski, (1996) “In-
dependent component analysis of electroencephalographic data”,
Advances in Neural Information Processing Systems, MIT Press,
Vol. 8, 145-151.
[20] G. D. Brown, S. Yamada, and T. J Sejnowski, (2001) “Independ-
ent components analysis (ica) at the neural cocktail party”,
Trends in neuroscience, Vol. 24, 54-63.
[21] P. O. Hoyer and A. Hyv¨arinen, (2000) “Independent compo-
nent analysis applied to feature extraction from colour and
stereo images”, Network: Computation in Neural Systems, Vol.
11, 191-210.
[22] Parra, L., C. D. Spence, P. Sajda, A. Ziehe, and K.-R. M¨uller,
(2000) Unmixing hyperspectral data. In Advances in Neural In-
formation Processing Systems 12, MIT Press, 942-948.
[23] S. Hagihira, M. Takashina, T. Mori, T. Mashimo, and I. Yoshiya,
(2001) “Practical issues in bispectral analysis of electroencepha-
lographic signals”, Anesth Analg, Vol. 93, 966-970.