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A QEEG index of level of functional dependence forpeople sustaining acquired brain injury: The SevilleIndependence Index (SINDI)Jose Leon-Carrion ab; Juan Francisco Martin-Rodriguez ab; Jesus Damas-Lopez b;Juan Manuel Barroso Y. Martin a; Maria Del Rosario Dominguez-Morales ba Human Neuropsychology Laboratory, School of Psychology, Department ofExperimental Psychology, University of Seville, Seville, Spainb Center for Brain Injury Rehabilitation (CRECER), Seville, Spain
Online Publication Date: 01 January 2008
To cite this Article: Leon-Carrion, Jose, Martin-Rodriguez, Juan Francisco,Damas-Lopez, Jesus, Martin, Juan Manuel Barroso Y. and Dominguez-Morales,Maria Del Rosario (2008) 'A QEEG index of level of functional dependence for
people sustaining acquired brain injury: The Seville Independence Index (SINDI)', Brain Injury, 22:1, 61 - 74To link to this article: DOI: 10.1080/02699050701824143URL: http://dx.doi.org/10.1080/02699050701824143
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Brain Injury, January 2008; 22(1): 6174
A QEEG index of level of functional dependence for peoplesustaining acquired brain injury: The Seville Independence
Index (SINDI)
JOSE LEON-CARRION1,2, JUAN FRANCISCO MARTIN-RODRIGUEZ1,2,
JESUS DAMAS-LOPEZ2, JUAN MANUEL BARROSO Y. MARTIN1, &
MARIA DEL ROSARIO DOMINGUEZ-MORALES2
1Human Neuropsychology Laboratory, School of Psychology, Department of Experimental Psychology, University of
Seville, Seville, Spain and 2Center for Brain Injury Rehabilitation (CRECER), Seville, Spain
(Received 31 August 2007; accepted 23 November 2007)
AbstractPrimary objective: To find an easy-to-use, valid and reliable tool for evaluating the level of functional dependence ofan individual with brain damage who seeks a diagnosis of his/her functional dependence in daily activities.
Methods: Eighty-one patients with acquired brain injury (ABI) in post-acute phase, 40 traumatic brain injury (TBI) and 41cerebral vascular accident (CVA), were assessed using quantitative electroencephalography (QEEG) and grouped accordingto the FIM FAM scale. Discriminant analysis was performed on QEEG variables to obtain a discriminant function withthe best discriminative capacity between functionality groups.Results: Discriminant analysis showed classification accuracy of 100% in the training set sample and 75% in an externalcross-validation sample; 100% sensitivity and 100% specificity were reached. Coherence measures were the most numerous
variables in the function.Conclusions: These results point out that the discriminant function may be a useful tool in objective evaluations of patientsseeking a diagnosis of their level of dependence and that it could be included in current functionality assessment protocols.
Keywords: Traumatic brain injury, stroke, functional independence, QEEG, neuropsychological assessment, forensic assessment
Introduction
Neurological damage and acquired brain injury
affect a large portion of the population in western
countries. An estimated 1.5 million Americans
sustain traumatic brain injury (TBI) every year in
the US. Of these, 1.1 million are now living with
disabilities related to TBI [1]. In the European
Union, brain injury accounts for 1 million hospital
admissions per year. At discharge, most of these
patients show impairments in multiple areas, which
affect their ability to carry out daily life activities and
cause important legal, professional and personal
consequences.
In Europe, new laws are being established by
national governments to assist people in accordance
with their level of dependency. In Spain, a new law
was approved on 15 December 2006 called
The Law to Promote Personal Autonomy and toAssist People in a State of Dependence. This law
has created a bureaucracy and a medical system that
regulate promoting the autonomy of dependent
individuals as well as the state-funded care they
receive. As a result, medical and social resources are
needed to implement this law, which will serve more
than 2 000 000 people in Spain alone. More than a
third of these individuals are dependent in their daily
Correspondence: Jose Leon-Carrion, PhD, Human Neuropsychology Laboratory, School of Psychology, Department of Experimental Psychology, C/Camilo
Jose Cela s/n. University of Seville, Seville-41018. Spain. Tel: 34 95 457 4137. Fax: 34 95 437 4588. E-mail: [email protected]
ISSN 02699052 print/ISSN 1362301X online
2008 Informa UK Ltd.DOI: 10.1080/02699050701824143
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life activities due to neurological disorders, the more
prevalent being cerebrovascular disorders and TBIs.
Any individual in a state of dependency that asks to
be included in the new state-funded system must be
clinically evaluated to determine whether they are
dependent or not and, if so, at what level. If they are
clinically proven to be in a dependent state, then,
apart from the state-funded care, they may also
receive financial support and other benefits.
Under this new legal action, and in order to avoid
malingering, an objective evaluation of the func-
tional state of these patients is needed, with new
tools that offer high reliability and validity.
Nowadays, few objective instruments exist to eval-
uate independence in people seeking social and legal
assistance. Existent tools generally rely on the
subjective impressions of caregivers, whose inter-
pretations may distort results and thus provoke false
positives. Moreover, exaggerating and malingering
symptoms are very common among patients withacquired brain injury, especially when monetary
compensation is expected or in litigation, where
they make up 1520% of all cases [2]. Individuals
with mild brain damage and post-concussional
disorders constitute 40% of those seeking compen-
sation [3].
Functionality is considered a multidimensional
concept, where the whole cannot be explained by the
mere sum of its parts. Instruments that evaluate
functionality must be capable of detecting malinger-
ing. In current clinical practice, functionality assess-
ment evaluates both the level of independence in
basic tasks, such as eating or bathing, as well asin more complex and instrumental tasks, such as
getting on a bus. Thus, a full assessment has
multiple levels of complexity involving multiple
assessors. The need for objective functional assess-
ments would require summarizing this complex
concept in an index that informs on the impact of
certain medical conditions on the lives of patients.
In recent years, advanced technology has been
used to locate affected areas of the brain with greater
precision. The use of sophisticated imaging software
has made it possible to create defined cerebral maps
that can be adapted to a patients brain in order to
determine which functional areas are affected. In thefield of brain injury, this technology is needed in
order to make objective evaluations and determine
the severity of the condition. When applying
neuroimaging techniques to the assessment of brain
injury, the electroencephalography (EEG) plays a
crucial role, with its relatively low cost, simple
application procedure, high testre-test reliability
and inherent stability [47]. Moreover, the develop-
ment of mathematical tools and data visualization
has made it possible to quantitatively analyse the
human EEG, a technique known as quantitative
EEG (QEEG). Human QEEG measures have been
correlated with certain diagnostic categories, both in
healthy [8,9] and clinical populations [10]. By
meeting certain statistical requirements, these stu-
dies have obtained a set of QEEG variables, known
as discriminant functions, which can predict the
severity of a clinical condition. The advantages of
these measurement systems over others include their
low cost, speed, lack of cultural influence and
minimal human intervention in the analysis of the
results. These systems allow predictions to be made
on an individuals psychological and functional
characteristics based on specific physiological
variables.
In the field of brain injury, functional indepen-
dence is normally evaluated using clinical behaviour
scales, the most widely used being the FIM FAM
[11], the Glasgow Outcome Scale (GOS) [12] and
the latters extended version [13]. The first scale was
devised by adding FAM items to FIM in order toaddress functional assessment in the TBI popula-
tion; the second was designed to evaluate the general
state of patients after neurosurgery. These scales,
which proved reliable and easy-to-use, were later
applied to other areas of evaluation. This caused
serious drawbacks to their effectiveness, a common
problem being that both scales offered too few items
for evaluating higher psychological functions and the
possibility of malingering.
However, a recent study by van Baalen et al. [14]
found the FIM and FAM scales to be highly reliable,
sensitive tools for assessing TBI patients. The
authors stressed that both scales offered betterresults during the first phases of post-TBI recovery,
as their effectiveness diminished during post-acute
phases and rehabilitation (1 year after brain injury).
Studies that correlated sensitivity in long-term TBI
population (up to 10 year post-injury) reported low
contributions from FIM FAM and GOS in func-
tional status assessment [15]. A new tool based on
the neurophysiological profile of brain injury patients
could help overcome these limitations by establish-
ing a more robust measure for the diagnosis of
long-term brain injury population.
Studies on QEEG present a consistent and
common neurophysiological pattern associated withseverity of brain injury, which involves increased
slow band amplitudes, decreased fast band ampli-
tudes [16,17] and changes in EEG coherence [18].
The present study pays special attention to the
differences in these measures.
The aim of the present study is to find the linear
combination of QEEG variables, by means of the
discriminant analyses, with the best discriminative
capacity for functional states. These states include
complete dependence (total assistance in various
DLA), modified dependence and independence.
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The discriminant analysis helps identify the char-
acteristics that differentiate (discriminate) two or
more groups. One will create a function capable of
distinguishing between the possible members of each
group with highly accurate precision. For classifica-
tion, the discriminant function must have linearity as
one of its characteristics. This feature exists when
the function follows the proportionality of magni-
tudes of the entry and exit variables. When this
happens, the scores in this function of the modified
dependence group members should fall between
those of the complete dependence and independence
group members.
Methods
Participants
The total sample included 81 patients with acquired
brain injury (ABI), 40 of which were traumatic braininjury (TBI) patients and 41 cerebral vascular
accidents (CVA). Of these, 48 made it into the
training set for the creation of the discriminant
function and its subsequent internal validation. The
remaining 33 patients participation served to
validate the function externally. Patients in the
training set sample ranged in age from 1675
(M 40); 27 were TBI patients and 21 CVA; 37
were male and 11 female. All subjects were recruited
from the Centre for Brain Injury Rehabilitation
(C.RE.CER) in Seville, Spain. Admission criteria
were age (over 16) and a clinical background of ABI
caused by TBI or AVC, confirmed by neuroimagingtests (CT or MRI). Figure 1 shows sample size and
representation of the clinical sub-groups.
All of the patients were in chronic or sub-acute
phase when examined. The average time period
between brain injury and QEEG evaluation was
22 months (range 0.5119 months). They were
also participants in a holistic, integral and
multidisciplinary rehabilitation programme which
treats neuropsychological, physical and functional
sequelae derived from brain injury.
Procedure
Functional assessment: FIMFAM. The
FIM FAM is a multidimensional scale for func-
tional assessments, widely used in evaluating the
impact of rehabilitation on the ABI population. The
current version of this scale (FIM FAM) stems
from a combination of the FIM (Functional
Independence Measure) and the FAM (Functional
Assessment Measure). It consists of 30 items, 18
from FIM and 12 from FAM. These items are
grouped into seven sub-scales: self-care (items 17),
sphincter control (89), mobility (type of transfer)
(1013), locomotion (1416), communication
(1721), psychological adjustment (2225) and
cognitive functions (2630).The FIM FAM scales have been widely studied
and are considered a valid assessment tool for ABI
patients. Statistical studies found a reliability index
of 0.860.97 [18,19]. They have also shown reason-
able validity, internal consistency and discriminative
capacity between brain damaged sub-populations
[20]. These characteristics render the FIM FAM
scale one of the most popular instruments for
evaluating the functional state of neurological
patients.
Three independent assessors (a physical therapist,
a speech therapist and a neuropsychologist) com-
pleted the FIM FAM sub-scales. All items werescored from 17 points, where 1 indexes extremely
functional dependence and 7 indexes total functional
independence. The average interval between func-
tional assessment and QEEG was 1 day (SD 1.32).
Averages were calculated for each FIM FAM
sub-test, as well as the average of the FAM items,
the FIM items and total FIM FAM. Based on
Figure 1. Sample sizes and clinical features.
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these averages, three functional groups were created:
Complete Dependence (CD; range 12.99),
Modified Dependence (MD; range 35.99) and
Independence (I; range 67).
There were no significant differences between the
three functional groups in gender (p 0.448),
aetiology of ABITBI or CVA (p 0.111), age
(p 0.748) or time period from brain injury to
QEEG test (p 0.337).
EEG recordings. EEG recordings were carried out
in a softly-lit, sound-proof room, with room tem-
perature set at 23C. Before each recording, patients
were seated in a comfortable armchair or in his/her
wheelchair and asked to relax during the recording.
Impedance was kept below 10 k. Nineteen scalp
locations were taken into account, based on the
international 10/20 system [21], using linked ears
(A1 and A2) as a reference.The patient was asked (or helped) to close his/her
eyes (EC) and remain relaxed and alert while EEG
activity was recorded. In order to maintain vigilance,
a technician monitored each subject, inspecting the
EEG traces on-line and verbally alerting the
subject any time behavioural and/or EEG signs of
drowsiness appeared. Each recording lasted 3
minutes, with bandwidths of 0.1100 Hz and
256 Hz sampling frequency.
Data selection. Data pre-processing and filtering
were carried out offline in Matlab
(TheMathworks, MA) using EEGLAB (http://
www.sccn.ucsd.edu/eeglab/index.html) [22] and
custom scripts. Low-pass filters were located at
40 Hz and high-pass filters at 0.5 Hz. EEG record-
ings were visually edited to remove any visible
artifact. Continuous artifacts or artifacts present in
over 80% of overall time are generally due to the
presence of spasticity or any motor artifact in some
patients. In most cases, ocular movement in frontal
electrodes and muscular tension in temporal deriva-
tions caused these artifacts. The authors applied an
Independent Component Analysis (ICA), a proce-
dure which has proven reliability in removing signalartifacts [23]. A maximum of two components were
selected that isolated the artifacts, which were then
removed from the original recording. This process of
artifact removal was carried out on a total of 11
recordings (22%) out of 50. Subsequently, frag-
ments free of artifacts in the most representative
EEG sections were visually selected, using
Neuroguide 2.2.1 software (Applied Neuroscience,
Inc., St. Petersburg, FL), for a total of 120 seconds
of recordings. Only fragments with over 90%
reliability were used for the spectral analysis.
QEEG measures. The measures most widely used in
QEEG research are spectral patterns and connectiv-
ity patterns. The former are based on the analysis of
EEG signal frequency spectrum at a specific loca-
tion; they are independent of time and yield the
intensity of the electromagnetic field of that location.
The latter are more complex measures that involve
spatial-temporal characteristics and include various
locations, yielding the strength of the connections
between these brain regions.
Amplitude measures
The most basic amplitude measure is absolute
magnitude (amplitude), defined as the average
absolute magnitude (expressed in microvolts) of
the frequencies that make up a specific band, over
a given time period. The relative magnitude is the
average relative magnitude (expressed in%) of a
specific frequency band (the absolute magnitudedivided by total microvolt generated at a particular
location by all bands). The amplitude asymmetry is
defined as the amplitude difference between two
locations in a particular bandwidth. It is calculated
as follows: (A B)/(A B), where A and B are
different locations [24].
Connectivity measures
Connectivity measures (or amplitude independence)
are coherence and phase lag. Coherence indexes the
degree to which two areas of the cortex are
functionally linked. Statistically, this measure iscalculated as the likelihood that two random signals
will arise from a common generator process and the
frequency bands in which this occurs [25].
Coherence is calculated for each frequency band
and for each combination of the 19 electrodes.
Coherence is calculated as:
2xyf
Gxyf 2
GxxfGyyf
where Gxy(f) is the cross-power spectral density and
Gxx(f) and Gyy(f) are the autopower spectral
densities, respectively. Due to the complex analysesinvolved in this formula, the calculation is made with
the cospectrum (r for real) and quadspectrum (q for
imaginary). Thus, coherence is obtained using the
following formula [26]:
2xy
r2xy q2xy
GxxGyy
Phase lag is defined as the time it takes one
wavelength from a particular location to reach the
phase or maximum amplitude of another wavelength
originating from a different specific location.
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The contribution of each frequency band to the
EEG signal was obtained by means of the Fast
Fourier Transformation, FFT. The frequency bands of
interest were delta (13.5 Hz), theta (47.5Hz),
alpha (812 Hz) and beta (1225 Hz), including high
beta (25.530 Hz). Coherence and phase lag were
computed using cross-spectra analysis.
Statistical analyses
Descriptive analyses were conducted on both demo-
graphic and behavioural variables. A factorial analy-
sis (varimax rotation) was applied to each of the
eight measures that make up the FIM FAM scale
(all quantitative variables). The highest load factor
explained the total variance of 85.52%. Correlation
analyses were performed to study the relation
between the different measures, with positive results
showing Pearson correlation coefficients (r) higherthan 0.8. These two results allowed one to select the
total FIM FAM measure (average of seven mea-
sures) as the dependent variable for the subsequent
discriminant analysis.
The aim of this experimental design was to obtain
a discriminant equation, capable of classifying and
predicting the functional state of each patient based
on his or her QEEG pattern. The first objective was
to differentiate between the extreme groups (inde-
pendence vs complete dependence). After classifying
the two groups, the equation was tested on an
intermediate functional group (modified depen-
dence) in order to evaluate the hypothesis on thelinearity of functionality. The final step was to
classify a new group of patients using this equation.
The procedure for data reduction and creation of the
discriminant function is summarized in Figure 2.
Power spectral analyses yielded a total of 2755
variables as candidates for the EEG discriminant
analysis. Student t-tests were conducted to deter-
mine the discriminant capacity of each variable,
using the functional group of the patient as grouping
factor. Of the 2755 variables, 368 (11 absolute
amplitude, 40 relative amplitude, 98 amplitude
asymmetry, 184 coherence and 35 phase lag) were
significant (taking into consideration the application
of the Levene test). No correction for multiple
comparisons was applied, given that the objective
of this analysis was data reduction, i.e. to distinguish
between significant and non-significant variables
without making inferences from the analysis.
In order to reduce the data, a factorial analysis was
applied to each EEG measure (absolute amplitude,
relative amplitude, amplitude asymmetry, coherence
and phase lag). A Varimax rotation was applied.Variables with the highest load for each factor were
identified (three absolute amplitude variables, five
relative amplitude, 14 amplitude asymmetry, 18
coherence and 11 phase lag). The result was a total
of 51 variables. By using this two-level procedure, the
initial set of 2755 variables was reduced by 98.15%.
The next step involved performing a discriminant
analysis, using the Bayesian criterion in order to
adjust for the n difference between groups. A step-
wise method was used to obtain information on the
individual significance of each variable in the
discriminant function. The Wilks Lambda statistic
was used to assess each of the 51 variables. In orderto demonstrate the linearity of the discriminant
equation, this study analysed the correlation between
Figure 2. Procedure used to reduce data and create discriminant function.
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the FIM FAM scores and the corresponding
discriminant scores (DS). A one-way ANOVA for
independent measures was used to determine the
differences between the functional groups discrimi-
nant scores, with DS as the dependent variable and
functional group as grouping factor (three functional
groups: complete dependence, modified dependence
and independence). Post-hoc tests were applied to
analyse the differences between groups in pairs.
Results
Functional outcomes
The factorial analysis on the 11 variables of the
FIM FAM scale (eight measures, FIM items,
FAM items and overall FIM FAM) produced a
factor that explained the total variance of 85.52%.
Furthermore, correlations between these variables
reached significance (p
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Since this procedure tends to over-estimate the
precision of the classification, we used the leave-one-
out method of classification. This study observed
that the global classification accuracy is near 100%
(see Table IV).
Figure 4 shows the distribution of each functional
group and lineal adjustment of the DS obtained with
the function. The lineal adjustment is correct and
there is distinction between the groups.
The clinical validity of the DS was tested by means
of the Pearson correlation for each FIM FAM
variable. Figure 5 shows the results of this correla-
tion analysis. All correlations reached significance
(all ps
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showed statistically significant differences between
the Independence and Complete dependence groups,
using the t-test. A chi-squared analysis was performed
with adjusted standardized residual analysis to inter-
pret the signs of the correlations. This procedure was
applied only to the measures of absolute amplitude,
coherence and phase lag. It was not applied to the
measures of relative amplitude and asymmetrical
amplitude due to the difficulty of interpreting signs of
the correlations for these measures. Table V sum-
marizes the results of the correlations betweenabsolute amplitude, coherence and phase lag and
the FIM FAM scores. The correlations of the
coherence variable are significantly higher in
number than those of absolute amplitude and phase
lag (2 142; p
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Figure 5. Diagram of distribution and lineal adjustment between DS (x-axis) and each FIM FAM sub-scale (y-axis). The colour code
represents the patients FIM FAM scores, red being the lowest score and blue the highest.
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highest extreme. Positive correlation and multipleregression analyses supported the linearity hypoth-
esis. The significant positive correlations between the
DS and the other measures of the FIM FAM sub-
scales attested to the clinical validity of the function.
In addition, EEG frequency-based analysis
showed a pattern of the differences in EEGs between
dependence and independence patients. The pattern
for dependent individuals was characterized by an
increase in slow wave amplitude and a decrease in
fast wave amplitude. Although the slow wave
increase was generalized, there appeared to be a
Figure 6. Mean DS for the three functional groups: Complete dependence, Modified dependence and Independence. The mean DS for the
Modified dependence group falls between the DS of the other two groups. (Overlapped) Diagram of distribution and lineal adjustment
between DS (y-axis) and global FIM FAM scores (n 48). The colour code represents the patients FIM FAM scores, red being
the lowest score, green being the mid-range and blue the highest.
Figure 7. (Left) Predicted values for the FIM FAM variable for all patients (n 48) based on the multiple regression analysis (y-axis) and
the DS (x-axis). (Right) Predicted values for the FIM FAM variable for all patients (n 48) based on the multiple regression analysis
(x-axis) and the original FIM FAM scores (y-axis).
Table V. Discriminant analysis classification of Independence
and Complete Dependence groups, based on the leave-one-out
method.
Classification,% as
FIM FAM group N Independence
Complete
dependence
Independence 14 100 (n 14) 0
Complete dependence 15 6.7 (n 1) 93.3 (n 14)
Overall classification accuracy 96.65%.
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greater presence of these waves in the left hemi-
sphere. The decrease of fast waves was generalized in
the group with the highest level of dependence. This
finding is in accordance with the literature on brain
injury EEGs. A recent study [27] found that TBI
patients with a lower response level showed a slower
EEG than patients with higher responsiveness.Moreover, a generalized presence of slow waves in
the EEG has been identified as a factor in negative
prognoses [17,28].
Specific weight of QEEG variables (type, location
and band)
To create the discriminant function an absolute
amplitude variable and an amplitude asymmetry
variable were used. No relative amplitude variable
was used. The connectivity measures (coherence and
phase lag) did, however, take on an important role in
obtaining the function. These measures illustrate the
connections between different areas of the cerebral
cortex. They also quantify the cortico-cortical
coupling, which indicates the level of functional
connection between two areas.
Analyses on the variables which best distinguished
between the two functional states identified those
associated with coherence as the most effective.
Consequently, this study will now concentrate onthe significance of these variables.
It is known that areas of the brain are connected to
one another at certain levels. When two areas show
low coherence, they are functionally disconnected;
when they show very high coherence (hypercoher-
ence), they are excessively connected. In both
situations, a cerebral connectivity deficit is implied.
In the discriminant function, four coherence vari-
ables are included in three different frequency
bands. These four variables, all of which are long
distance connections, involve various cerebral lobes.
The discriminant function demonstrates the impor-
tance of long distance fibres that connect distantregions of the brain. Since level of dependence/
independence is a global measure, one would not
expect one single location in the brain to be respon-
sible for this function. It is more feasible to suggest
that there are wide, interconnected brain circuits
that account for this complex function. This finding
corresponds with the models of a functional brain
proposed by Hebb [29], Luria [30] and Fuster [31].
Another interesting result regarding localization is
that middle line locations (Fz, Cz, Pz) intervene in
both coherence variables. Studies on responses to
Figure 8. Interpolation of 19 t-scores for five frequency bands on a cortical topographic map. (*) indicates significant t-values (p
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diverse paradigms in these locations, by means of
other neurophysiological techniques (see evoked
cognitive potentials, e.g. P300 or N400), have
shown their importance in processing information,
as well as their sensibility to diverse states of
pathological consciousness due to traumatic brain
injury (TBI) [32].
Measures involving frontal locations are also of
particular importance to the function. At least twofactors can help explain this. First, the proportion of
frontal lobe cortex is comparatively higher than that
of the other lobes. Therefore, it is probable that any
discriminant analysis will have more locations in this
area. Secondly, the frontal lobe is particularly
vulnerable to injury, especially in TBI cases, and
thus shows a higher number of irregularities in the
QEEG pattern. Neurophysiological studies have
found that the frontal regions of the brain show
greater measures of coherence than the posterior
regions [33, 34]. This could be due to the fact that
the frontal cortex favours long distance connections,
while the posterior cortex participates more in localprocesses.
In delta band coherence measures, a positive
correlation was found with the FIM FAM scores,
i.e. the higher the coherence in this band, the greater
the functionality measure. Beta and high beta
frequency bands correlated negatively with the
FIM FAM scores, i.e. the lower the coherence in
these bands, the lower the functionality. Both results
confirm previous findings related to the present EEG
pattern. They also support the idea that both
slow and fast frequencies bands must be considered
as a set in order to correctly interpret QEEG as a
measure of functionality.
Sensitivity, specificity and cross-validation of the
discriminant function
Results show that the discriminant function is
capable of clearly discriminating between different
functional states of dependence in ABI patientsduring post-acute phase and rehabilitation.
Moreover, the resulting function can classify these
patients with a high level of efficacy. It also has an
effective predictive capacity, as shown by its highly
accurate cross-validation. Results also confirm the
linearity of the discriminant function, which, accord-
ing to its indexes, classifies patients on a dimension
where the two extremes represent complete
dependence and complete independence and
whose mid-range values correspond to patients
with intermediate levels of functionality.
The obtained discriminant function in this study
offered 100% sensibility (i.e. [true positives (15)]/[false negatives (0)] [true positives (15)] (14/
15)*100 100%). Specificity also reached 100%
(i.e. [true negatives (14)]/[false positives (0)] [true
negatives (14)] (14/14)*100 100%). The loga-
rithm had a Positive Predictive Value (PPV) of
100%, meaning that all patients whose FIM FAM
scores indicated complete dependency were categor-
ized as such by the logarithm. In this case, the
logarithm had a Negative Predictive Value (NPV) of
100%, indicating that no patient was identified as
complete dependence when the QEEG is negative
Figure 9. (Left) Scatterplot of distribution and lineal adjustment between DS (y-axis) and global FIM FAM scores (n 80; training
sample external sample). The colour code represents the patients FIM FAM scores, red being the lowest score, green being the mid-
range and blue the highest. The correlation is R 0.826 (p
8/2/2019 2008 Qeeg Index
14/15
(the patient is not diagnosed as complete
dependence).
Cross-validation of the discriminant function was
done using the leaving-one-out method of classifica-
tion, with 95% accuracy. Moreover, all correlations
between discriminant scores and FIM FAM vari-
ables were significant, as were those between
discriminant scores and the predicted scores from
the multiple regression analysis. Finally, discrimi-
nant indexes were compared to the group of patients
with moderate dependence. Results showed, as
predicted, that this group was situated between the
independence and complete dependence scores.
This data shows a lineal relationship between
QEEG variables and the functional capacity of
patients with acquired brain injury.
Validity of QEEG as a tool for evaluating the functional
state of a patient with acquired brain injury
Finally, the aim of this study was to obtain an index
of the functionality of patients in post-acute phase.
Contrary to other studies, where functionality is
assessed in the acute phase [9, 35], this study focuses
on the post-acute phase, considering that brain
injury is a dynamic process which entails cerebral
restructuring, progress and deterioration. The focus
is also on rehabilitation and the potential for
recovery of each patient with acquired brain injury.
Consequently, the development of a discriminant
function was seen, with data on patients from the
post-acute phase, as more sensible and stable:
sensitive, because it reflects the current functionalstate of the patient with greater clarity and precision;
stable, because non-treated deficits that persist 6
months post-TBI normally are considered possible
sequelae. These assumptions are based on the big
bump theory of brain injury, which hypothesizes
that residual pathology or compensation after brain
injury could be detected months and years later
using QEEG [10].
Limitations of the discriminant function
Certain considerations should be taken into account
when using the discriminant function. First of all, itcan only be used on patients similar to those used
to design the function, that is, TBI or CVA patients
in post-acute phase (over 6 months post-injury).
Secondly, and no less important, the SINDI is for
use as a complement, not as a substitute, to
functional assessment.
Concluding remarks
The data clearly shows that the discriminant func-
tion obtained in this study is a tool capable of
discriminating between different functional states
of dependence in ABI patients during post-acute
phase and classifying them with a high level of
accuracy. The function offers 100% sensibility and
100% specificity, a PPV of 100%, a NPV of 100%
and a cross-validation of 95% accuracy. These
results attest to the functions usefulness in providinga QEEG index for the assessment of patients seeking
a diagnosis of their dependence state, which in turn
could be included in current functionality assess-
ment protocols.
Acknowledgements
Presented as an oral communication to the Society
of Applied Neuroscience Inaugural Meeting, 1319
September 2006, Swansea, Wales, UK.
This study was conducted in the Centre for
Brain Injury Rehabilitation C.RE.CER. in collabora-tion with the University of Seville.
Supported by the Ministry of Science and
Education as part of the National Plan for Scientific
Research, Development and Technological
Innovation (20042007) and co-funded by the
European Regional Development Fund (ERDF):
FIT-300100-2006-77.
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