REGIONAL DIFFERENCES IN NEONATAL SLEEP
ELECTROENCEPHALOGRAM
Karel Paul 1), Vladimír Krajča 2), Zdeněk Roth 3), Jan Melichar 1), Svojmil Petránek 2)
1) Institute for the Care of Mother and Child, Prague, Czech Republic
2) Faculty Hospital Bulovka, Department of Neurology, Prague, Czech Republic
3) National Institute of Public Health, Prague, Czech Republic
Karel Paul
Institute for the Care of Mother and Child
14 710 Prague 4
Czech Republic
Tel. : +420 296511498 Fax. : +420 241432572 e-mail: [email protected]
ABSTRACT
Background and purpose: While EEG features of the maturation level and behavioral states
are visually well distinguishable in fullterm newborns, the topographic differentiation of the
EEG activity is mostly unclear in this age. The aim of the study was to find out wether the
applied method of automatic analysis is capable of descerning topographic particulaities of
the neonatal EEG. A quantitative description of the EEG signal can contribute to objective
assessment of the functional condition of a neonatal brain and to rafinement of diagnostics
of cerebral dysfunctions manifesting itself as “dysrhytmia”, “dysmaturity” or
“disorganization”.
Subjects and methods: We examined polygraphically 21 healthy, full-term newborns during
sleep. From each EEG record, two five-minute samples were subject to off-line analysis and
were described by 13 variables: spectral measures and features describing shape and
variability of the signal. The data from individual infants were averaged and the number of
variables was reduced by factor analysis.
Results: All factors identified by factor analysis were statistically significantly influenced
by the location of derivation. A large number of statistically significant differences was also
found when comparing the data describing the activities from different regions of the same
hemisphere. The data from the posterior-medial regions differed significantly from the other
studied regions: They exhibited higher values of spectral features and notably higher
variability. When comparing data from homotopic regions of the opposite hemispheres, we
only established significant differences between the activities of the anterior-medial regions:
The values of spectral features were higher on the right than on the left side. The activities
from other homotopic regions did not differ significantly.
Conclusion: The applied method of automatic analysis is capable of discerning differences
in the sleep EEG activities from the individual regions of the neonatal brain.
Significance: The capability of the used method to discriminate regional differences of the
neonatal EEG represents a promise for their application in clinical practice.
Keywords: Full-term newborn; EEG; Regional differences; Automatic analysis
INTRODUCTION
When analyzed visually, the EEG activity of sleeping full-term newborns at the first
glance appears topographically non-differentiated for the most part. The EEG atlases
dealing with the earliest age either do not mention the regional differences in the EEG
signal in full-term sleeping newborns at all [1,2], or the EEG activity of these infants is
being described as „uniformly distributed‟ [3]. The reason for this is probably the fact that
the human eye will not discern differences in the activities from the individual cranial
regions. However, the application of computing technology has proven that the
electroencephalogram of a full-term newborn is in fact topographically differentiated. The
regional differences in the values of spectral energies were described [4-8].
Intrahemispheric and interhemispheric coherence of the EEG activity had been studied [8-
14]. Automatic brain mapping was applied to the neonatal EEG [15,16]. Topographic
interdependencies of the neonatal EEG have been examined by the means of non-linear
methods [17-20].
In the present study, we have applied a multi-channel automatic method based on
adaptive segmentation [21] in order to describe the EEG activity from the specific regions
of the neonatal brain. This method evaluates not only spectral measures but also additional
features as amplitude level, shape and variance of the signal, in which it comes close to
visual analysis. The objective of the study is to verify whether the applied method is capable
of discerning the differences in the EEG activities of the specific brain regions. It is possible
to suppose that if the used method is able to discerne physiological regional EEG
differences it will be possible to use the method in a detection and objective description of
topographic deviations in patients with a cerebral pathology.
SUBJECTS
We included 21 healthy full-term newborns in the study. They were born in the 39th
to the 40th
week of gestation, the Apgar score was >7 in the first minute and >8 in the fifth
minute, and their birthweights ranged from 3010 to 3950g. The infants were examined in
the 4th
and 10th
day of their life. Parents of the infants were informed of the methods and
purposes of the examination and gave their consent. The project was approved by the
institute‟s ethical commission.
METHODS
The examinations were carried out in an EEG laboratory in standardized conditions
after morning feeding and lasted 90-120 minutes. The examination room was noise-
protected and background noise level did not exceed 45dB. The illumination level was
reduced to a degree that would enable the observer to just perceive changes in infant‟s
behavior. Room temperature was in the 23-25°C range. Disturbing environmental stimuli
were excluded. Infants were examined in a crib, placed in supine position. The EEG activity
was recorded polygraphically from eight bipolar derivations, positioned under the system
10-20 (Fp1-C3, C3-O1, Fp1-T3, T3-O1, Fp2-C4, C4-O2, Fp2-T4, T4-O2); the reference
derivation, linked ear electrodes; filter setting, 0,2 and 60Hz; sensitivity, 100μV per 10mm.
The respiration (PNG), ECG, EOG, and EMG of chin muscles were also recorded.
Electrode impedances were not higher than 5kOhm. The recording was performed using the
Brain-Quick (Micromed) digital system with sampling frequency of 128Hz and the data
were stored on CDs. An observer continuously recorded any change in infant‟s behavior on
the polygram.
Two five-minute-samples free of artifacts (segments contaminated by artifacts were
eliminated by visual inspection) were selected from the EEG record of each infant. One
sample was chosen from the middle part of quiet sleep, the other from the middle of the
subsequent active sleep. In this study, we have defined mentioned sleep states according to
the following criteria: Quiet sleep was defined as sleep with closed eyes, absence of eye
movements, regular breathing, absence of body movements except for startles, and the
typical EEG pattern „tracé alternant‟. Active sleep was defined as a behavioral state in
which the infant‟s eyes were closed or nearly closed, eye movements were apparent,
breathing was irregular, and mimic muscle movements, small movements of extremities and
even large generalized movements occurred intermittently. The EEG showed the „activité
moyenne„ pattern [3].
Quantitative processing of EEG was performed off-line. Subject to analysis were
data from the above-mentioned bipolar montage. A method based on multi-channel adaptive
segmentation [21] was used. The method was selected for the following reasons: (a) The
algorithm of the adaptive segmentation divides the EEG signal into quasi-stationary
segments of variable length. The idea was that the feature extraction from such relatively
homogeneous epochs would be substantially more effective than the feature extraction from
fixed epochs. This holds especially true when analyzing the highly variable pattern as tracé
alternant. (b) The division of the signal into quasi-stationary segments made it possible to
evaluate length, number and proportional occurrence of these segments and thus to quantify
the stability and variability of the signal.
The method of applied automatic analysis was explained in detail in our previous
paper [22]. Therefore it is described only briefly in this study. Using adaptive segmentation,
the EEG signal from each derivation was divided into relatively homogeneous segments of
variable length. The limits of the segments were in fact defined by the change in stationary
character of the signal. The segments were distributed into three classes according to their
maximum voltage. The segments whose amplitude didn‟t exceed 50μV were placed into the
1st class, the 2
nd class contained segments with voltage higher than 50μV and lower than
90μV, and the 3rd class was occupied by segments with the amplitude of 90μV and more.
Examples of the application of adaptive segmentation and the distribution of segments into
voltage classes are presented in Fig. 1. The activity of each segment was then described by
ten features. The AV feature described the variance of the segment‟s amplitude; Mm defined
the value of the maximum amplitude „peak-to-peak‟; the following five features provided
information about the value of spectral amplitude in five frequency bands, δ1 in the 0.2-
1.5Hz band, δ2 in the 1.6-3Hz band, θ1 in the 3.1-5Hz band, θ2 in the 5.1-8Hz band, α in
the 8.1-15Hz band; feature D1 described the steepness of the curve; D2 described its
sharpness; ØF informed about the average frequency of an activity in the segment. The data
of the features describing each segment were then averaged in each class, and for each class
three additional features were extracted: t% defines the time percentage of the specific class
occurence; No gives the number of segments of a specific class; L provides the information
about the average duration of the segments of a specific class in sec. In this manner the
automatic analysis provided 312 values (8 derivations x 3 classes x 13 features) from the
five-minute-sample of the analyzed EEG signal. An example of the numeric output of the
automatic analysis is presented in Table 1.
STATISTICAL ANALYSIS
The data collected from individual infants were averaged and the number of
variables taken into account was reduced by means of factor analysis. Using the principal
component analysis, three factors – Fc1, Fc2, Fc3 – were extracted, transformed by
Varimax rotation with the Kaiser normalization and the respective factor scores were
computed. Table 2 shows the list of factors identified by the factor analysis and the list of
features represented by the specific factors; furthermore the table shows data about the
eigenvalues of factors and the percentage of variance explained by these factors.
In the first phase of the statistical analysis we tested (a) the effect of brain region,
(the activity from each brain region is represented by a symbol of the individual bipolar
derivation: Fp1-C3, …, T4-O2), (b) the effect of voltage class (low-, mid-, and high-voltage
class), (c) the effect of sleep state (quiet and active sleep), and (d) the mutual statistical
dependences of these effects on all three factors using the method of General Linear Model;
the Wilks‟ multivariate test (λ) evaluated by means of F-test served as criterion.
Subsequently using the F-test, the effect of brain region upon each factor was tested
separately, as well as the effect of voltage class, the effect of sleep state and mutual
dependences of these effects.
In the next phase of the statistical analysis, in order to determine the differences
between the individual brain regions, we evaluated the vector of the 13 EEG features in
each voltage class separately both in quiet and in active sleep. Using the General Lineal
Model method, we employed the multidimensional analysis of variance, which further
modifies the calculations of comparative tests with regard of mutual correlations between
the 13 features, so that the final tests are not affected by these correlations. Following the
initial parallel analysis of the 13 features, we compared in detail the effects of individual
brain regions for each of the 13 features using the test according to Šidák. These tests for
the individual features serve as an explanatory supplement to the basic multidimensional
tests and they illustrate which brain areas and which features participate in the topographic
differences, and the direction of these differences.
RESULTS
The effect of brain region
By evaluating the effect of brain region we were testing the presence of topographic
differentiation of EEG activity. The influence upon the factors identified by factor analysis
are shown in Table 3. It is apparent that both the entire set of factors – Fc1, Fc2, Fc3 – and
even each individual factor are highly significantly influenced by the brain region. This
means that both the factor Fc1 representing above all spectral features, and the Fc2 and Fc3
factors, which represent non-spectral features, are influenced.
The effect of voltage class and dependence between the effects of brain region and voltage
class (Table 3)
When analyzing the effect of voltage class we were testing whether the studied
features of EEG signal differ significantly in individual voltage classes. We established that
the effect of voltage class significantly influenced the entire set of factors as well as each
factor in particular. We have also found a statistically significant dependence between the
effect of brain region and the effect of voltage class for all three factors together and for
each factor in particular, which points to the fact that the effect of brain region is different in
each voltage class.
The effect of sleep state and dependence between the effects of brain region and sleep state
(Table 3)
The sleep state also significantly influenced all the analyzed factors together as well
as each factor separately. We have also proven the presence of a significant dependence
between the effect of brain region and the effect of sleep state, documenting that the brain
region effect is influenced by the sleep state, for all the three studied factors as a whole and
for factors Fc1 and Fc3.
The effect of brain region on the EEG features
The results of the comparison of measured values of the EEG features between the
individual brain areas with respect to the voltage class and to the sleep state are depicted
synoptically in Fig. 2. First we compared the data from the specific regions of the given
hemisphere to one another, so that each region was compared to the other regions of the
hemisphere (Fp1-C3 vs. C3-O1, …, C4-O2 vs. T4-O2; the activities from the studied
regions are in this case represented by the symbols of the specific derivations). Then we
compared the data from the homotopic regions of the two hemispheres to one another (Fp1-
C3 vs. Fp2-C4, …, T3-O1 vs. T4-O2). In this way we have mutually compared the activity
from the total of 12 pairs of regions altogether. In each pair of regions we were comparing
39 pairs of items (13 features x 3 voltage classes). In the end we have acquired 468 items
(12 pairs of areas x 39 pairs of items) for each sleep state, which provide the information on
the occurrence of statistically significant differences between the compared data, or lack
thereof.
While comparing data describing the activities from the individual regions of the
same hemisphere, we have found a large number of significantly different values. (a) We
have found most differences between the activities of the anterior and posterior medial
regions (Fp1,2-C3,4 vs. C3,4-O1,2). It became evident that spectral features (AV, …, D2)
and the feature No have significantly higher values in posterior regions. On the other hand
the low voltage class values of the L and t% features were significantly higher in anterior
regions. (b) The differences in activities of the anterior and posterior temporal regions
(Fp1,2-T3,4 vs. T3,4 – O1,2) were distinguished by the following fact: While the majority
of spectral features (AV, …θ2) of the high-voltage and mid-voltage class reached
significantly higher values in the anterior regions, the values of the α, D1 and D2 features in
the mid-voltage and low-voltage classes were higher in the rear. The values of non-spectral
features from both regions mostly did not differ. (c) We established sleep state dependent
differences while comparing the values from the anterior-medial and anterior-temporal
regions (Fp1,2-C3,4 vs. Fp1,2-T3,4). In quiet sleep, the values of spectral features (δ2, …,
α) were higher in the medial regions, on the contrary in active sleep spectral features
presented higher values in lateral regions. (d) The differences between activities from the
posterior-medial and posterior-lateral regions (C3,4-O1,2 vs. T3,4-O1,2) were noted for
medially localized higher values of spectral features (AV, …, α). However in active sleep
the α, D1, D2 features exhibited higher values laterally. The non-spectral features L and t%
had in the low-voltage class significantly higher values temporally, while the values of the
t% and No features in the high-voltage class were higher medially.
The comparison of data from the homotopic regions of the opposite hemispheres
exhibited only small number of statistically significant differences. Only activities from the
right and left anterior-medial regions (Fp1-C3 vs. Fp2-C4) differed significantly from each
other: Most spectral features showed higher values on the right side. Lateral differences
between the other regions were rare.
Each feature contributed to the topographic differentiation to a different degree.
Features θ1, θ2, α, and δ2 exhibited the highest occurence of significantly different entries
in quiet sleep; in turn in active sleep, these were the features δ2, t%, D1 and α. In feature
ØF, we have encountered fewest significant differences.
CONCLUSION
The main objective of the study was to establish whether the applied method is
capable of discerning topographic particularities of the neonatal EEG. The statistical
analysis proved that the effect of the brain region influences all the factors, representing the
original measured EEG variables, in a highly significant manner. In this way it
demonstrated that the applied method is adequately sensitive and that it is capable to
distinguish regional specifities of the neonatal EEG. Beside that the statistical analysis
proved that all factors are significantly influenced by both voltage class and sleep state.
Mutual statistical dependences between the effects of the brain region and voltage class and
between the effects brain region and sleep state have been found.
Paired comparison of the data acquired from each region of the individual
hemisphere exhibited substantial number of significantly different values. Our findings are
thus in accord with the outcomes of the preceding studies, which suggest topographical
differences in the values of spectral energies [6-8,10] and in the EEG complexity [17-20].
Topographic differences in EEG activity are no doubt connected to the described
morphological differences of the individual regions of the neonatal brain [23, 24], to the
established regional variances in the brain metabolism [25, 26], as well as to the identified
local differences in the maturation of brain structures [27,28]. We found that spectral
features exhibited higher values in medial derivations than in lateral ones, and at the same
time higher values in posterior than in anterior regions. The above mentioned findings
apparently testify to a more advanced functional organization in the posterior-medial
regions of the brain cortex. The analysis of non-spectral features has shown that the low-
voltage and mid-voltage segments of greater length (L) occupy greater time percentage (t%)
in the activity of the anterior-medial and posterior-lateral regions. The activity of these
regions is therefore less changeable, more rigid, and apparently contributes decisively to the
low-voltage and mid-voltage part of the tracé alternant pattern.
While comparing the data measured in the homotopic regions of the two
hemispheres, we found greater number of significantly different values only between the
activities from the anterior-central regions. Right spectral features exhibited mostly higher
values than the same features on the left. When comparing the activities from the remaining
homotopic regions of the two hemispheres, we have not found any other marked
differences. Consequently it became evident, that when using our method, the neonatal EEG
activity appears predominantly symmetrical. Other authors have come to a similar
conclusion [8, 29, 30]. One of the probable causes of the bilateral EEG symmetry are the
connections running through the corpus callosum. The described symmetry of the EEG
activity can, however, also support the idea that the functional organization of the majority
of homotopic cortical regions is not yet laterally distinguished in the neonatal period.
In the present study, we have shown that the applied automatic method is capable of
discerning the differences in the EEG signals from the different regions of the neonatal
brain. We have also proven that the topographic differences in the neonatal EEG pertain not
only spectral measures, as it is evident from the preceding computer-aided studies, but that
the topographic differences also pertain the shape and variance of the EEG signal – a fact
that has so far solicited no attention. We believe that the discriminatory capabilities of the
used method represent a promise for its application in clinical practice.
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ACKNOWLEDGEMENTS
This work was supported by the research program “Information Society” under Grant No.
1ET101210512 “Intelligent methods for evaluation of long-term EEG recordings” , and by
Grant IGA MZ ČR 1A8600.
Fig. 1.
100μV; 1 sec
Fp1-C3
C3 -O1
Fp1-T3
T3 -O1
Fp2-C4
C4 -O2
Fp2-T4
T4 -O2
PNG
EOG
ECG
EMG
QS AS
QS
AS
Fig. 2
AV Mm δ1 δ2 θ1 θ2 α D1 D2 ØF t% No L AV Mm δ1 δ2 θ1 θ2 α D1 D2 ØF t% No L A A P P P P ┐3 Fp1-
C3 x
C3-O1
3┌ P P
P P P P P P P P P A ┤2 2├ A P P P P P P P
P P P P P P P P P A P A
┘1 1└
P P P P P P P P A A
A A A P A P P ┐ Fp2-C4 x
C4-O2
┌ P P
P P P P A P A ┤ ├ A A A P P P P P P
P P P P P P P P A A P A
┘ └
P P P P P P P P P A P A
A A A A A ┐ Fp1-T3 x
T3-O1
┌ A
P A A P P P P ┤ ├ A A A P P P
P P P
┘ └
A A A P P
A A A A A A A ┐ Fp2-T4 x
T4-O2
┌ A
A P P P ┤ ├ A A A P P P P
P P P P A
┘ └
P P P
L M M M ┐ Fp1-C3 x
Fp1-T3
┌
L M M M M ┤ ├ L L M L L L M
M M M M
┘ └
L M L L L L L
M M M M ┐ Fp2-C4 x
Fp2-T4
┌
M M M M ┤ ├ L L M L L L L M
M M M M L
┘ └
M L L L
L M M M M M M ┐ C3-O1 x
T3-O1
┌ M M M
L M M M M L M L ┤ ├ M L M L L M M
M M M M M M L L
┘ └
M M M M M L L L L
M M M M M M M M ┐ C4-O2 x
T4-O2
┌ M M M
M M M M M L L ┤ ├ M M M L L L M M
M M M M M L M L
┘ └
M M M M M L L L L L
D D D D D D S ┐ Fp1-C3 x
Fp2-C4
┌
D ┤ ├ D D D D D S
S D
┘ └
D
D D ┐ C3-O1 x C4-O2
┌
┤ ├
┘ └
┐ Fp1-T3 x Fp2-T4
┌
S ┤ ├
┘ └
┐ T3-O1 x T4-O2
┌
S ┤ ├ S
D ┘ └
Table 1
An example of the automatic analysis output
Fp1-C3
AV Mm δ1 δ2 θ1 θ2 α D1 D2 ØF t% No L
3: 29.5 119.0 134.1 94.4 54.0 32.5 15.7 25.3 26.3 2.4 21 27 2.4
2: 17.6 73.3 80.9 50.2 33.9 22.1 11.6 19.7 21.5 2.5 31 33 2.8
1: 11.1 44.7 50.0 28.0 18.1 11.9 6.7 12.4 14.8 3.0 48 37 3.9
Numerical data obtained by the analysis of a 5–minute-period of the EEG activity from the
channel Fp1-C3 in quiet sleep. 1,2,3, voltage classes; AV,…,L, features.
Table 2
Features' representation, eigenvalues and percentage of variance of factors identified by
factor analysis
Factors Representation of features Eigenvalues % of variance
Fc1 AV,Mm,δ1,δ2,θ1,θ2,α,D1,D2, ØF 7.38 56.76
Fc2 No,t% 1.49 11.47
Fc3 L 1.31 10.08
Table 3
The effects of brain area, voltage class and sleep stat upon the factors identified by factor
analysis and the effects' interactions
Effects Brain area Volt. class Sleep state Area x Class Area x
Sleep
F p F p F p F p F p
Factors
Fc1,Fc2,Fc3 4.76 < .001 317.00 < .001 298.66 < .001 6.86 < .001 2.27 =
.001
Fc1 3.60 < .001 610.09 < .001 46.21 < .001 2.51 = .002 3.06 =
.003
Fc2 5.19 < .001 258.64 < .001 436.92 < .001 13.16 = .037 1.24 =
.274
Fc3 6.33 < .001 412.28 < .001 452.85 < .001 4.72 < .001 2.50 =
.015