Development of an Interhemispheric Symmetry Measurement

in the Neonatal Brain

Ninah Koolen

1,2

, Anneleen Dereymaeker

, Katrien Jansen

, Jan Vervisch

, Vladimir Matic

1,2

Maarten De Vos

1,2,4

, Gunnar Naulaers

and Sabine Van Huffel

1,2

Department of Electrical Engineering (ESAT), division SCD, University of Leuven, Leuven, Belgium

iMinds-KU Leuven Future Health Department, Leuven, Belgium

Department of Pediatrics, University Hospital Gasthuisberg, Leuven, Belgium

Department of Psychology, University of Oldenburg, Oldenburg, Germany

Neonatal Intensive Care Unit, University Hospital Gasthuisberg, Leuven, Belgium

Keywords: Preterm Brain, Symmetry, Channel Symmetry Index, Spectral Power, EEG, One-class SVM, Classification.

Abstract: The automated analysis of the EEG pattern of the preterm newborn would be a valuable tool in the neonatal

intensive care units for the prognosis of neurological development. The analysis of the (a)symmetry

between the two hemispheres can provide useful information about neuronal dysfunction in early stages.

Consecutive and subgroup analyses of different brain regions will allow detecting physiologic asymmetry

versus pathologic asymmetry. This can improve the assessment of the long-term neurodevelopmental

outcome. We show that pathological asymmetry can be measured and detected using the channel symmetry

index, which comprises the difference in power spectral density of contralateral EEG signals. To distinguish

pathological from physiological normal EEG patterns, we make use of one-class SVM classifiers.

1 INTRODUCTION

Electroencephalogram (EEG) is a non-invasive and

sensitive tool for assessing cerebral function in

premature infants. The chronological changes in

EEG background with increasing postconceptional

age reflect the central nervous system maturation.

Standard values of maturational features in

premature EEG are already well described

(Hellström-Westas, 2005; Vecchierini, 2007;

Hayashi-Kurahashi, 2012; Le Bihannic, 2012).

Chronic EEG background abnormalities are strongly

associated with adverse neurological outcome in

both preterm and full-term infants (Hellström-

Westas, 2001; Okumura, 2002). These abnormal

patterns must be scored and interpreted visually and,

thus, subjectively. However, analyses and

interpretation of multichannel neonatal EEG is

difficult, requires expertise and is time consuming.

Therefore, objective criteria for the assessment

of EEG abnormalities have to be established and

automated background EEG analysis may contribute

to reliable interpretation (Palmu, 2010). In previous

research, we have defined features that can be

calculated for each time signal, in other words, for

each EEG channel independently (Koolen, 2013).

However, EEG is a multichannel measurement, and

all too often this spatial dimension is ignored.

Spatial 'integration' is usually limited to concatenate

the characteristics of different channels (Hunyadi,

2011). Here, we intend to exploit the available

spatial information to quantify the interaction

between the various areas of the brain using

connectivity analysis. Such connectivity values can

be highly relevant to make an accurate prognosis of

the neurological outcome of the premature babies,

since they are the mathematical quantification of the

degree of connectivity between different brain

regions. After all, it is commonly assumed that brain

areas in very young babies are barely connected, and

that during brain development through spontaneous

interaction a sophisticated network is formed

(Vanhatalo, 2006; Smyser, 2010). In this paper, the

interhemispheric symmetry is examined: can we

detect pathological asymmetry in

electrophysiological activity between two

hemispheres.

765

Koolen N., Dereymaeker A., Jansen K., Vervisch J., Matic V., De Vos M., Naulaers G. and Van Huffel S..

Development of an Interhemispheric Symmetry Measurement in the Neonatal Brain.

DOI: 10.5220/0004922407650770

In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods (ICPRAM-2014), pages 765-770

ISBN: 978-989-758-018-5

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

Interhemispheric asymmetry in premature infants is

defined as a persistent amplitude difference of 50%

over homologous areas in one hemisphere and must

be persistent to all behavioural states to be

significant (Holmes, 1993).



In addition,

abnormalities can be extracted from a difference in

the power of the frequency domain.



This

abnormality in background pattern is frequently

associated with lateralized pathology such as an

underlying anatomical or acquired brain lesion (e.g.

intraparenchymal haemorrhage, stroke, ischemic

insults and congenital brain malformations)

(Holmes, 1993; Van Putten, 2004).

Automated interhemispheric symmetry analysis

in preterm and term infants can be a valuable tool to

detect neuronal dysfunction in early stages.

Consecutive and subgroup analyses of different

brain regions (as detected in frontal, central,

temporal and occipital electrophysiological activity)

will allow detecting physiologic asymmetry versus

pathologic asymmetry. Correlation of this pattern

with long–term neurodevelopmental outcome has to

be defined.

In order to detect those abnormal patterns in a

classification process, we will use the channel

symmetry index (CSI) (Hunyadi, 2010). Both

amplitude and frequency content are taken into

account, since the CSI is based on the difference in

the power spectral bands of the contralateral

channels. Derived features from the CSI curves will

serve as input for the classification. Due to an

unbalanced dataset, we will make use of a one class

Support Vector Machine (SVM) (Schölkopf, 2000,

2001).

2 DATA ACQUISITION

The proposed method is tested on EEG

measurements, obtained with OSG equipment (OSG,

Belgium). The purpose is to observe the symmetry

between different brain areas. Therefore, we use

multi-channel or ‘full’ EEG taken at 9 electrode

locations (Fp1, Fp2, T3, T4, C3, C4, Cz, O1, O2).

The sampling frequency is 250 Hz. The dataset

contains EEG recordings of 47 newborns, born at a

postmenstrual age (PMA) of 24-40 weeks, including

patients with structural brain abnormalities (acquired

or congenital) and clinical convulsions. To assess

brain maturation, 20 preterm infants had several

EEG measurements at consecutive moments with

increasing PMA. This resulted in a total of 92 EEG

measurements. In this way, we want to score

physiologic brain symmetry in the developing

premature brain. On the other hand, we can score

brain symmetry in both preterm and term infants

with pathologic conditions. Data containing artefacts

(>30% of the whole measurement) were excluded

from this study. In consideration of symmetry

calculations between two EEG channels, signals of

only these two channels are excluded in case that

one electrode is disturbed. In other words, not the

whole full-EEG is excluded. The data was labelled

into two categories: pathologic asymmetry and

physiologic normal symmetry. The protocol was

approved by the ethics committee of the University

Hospitals of Leuven, Belgium. A pre-processing

step including a 50 and 100 Hz Notch filter and a 1-

20 Hz band pass filter was applied on the data.

3 METHODOLOGY

3.1 Channel Symmetry Index

First, we investigate detecting asymmetry between

different EEG channels based on a feature called

channel symmetry index (CSI) (Hunyadi, 2010).

This feature is defined as the power asymmetry in

predefined frequency bands and is calculated

between two contralateral channel pairs presented in

formula 1 (O1 vs. O2, C3 vs. C4, T3 vs. T4, Fp1 vs.

Fp2).

max

min

(,)

f ch f opp

fch fopp

PSD PSD

CSI ch F

PSD PSD











(1)

where ch is a chosen EEG channel and opp its

contralateral channel as can be found in Figure 1.

Figure 1: Electrode placements for 9 channel EEG

measurement, contralateral channels are given in white-

black (O1-O2, C3-C4, T3-T4, Fp1-Fp2).

The selected frequency bands correspond to the

clinically relevant frequencies to monitor brain

activity: delta band (1-4 Hz), theta band (4-8 Hz),

alpha band (8-13 Hz) and beta band (13-21 Hz). A

ICPRAM2014-InternationalConferenceonPatternRecognitionApplicationsandMethods

766

CSI value is calculated for each 150 seconds of the

EEG measurement, for each channel pair and each

frequency band. A mean value is obtained by

averaging the CSI values of the different frequency

bands (Figure 2a). Subsequently, a patient specific

box plot of these values over the whole EEG

measurement is determined (Figure 2b).

3.2 Outcome Dependent Features

For classification, we need features to distinguish

normal and abnormal EEG patterns. Based on the

box plots, we have worked with four features:

 Median box plot

 Interquartile range (iqr) of the box plot: a

statistical measurement to describe the spread

of the data. iqr is calculated as the difference

between the upper and the lower quartiles. This

feature is similar to the range, although it is

less sensitive to outliers, e.g. the movement of

an electrode.

 Standard deviation (std) of the boxplot:

variation from the average.

 Postmenstrual age (PMA): brain maturation

may have an influence on the median of the

box plot. The hemispheres connection is still

developing.

Figure 2: a. mean CSI values, averaged over frequency

bands, shown for different channel pairs; b. box plot mean

CSI.

3.3 One-class SVM Classification

A simple threshold on the median value of the

patient box plots is not sensitive enough, leading to a

lot of false positive detections. Therefore, we need

to incorporate the different features into a more

Figure 3: Different features for a normal patient (patient 1)

and a patient with hemimegalencephaly-haemorrhage

(patient 2), measurements taken at comparable PMA.

Figure 4: Number of normal/abnormal patients, number of

data excluded for analysis since artefacts are present in the

data.

complex classification system. Here, a classifier is

constructed and trained for every channel pair,

resulting in four classifiers. Unfortunately, one

classifier incorporating all features is not enough,

since pathologies can result in lateralization of only

one region of the brain. This means, the

hemisphere’s symmetry elsewhere in the brain can

be normal compared to patients of similar age. In

addition, there is a huge unbalance between the

number of normal and abnormal classes (Figure 4).

For this purpose, one-class SVM classifiers are

applied. It will give the value +1 in a small region

capturing the pathological class and -1 for all other

data points (normal class). The data is mapped in the

feature space corresponding to the radial basis kernel

function and to separate them from the origin with

maximum margin (Schölkopf, 2000). On which side

of the hyperplane a new point will fall in feature

space, will decide to which class this point will

belong.

The quadratic problem to separate the data from

the origin is solved (Schölkopf, 2001):

min

∈,∈





,∈



‖



‖

















subjectto∙













,



0

(2)

0 1000 2000 3000 4000 5000 6000 7000

0.2

0.4

0.6

Fp1-Fp2

mean CSI (over different frequency bands)

0.2

0.4

0.6

boxplot mean CSI

0 1000 2000 3000 4000 5000 6000 7000

0.2

0.4

0.6

C3-C4

0.2

0.4

0.6

0 1000 2000 3000 4000 5000 6000 7000

0.2

0.4

0.6

T3-T4

0.2

0.4

0.6

0 1000 2000 3000 4000 5000 6000 7000

0.2

0.4

0.6

time (seconds)

O1-O2

0.2

0.4

0.6

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

17 16

mean CSI for Fp1-Fp2

iqr1

median 1

median 2

patient 1

patient 2

iqr2

100

Abnormal

Normal

Artifact

DevelopmentofanInterhemisphericSymmetryMeasurementintheNeonatalBrain

767

Here, w and ρ are a weight vector and an offset to

parameterize the hyperplane in feature space

associated with the kernel (Schölkopf, 2000).

Outliers are those points defined as points on the

wrong side of the hyperplane (within margin ρ of the

origin). 



are the outlier distances from the

hyperplane.

Figure 5: Principle of the quadratic problem of the one-

class SVM (Fourie, 2011).

In order to choose the best subset of model

parameters, we have trained the hyper parameters of

this classifier, namely ν and γ. ν is an upper bound

on the fraction of training errors and a lower bound

of the fraction of support vectors. When ν decreases,

a misclassification will have a large impact on the

objective. This can also result in overfitting of the

data. First, ν is adapted from 0.05 to 0.2 in steps of

0.05, since it resembles how many outliers can be

without the class. For the data (Figure 4), this is a

percentage of around 10% for each group. γ is

constrained to a default value by

1/number_features=0.25 (Chih-Chung, 2011).

As we applied the RBF kernel, γ represents the

width of the Gaussian kernel, representing the

boundary to catch all the training data within the

class. Therefore, in a next step ν and γ are adjusted

simultaneously to find the optimal set of hyper

parameters. ν (range 0.05 to 0.2) is adapted in steps

of 0.05 and γ (range 0.1 to 0.7) in steps of 0.15.

We made use of leave-one-out 10-fold cross-

validation (loo-cv) to optimize the model

parameters. Thereby, a comparison of the accuracies

of the models built on each subset is carried out. Due

to the small sample at disposal, a result in term of

best loo-cv error is presented here, as a (necessarily

small) test set would not be very informative.

Moreover, within a small test set there is necessarily

huge variability. The loo-cv error is called the

specificity of the model (true negative rate). After

the model is trained, we can define the sensitivity as

the true positive rate. In other words, how many

abnormal EEG patterns have been found by the

SVM model.

4 RESULTS AND DISCUSSION

Results of the optimization of the four one-class

SVM classifiers are shown in Table 1. For the C3C4

classifier, ν = 0.15 is chosen to be the optimal value.

For the other 3 classifiers, ν = 0.1 gives the highest

sensitivity and specificity. Grid search for optimal

combination of γ and ν by adapting them

simultaneously resulted in the same optimal value

for the model parameter γ (=0.25). Due to the

relatively small sample size, both high sensitivity

and specificity are difficult to obtain. Therefore, we

set the minimum specificity on 70%, considering

that not too many false alarms can be given in case

there is no pathological pattern.

We want to emphasize that we want to detect

patients with lateralized lesions; cases confirmed by

clinical doctors through lesion(s) on MRI images.

For example, lateralized lesions can be

hemimegalencephaly or cases with convulsions.

These cases were found by all four optimized one-

class SVM classifiers. However, there are still some

false negatives in the C3C4 classifier and T3T4

classifier. That is, there exists always three false

negatives for the T3T4 classifier; abnormal patterns

which are not detected (Table 1). Two of these false

negatives are not detected by most T3T4 classifiers.

An explanation could be that one patient has corpus

collosum agenese, what affects more the asynchrony

instead of the asymmetry. The other false negative

has mainly asymmetry in only one frequency band

(theta), which is presumably not picked up by the

classifier as we work with the average over the

different frequency bands (CSI). In fact, clinical

doctors visually detected the lesion on the other

hemisphere on the MRI than they did on the EEG

measurement, indicating it is hard to specify the

lateralization. In addition, one patient with

pathologic asymmetry is never detected by the C3C4

classifier. The pathologic EEG pattern, comparing

the C3 and the C4 channel, is very short compared to

the length of the whole measurement. Moreover, the

lesion is located in the thalamus (=sub cortical),

which is less pronounced in the cortical EEG

signals. Overall, the Fp1Fp2 and the O1O2

classifiers are performing well, all pathologies are

detected.

ICPRAM2014-InternationalConferenceonPatternRecognitionApplicationsandMethods

768

Table 1: Specificity (%) of the four trained one-class SVMs with 10-fold cross-validation. Sensitivity (%) on detecting the

abnormal EEG patterns. For this small sample, we would go for ν =0.15 in case of the C3C4 classifier and ν = 0.1 for the

three other classifiers.

C3C4 Fp1Fp2 O1O2 T3T4

spec sens spec sens spec sens spec sens

0.05 67.12 42.86 (3/7) 61. 72 66.67 (2/3) 74.32 100 (6/6) 64.47 62.5 (5/8)

0.1 71.23 57.14 (4/7) 71.60 100 (3/3) 75.67 100 (6/6) 73.68 62.5 (5/8)

0.15 76.71 71.43 (5/7) 67.9 66.67 (2/3) 75.67 100 (6/6) 68.42 62.5 (5/8)

0.2 68.49 85.71 (6/7) 66.67 66.67 (2/3) 68.92 83.33 (5/6) 61.84 62.5 (5/8)

In future, other classification models can be applied

to this problem, which also take the unbalance

between classes into account. For example, weighted

least squares-support vector machines (LS-SVM)

can be trained (Cawley, 2006). In general, weighted

loss functions are appropriate to balance the

contribution of classes that are not equally

represented. Nevertheless, further improvement of

the applied one-class SVM model is possible by

adding more features into the training phase.

Thereby, we think of asymmetry defined in clinical

papers; a ratio of amplitude difference. Another

possibility is to search more localized in time, in

case of convulsions, instead of calculating features

over the whole EEG measurement. However, this

will probably lead to a higher rate of false positive

detections introduced by short-time artifacts. In

addition, more patients will be incorporated in future

to refine and tune the model. In this way, normal

values for the channel symmetry indexes can be

established, dependent on the physiologic

maturation.

5 CONCLUSIONS

The developed algorithm is a successful strategy to

detect abnormal lateralized lesions in the neonatal

brain. Based on non-invasive EEG measurements,

we can extract useful features to distinguish

physiological from pathological asymmetry.

Therefore, we have used characteristics derived from

the channel symmetry index as input features for a

classifier. Moreover, automated assessment creates

possibilities to look over a longer period of time in

an objective way including the experience of clinical

doctors. Future work will focus on fine-tuning the

algorithm based on a larger dataset, adding clinical

relevant features to the classifier, and trying out

other weighted SVMs. Measuring and analyzing

connectivity in the neonatal brain is of added value

and high interest for the overall assessment in the

Neonatal Intensive Care Units for EEG diagnosis.

ACKNOWLEDGEMENTS

I want to thank M. Signoretto, B. Hunyadi and M.

Milosevic for their valuable discussions. Research

supported by Research Council KUL: GOA MaNet,

PFV/10/002 (OPTEC), several PhD/postdoc &

fellow grants; Flemish Government: FWO: Postdoc

grants, projects: G.0427.10N (Integrated EEG-

fMRI), G.0108.11 (Compressed Sensing)

G.0869.12N (Tumor imaging) G.0A5513N (Deep

brain stimulation) IWT: PhD grants, projects: TBM

070713-Accelero, TBM 080658-MRI (EEG-fMRI),

TBM 110697-NeoGuard iMinds: SBO dotatie 2013,

ICONs: NXT_Sleep, FallRisk Flanders Care:

Demonstratieproject Tele-Rehab III (2012-2014)

Belgian Federal Science Policy Office: IUAP P719/

(DYSCO, `Dynamical systems, control and

optimization', 2012-2017); ESA AO-PGPF-01,

PRODEX (CardioControl) C4000103224 EU:

RECAP 209G within INTERREG IVB NWE

programme, EU HIP Trial FP7-HEALTH/ 2007-

2013 (n° 260777), EU MC ITN TRANSACT 2012

(n° 16679), ERC Advanced Grant: BIOTENSORS

(n° 39804), ERASMUS EQR: Community service

engineer (n° 539642-LLP-1-2013).

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