Automatic Burst Detection based on Line Length in the Premature

EEG

Ninah Koolen

1,2

, 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, Katholieke Universiteit 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: Brain Monitoring, Premature EEG, Automatic Detection, Burst, Interburst Interval, Neonatal Intensive Care

Unit.

Abstract: To extract useful information from preterm electroencephalogram (EEG) for diagnosis and long-term

prognosis, automated processing of EEG is a crucial step to reduce the workload of neurologists. Important

information is contained in the bursts, the interburst-intervals (IBIs) and the evolution of their duration over

time. Therefore, an algorithm to automatically detect bursts and IBIs would be of significant value in the

Neonatal Intensive Care Unit (NICU). The developed algorithm is based on calculation of the line length to

segment EEG into bursts and IBIs. Validating burst detection of this algorithm with expert labelling and

existing methods shows the robustness of this algorithm for the patients under test. Moreover, automation is

within our grasp as calculated features mimic values obtained by scoring of experts. The outline for

successful computer-aided detection of bursting processes is shown, thereby paving the way for

improvement of the overall assessment in the NICU.

1 INTRODUCTION

Premature infants are at high risk for neurological

disorders. Electroencephalography (EEG) indicates

both the nature and the location of the pathogenesis.

It would be very helpful, in addition to the visual

inspection of time-consuming EEG by

neonatologists, to develop an automatic algorithm

that quantifies the brain activities and its evolution.

Despite the fact that EEG is already widely used

for registration of brain processes for epilepsy

patients, (semi-)automated monitoring of

quantitative EEG variables and its validated use is

almost nonexistent. Moreover, there is a high need

for automatic analysis of the neonatal EEG to

significantly reduce the workload of clinicians in the

NICU. In this paper, an algorithm is developed for

premature infants. For these patients it is very

important to monitor EEG within the first six hours

after birth to make an accurate prognosis on survival

quality and neurological outcome.

This diagnosis is based on the ‘hidden’ information

in the so-called background EEG activity. Critical

factors for prognosis are amplitude and the degree of

(dis-)continuities of the background EEG (Vanhatalo

and Kaila, 2006).

Furthermore, specifically abnormal patterns can

be observed. Discontinuous EEG pattern, or the so-

called trace discontinue, consists of bursts with high

frequencies and high amplitude, interrupted by

periods of low brain activity with low-voltage EEG,

named the interburst intervals (IBIs). It is believed

that long low-voltage periods give rise to an

increased risk of brain dysfunctions (Le Bihannic et

al., 2011). Nevertheless, good neurological outcome

can be expected if low-voltage activity recovers into

increasing activity between bursts and evolve to a

normal pattern within 12 hours after birth. However,

there is no golden standard for the description of

bursts in the literature, so validation of the detection

algorithm is subjective. In this way, validation

should be performed by more than one clinician and

105

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

Automatic Burst Detection based on Line Length in the Premature EEG.

DOI: 10.5220/0004186401050111

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2013), pages 105-111

ISBN: 978-989-8565-36-5

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

the experience of experts should be combined.

Earlier, automatic detection of bursts was often

only based on a threshold on the amplitude of the

EEG channels, but this has some drawbacks. High

frequency artefacts which are not filtered out can be

seen as bursts, whereas medication and filter settings

can also influence the amplitude of the EEG. Recent

studies include besides amplitude content also

frequency content of bursts and IBIs, e.g. they make

use of a non-linear energy operator (Särkelä et al.,

2002); (Palmu et al., 2010) or threshold detection on

the envelope of the EEG channels (Jennekens et al.,

2011).

The goal of this study was to implement a

reliable detection algorithm with the focus on the

advantage of combining amplitude and frequency

content. Therefore, line length is introduced for this

application, because it is very accurate in detecting

the onset of high activity in the EEG (Esteller et al.,

2001). Moreover, the developed method allows

defining an adaptive and patient specific threshold.

This avoids the limitations of changing amplitudes’

level, e.g. when medication is administrated.

Furthermore, no training set is needed. Such an

algorithm in the NICU would allow a more objective

analysis. Future research will also investigate the

influence of additional artefact removal as proposed

in (De Vos et al., 2011) on the accuracy of burst and

IBI detection. The computer-aided analysis of the

EEG enables to reduce the cost for the time-

consuming long-term analysis, and thereby reduces

the risk of brain damage of preterm infants.

2 DATA ACQUISITION

The EEG was measured with OSG equipment at 9

electrode locations (Fp1, Fp2, T3, T4, C3, C4, Cz,

O1, O2) and sampling frequency of 250 Hz. The

polysomnographic dataset included long-term video-

EEG recordings of 5 preterm infants with a

postmenstrual age of 24-32 weeks. Two patients had

measurements at 3 different moments in time to see

an evolution in the brain development. These

moments are as soon as possible after birth, at day

14 and at the day when the patient could leave the

hospital. The protocol was approved by the ethics

committee of the University Hospitals of Leuven,

Belgium. First, a pre-processing step is performed; a

50 and 100 Notch filter and a 1-20 Hz band pass

filter are applied. After this step, twenty minutes of

each EEG were chosen for further analysis and

scored (burst/IBI) by two experienced clinicians.

3 METHODOLOGY

3.1 Detection based on Line Length

Fractal dimension (FD) is a promising method for

transient detection, requiring no prior knowledge of

the characteristics of the transient (Accardo et al.,

1996). As the dimension of a line is 1 and for a plane

2, the FD in EEG will always be between 1 and 2.

The more the line fluctuates, the more the plane is

‘covered’, so the more the FD increases. The line

length is a simplified version of the FD. Line length

is more successful than FD for burst detection. It is

also reported (Esteller et al., 2001) for the detection

of seizures in the EEG. The line length is the

running sum of the absolute differences between all

consecutive samples within a predefined window.

An efficient burst detection algorithm is derived

from this feature. The algorithm consists of the

following steps:

1. Segmentation of each EEG channel in

consecutive segments of 1 second, with an

overlap of 0.12 second (Figure 1) (Accardo et al.,

1996). To have reliable detection of transient

events like bursts, short duration segments are

necessary. Line length will grow as the data

sequence magnitude or signal variance increases.

Hence, it can be seen as an amplitude and

frequency demodulator (Esteller et al., 2001).

Figure 1: Partition of the EEG signals in overlapping

consecutive segments of 1 second. The overlap is 0.12

second.

2. For each segment i of each channel n, the total

length is calculated as in formula 1. L(i)

represents the line length value for each segment

i, calculated as the sum of the distances between

successive data samples x

within this segment.

250-1

j+1 j

j=1

L(i)= x -x



(1)

After that, these line lengths are normalized by the

total sum of the line lengths of that EEG channel n

(Accardo et al., 1996):

L(i)

L(i)=

L(i)



(2)

BIOSIGNALS2013-InternationalConferenceonBio-inspiredSystemsandSignalProcessing

106

Finally, the median value over all channels is

taken for each segment (median L

(i)), which is

the (blue) curve in Figure 2 part c. Median value

is more robust than the mean value because high

amplitude or frequency content in only one

channel would influence the mean value too

much. Thereby, experts define bursts as high

activity on more than one channel (or more than

half of the EEG channels).

3. Bursts are detected when the amplitude of this

curve (median L

(i)) is above Thr_Det. This

patient dependent threshold is calculated as

0.85*mean of this curve. An additional condition

for detection is that the difference in amplitude

between a successive non-detected and detected

point (and vice versa) should be large enough

(>0.4*std(median L

(i)), so only pronounced

peaks are detected. Additionally, all IBIs shorter

than 2 seconds are removed as they are also not

considered by clinical experts. For an example of

65 seconds EEG, this leads to detection of the

segments indicated by red circles in Figure 2 c.

In part b of Figure 2, the detected bursts are

compared with clinical labelling of high activity.

Figure 2: a) Example of 65 seconds 9-channel EEG

recording, b) Burst detection: by 2 clinicians and by

algorithm, c) Blue curve: median L

(i) as calculated in step

2, grey line: threshold for detection of bursts (Thr_Det),

red circles: detected bursts after step 3.

3.2 Comparison of Detection Methods

Jennekens et al.(2011) first calculate the envelope

values EV(i), which are derived from the average

signal power P(i) as in formula 3. x(i) is the

amplitude of the signal and Nw a window length

equal to the number of sample points in 1 second of

data. When these envelope values are obtained for

every EEG channel, an amplitude-threshold is

applied. If point Ev(i) has a value higher than this

threshold on two or more channels, this sample x(i)

is detected as a burst sample. IBIs shorter than two

seconds are removed.

i=1

EV(i)= 2P(i) = x(i)



(3)

Another non-linear method is explored. It makes use

of the non-linear energy operator (formula 4)

(Palmu, 2010), where i is the current sample and x(i)

the value at that sample.

LEO(x(i))=|x(i)x(i-3)-x(i-1)x(i-2)|

(4)

Thereafter, the processed signal is smoothed by the

average value of a sliding window of 1.5 second

centred at the time sample NLEO(x(i)). To remove

continuous artefacts, a baseline correction is done by

subtracting the minimum value of the smoothed

signal from 1 minute epoch before the current

sample. As in the previous method, marking as a

burst is performed when the sample has a value

higher than a predefined amplitude on two or more

channels.

We compared those two methods with the

developed burst detection algorithm, by comparing

the accuracy of detection (to clinical labelling) and

different features describing these epochs. The

accuracy is calculated sample by sample, where a

true positive (TP) is found as a sample x(i) which is

both by the algorithm and by the clinician detected

as a burst. A true negative stands for a sample x(i)

which is marked as an interburst interval by the

expert and by the algorithm. Then, the accuracy is

calculated as in formula 5.

accurac

(

TP+TN

)

#sam

les

(5)

3.3 Features Describing Epochs

To see an evolution of the EEG pattern of the

premature brain, we will look at parameters which

describe bursts and IBIs. Namely, more bursts

indicate more activity and more connectivity

between neurons in the premature brain.

In this paper, several parameters were used to

compare epochs detected by different algorithms

with clinical detected epochs (Palmu et al., 2010):

- Number of Bursts / IBIs: number of these

specific epochs. It cannot be confused with the

number of points in the EEG time series

classified as bursts.

- Mean Burst / IBI Duration: average length of

the burst or interburst interval.

0 10 20 30 40 50 60

Fp2

Fp1

Time (s ec)

0 10 20 30 40 50 60

algorithm

clinician2

clinician1

time (s)

10 20 30 40 50 60 70

0.05

L median

segment i

AutomaticBurstDetectionbasedonLineLengthinthePrematureEEG

107

Acc(%)

pt1

Acc(%)

pt1

Acc(%)

pt2

Acc(%)

pt3

Acc(%)

pt4

Acc(%)

pt4

clinician 1 clinician 2 Inter-rater

Acc(%)

pt5

meth1 meth2 meth3 inter-rater

- Median Burst / IBI Duration: middle value

of a finite ordered list of these bursts or IBIs.

- Burst%: proportion of time covered by bursts.

Although these parameters are clinically relevant,

they do not fully summarize the patient’s state.

Therefore, some additional parameters are explored

(Särkelä et al., 2002) related to the energy within the

burst / IBI:

- Average Bursts / IBIs Amplitude: average

absolute amplitude values of the original EEG

samples which are detected as a burst or an IBI.

It is calculated for the bursts as in formula 6:

burst length

#bursts

i=1

#channels

j=1

n=1

|x(i)|

urst length

#bursts

#channels



(6)

- Average Energy Operator Bursts / IBIs:

averaged NLEO values characterize the burst

suppression pattern. Formula 7 presents how to

calculate this value for burst epochs.

burst length

#bursts

i=4

#channels

j=1

n=1

|x(i)x(i-3)-x(i-1)x(i-2)|

burst length

#bursts

#channels



(7)

4 RESULTS AND DISCUSSION

4.1 Accuracy of Detection Methods

Validation of the different algorithms is performed

by comparison of automatic versus manually

indicated bursts. In Figure 3, we present accuracy

for the three algorithms: 1. based on line length, 2.

based on envelope calculation and 3. based on

NLEO.

It can be seen that the developed algorithm

(meth1) performs similarly to the inter-rater

agreement in almost all patients. The mean accuracy

is respectively 83.8% for validation of meth1 and

86.5% for inter-rater agreement. For patient 3 the

inter-rater agreement is very high (90.6%) and

differs 5% from the automatic detection. In many

cases, NLEO-based algorithm (80.9% mean

accuracy) performs similarly to the method based on

line length calculation, but with the difference that

the first method has a computation speed of 4-5

times faster. This is because in the latter one, there is

a smoothing step. For one case, the NLEO method

has clearly a lower accuracy (68.6%). The EEG of

this patient has higher activity periods which are not

seen by clinicians as bursts. Because of a smoothing

step, this higher activity is smoothed out.

Afterwards, detection is performed with fixed

amplitude in contrast to the proposed patient

dependent amplitude (meth1).

The mean accuracy for meth2 (based on

envelope detection) is 78.8%. False positives are

introduced by movement artefacts as there is only an

amplitude threshold on the envelope values (around

30 µV). These are bursts detected by the algorithm,

but not by the expert. Besides that, a training phase

is needed for this algorithm to tune the different

parameter values for the algorithm settings, which is

not optimal here because the dataset is limited. This

method has around the same computation time as the

one based on NLEO. Hence, it can be concluded that

the developed algorithm is accurate, robust and fast.

Figure 3: Accuracy obtained by comparing sample by

sample clinical labelling with automatic detections of

bursts. In the first column labelling of clinical expert 1 are

compared with the labelling of the three methods, whereas

in the second column this is done for clinical expert 2. In

the third column labelling of both clinicians are compared.

This analysis is done for 5 patients, where pt1

stands for

the second measurement of patient 1 at day 14 and pt1

for

the measurement when the patient could leave the

hospital.

4.2 Comparison of Features

Figure 4 summarizes the calculated features for the

detection of bursts and IBIs by three previously

BIOSIGNALS2013-InternationalConferenceonBio-inspiredSystemsandSignalProcessing

108

Figure 4: Results for each feature after clinical labelling of bursts and applying different burst detection methods (meth1:

based on line length calculation, meth2: based on envelope calculation, meth3: using Non-Linear Energy Operator).

pt1 pt1_2 pt1_3 pt2 pt3 pt4 pt4_2 pt5

number of bursts / 2,5 minutes

pt1 pt1_2 pt1_3 pt2 pt3 pt4 pt4_2 pt5

3.5

4.5

5.5

6.5

7.5

8.5

median burst duration (s)

pt1 pt1_2 pt1_3 pt2 pt3 pt4 pt4_2 pt5

max burst duration (s)

pt1 pt1_2 pt1_3 pt2 pt3 pt4 pt4_2 pt5

averaged amplitude bursts (microV)

pt1 pt1_2 pt1_3 pt2 pt3 pt4 pt4_2 pt5

averaged energy bursts (microV

)

pt1 pt1_2 pt1_3 pt2 pt3 pt4 pt4_2 pt5

burst %

pt1 pt1_2 pt1_3 pt2 pt3 pt4 pt4_2 pt5

number of IBIs / 2,5 minutes

pt1 pt1_2 pt1_3 pt2 pt3 pt4 pt4_2 pt5

4.5

5.5

6.5

7.5

median IBI duration (s)

pt1 pt1_2 pt1_3 pt2 pt3 pt4 pt4_2 pt5

max IBI duration (s)

pt1 pt1_2 pt1_3 pt2 pt3 pt4 pt4_2 pt5

3.5

4.5

5.5

6.5

averaged amplitude IBIs (microV)

pt1 pt1_2 pt1_3 pt2 pt3 pt4 pt4_2 pt5

1.5

2.5

averaged energy IBIs (microV

)

clin1

clin2

meth1

meth2

meth3

Legend

AutomaticBurstDetectionbasedonLineLengthinthePrematureEEG

109

described methods and two clinicians. Mean

duration is not shown because it has similar values

as the median duration, where the median is more

robust. Detection is subjective from rater to rater,

what always leads to a difference between the values

between the raters. In other words, there is no golden

standard for the description of bursts and IBIs.

Nevertheless, a clear distinction between bursts

and IBIs can be found by looking at two features:

average amplitude and average energy operator.

Namely, the average amplitude for IBIs is smaller

than the average amplitude for bursts. This is always

the case, for all methods and for all raters. This is

also true for values of the average energy operator.

IBIs contain less energy than bursts.

Although burst% was considered as the

statistically most significant parameter for

correlations between all raters (Palmu et al., 2010),

representation of this feature in Figure 4 shows that

the correlation between values obtained by the

NLEO algorithm and the clinicians is not that high.

Especially the line length method gives a high

correlation.

Maximum IBI duration has been reported to

decrease as the postmenstrual age increases

(Hayakawa et al., 2001). In the present study, the

maximum IBI decreases from the first measurement

to the second measurement two weeks later for

patients 1 and 4 (Figure 4). Not only a decrease of

the maximum IBI duration, but also a decrease of

the median IBI value has a good prognostic value for

good neurological outcome. Values for this

parameter decrease as well for patients 1 and 4.

By checking differences between values obtained

for clinical and algorithm detection, it can be said

that the developed algorithm approximates well the

values of the parameters after the clinical detection.

5 CONCLUSIONS

The developed algorithm is a successful strategy to

detect patterns in the premature EEG, like bursts and

the intervals between them. The automated analysis

of EEG provides possibilities to look over a longer

period of time and over various records at different

points in time. Also, assessment of the evolution

over time of the unique characteristics of the EEG is

very valuable. Consequently, good approximation of

clinical features is of high importance. Thereby, it

aggregates the experience and trained eyes of more

clinical doctors and researchers in an overarching

model. Future work will focus on fine-tuning the

algorithm based on a larger dataset of validated EEG

segments. Additionally, more features and their

clinical relevance have to be explored. Such a

detection algorithm would dramatically improve the

overall assessment in the NICU for EEG diagnosis.

ACKNOWLEDGEMENTS

Research supported by

Research Council KUL: GOA MaNet, PFV/10/002

(OPTEC), IDO 08/013 Autism, several PhD/postdoc

& fellow grants;

Flemish Government: FWO: PhD/postdoc grants,

projects: G.0427.10N (Integrated EEG-fMRI),

G.0108.11 (Compressed Sensing) G.0869.12N

(Tumor imaging); IWT: TBM070713-Accelero,

TBM070706-IOTA3, TBM080658-MRI (EEG-

fMRI), TBM110697-NeoGuard, PhD Grants; IBBT;

MDV is supported by an Alexander von Humboldt

stipend.

Belgian Federal Science Policy Office: IUAP P7/

(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).

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