AUTOMATED BURST DETECTION IN NEONATAL EEG

Sourya Bhattacharyya

, Jayanta Mukhopadhyay

, Arun Kumar Majumdar

Bandana Majumdar

, Arun Kumar Singh

and Chanchal Saha

Indian Institute of Technology, Kharagpur, West Midnapore 721302, West Bengal, India

SSKM Hospital, Kolkata 700020, West Bengal, India

Keywords: Electroencephalogram (EEG), Neonatal Intensive care Unit (NICU), EEG Burst Suppression, Amplitude

integrated EEG (aEEG).

Abstract: Presence of burst suppression pattern in neonate EEG is a sign of epilepsy. Detection of burst patterns is

normally done by visual inspection of recorded raw EEG or amplitude integrated EEG signal. Existing

automatic burst detection approaches consist of either supervised learning mechanism or static energy

threshold based comparison. Both approaches can produce inconsistent results for babies with different ages

(for example, a neonate EEG and a six month old baby EEG). That is because, EEG signal amplitude or

energy increases according to baby’s age. Training based classifiers or static thresholds cannot adapt with

this amplitude variation. Here we propose an automatic burst detection method, which first computes signal

parameters such as energy, variance and power spectral density. From generated signal data, so called low

level amplitude or energy output is used as a ground reference for indication of signal suppression level.

Burst is identified according to high deviation of parameter values from those in suppression pattern. It does

not need any static threshold based comparison. Results show that our algorithm exhibits greater sensitivity

and equal specificity than existing methods. Due to adaptive thresholding for burst detection, our method is

applicable for analyzing EEG signals of babies with different ages.

1 INTRODUCTION

Electroencephalogram (EEG) monitors cerebral

electrical activities through electrodes placed on

scalp and provides a sensitive real time graphical

representation of brain function. Especially for

neonates, neurophysiological disorders and seizures

are mostly diagnosed by visual inspection of EEG

signals. Reason behind that is, unlike the seizure

cases in adults or matured children, neonates

commonly do not exhibit clinical sign and symptoms

(Connell et. al., 1989) for seizures. Thus visual

inspection of EEG for monitoring seizures is a

standard of care for most neonatal intensive care

units (NICUs) around the world (Rennie, 2008;

Sanei, 2007).

Burst suppression pattern is one of the typical

abnormal EEG patterns which are seen in neonatal

seizures. It is a pattern of high amplitude activity

interrupted by relatively low amplitude activity

typically less than twenty micro volts peak-to-peak.

High amplitude activity is termed as burst, whereas

low amplitude activity is termed as suppression.

Together, the burst-suppression patterns usually

have duration of a few seconds. They occur in an

unpredictable, irregular fashion. Repeated

occurrence of burst-suppression patterns produces a

burst-suppression cycle or event, which can be used

to predict epilepsy. Burst portions contain

physiological burst (normal) and pathological burst

or seizures (if present). Generally, visual inspection

of raw EEG signal is employed to detect burst-

suppression pattern. This visual detection is very

much subjective to the respective viewer (Löfhede,

2008; Löfhede, 2010; Wang, 2007).

Burst detection using amplitude integrated EEG

(aEEG) (Hellström-Westas L, 2008; Maynard et. al.,

1969, 1971) is quite common now. Here, input raw

EEG signal is first band pass filtered in the

frequency range 2-15 Hz to attenuate electrical

activities outside this range. Filtered EEG is then

rectified (i.e. negative voltages are converted to

positive values) and peak to peak voltages are

measured. Finally smoothing and semi-logarithmic

scale based compression is applied. It is very helpful

in detecting long term EEG trends (of several hours,

for example). Prolonged burst patterns can be

Bhattacharyya S., Mukhopadhyay J., Majumdar A., Majumdar B., Singh A. and Saha C..

AUTOMATED BURST DETECTION IN NEONATAL EEG.

DOI: 10.5220/0003123900150021

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2011), pages 15-21

ISBN: 978-989-8425-35-5

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

identified through visual inspection of single

channel or multiple channel aEEG patterns. But,

short term bursts cannot be easily identified from the

compressed logarithmic scale display of aEEG

patterns (Hellström-Westas L, 2008). Thus,

considering huge amount of recorded data, it is

necessary to develop mechanisms for automatic

detection of burst suppression patterns, without any

need of visual inspection.

Section 2 lists earlier works on the proposed topic.

Section 3 describes our approach. Finally in section

4, we show that our proposed approach has a

consistent high performance in detecting burst

patterns for babies of any age.

2 RELATED WORKS

There are many burst detection algorithms which are

classified based on whether they use any training

data for supervised learning and classification, or

whether they use static threshold based burst pattern

detection.

Automated burst detection algorithm using non

linear energy operator (NLEO), applied upon EEG

band and artefact band signal (Palmu, 2010; Särkelä,

2002) falls in the second category. In this algorithm,

EEG signal is first divided in two frequency bands:

1) EEG band (0.1-8 Hz), 2) Artefact band (47-49

Hz). For each sample i, if we say that x(i) is the

value of corresponding band filtered EEG at that

sample, then NLEO output for that sample is written

as:

NLEO(x(i)) = x(i) x(i-3) – x(i-1) x(i-2) (1)

For each sample i, difference of NLEO outputs

between EEG band and artefact band signals is

evaluated as follows.

DIFF(x(i)) = NLEO_EEG_band(x(i)) –

NLEO_artefact_band(x(i))

(2)

If this difference is persistently greater than certain

predefined burst threshold value for at least

minimum burst duration (which is set as 1 second),

then the algorithm notifies occurrence of a burst

pattern. Similarly, if this difference stays below a

fixed suppression threshold for a certain period of

time then it indicates the occurrence of a suppression

pattern.

This fixed threshold based burst pattern detection

method, however, leads to two kinds of drawback:

1) Recorded EEG signal amplitudes or energy

values increase along with baby’s age. Thus,

static threshold value based decision is not

suitable for burst detection over babies of

different ages.

2) Ranges of EEG amplitudes or energy values

vary on different recording channels. For

example, involvement of occipital channel (O1

or O2) results in generation of higher EEG

amplitude than EEGs from frontal channel (FP1

or FP2). Thus a fixed threshold cannot properly

detect burst in all channels.

Figure 1 shows false burst detection for one EEG of

six month old baby, throughout the channels P4-O2

and P3-O1, for the NLEO based algorithm as

discussed above. The display has sensitivity 15

µv/mm and time base 15 mm/sec. Due to static

threshold based calculations, high amplitude

recordings are misclassified as bursts. On the other

hand, bursts in frontal channels FP1 or F8 can not be

detected because of their relatively low amplitude.

Figure 1: False burst detection (marked in blue rectangles)

for NLEO based algorithm for a 6 month old baby, for

channels P4-O2 and P3-O1. Here, high amplitude

recordings throughout are misclassified as bursts.

In other words, for the NLEO based algorithm, high

amplitude recordings (compared to the predefined

static burst detection threshold) are always detected

as bursts. Similarly, bursts in relatively low

amplitude recordings may not be detected.

Another algorithm based on computation of

moving instantaneous amplitude and comparison

with threshold (Wang, 2007) has the provision of

dynamically setting the amplitude threshold. But it is

dependent upon visual perception, rather than

individual channel data based adaptation. So it also

does not generalize burst suppression detection for

babies.

Algorithms which use training data based

supervision extract several features like spectral

edge frequency, 3 Hz power, median, variance,

BIOSIGNALS 2011 - International Conference on Bio-inspired Systems and Signal Processing

Shannon entropy (Löfhede, 2008, 2010; Greene,

2008) etc. Training data is obtained by feature

values at burst instances which are manually marked

by experts. But this training based algorithm which

uses feature values during burst duration, is also not

free from the problem of false burst detection or

non-detection during high or low amplitude EEG

signals.

A general burst suppression pattern detector

should consider transition of feature values from

burst to suppression or background EEG or vice

versa. This analysis should be adaptive as per

individual channel data, so as to avoid

misclassification for wide variety of samples. Our

approach adapts burst or suppression thresholds

according to channel data. Using these adaptive

thresholds, burst patterns are detected. It leads to

generalized and high performance burst pattern

detection despite the variation of baby’s age or

channel data.

3 PROPOSED METHOD

3.1 Dataset

We perform the study over eight full term infants

having epileptic data and clear burst suppression

pattern. The data set is obtained from Department of

Neonatology, SSKM hospital, Kolkata, India.

During data recording, bipolar longitudinal montage

with sixteen electrodes is used, according to

international 10-20 standard (Rennie, 2008), at

positions FP1, FP2, F3, F4, P3, P4, O1, O2, C3, C4,

T3, T4, F7, F8. Voltage difference of two electrodes

is used as the input data, for example P4-O2 or C3-

P3. Each data has duration of 20 to 30 minutes. The

data covers babies of age from 6 days to 8 months.

Thus detecting proper burst patterns in this dataset

confirms generalized utility of our approach.

At least ten multi channel burst patterns are

present in each input data. Burst patterns are

manually marked by doctors. They also identify and

mark the artefacts to separate them from burst

patterns. In our algorithm, we check for only correct

burst pattern detection; detection of artefacts and

automatic separation of them from burst patterns is

not exercised.

3.2 Feature Extraction

The available data is digitized at a sampling rate of

256 Hz and band pass filtered between 0.5 to 20 Hz.

The band pass filter has its high pass component of a

order Butterworth filter and low pass component

of a 6

order elliptic filter. For the feature extraction

purpose, a sliding window of 1 second time

resolution and 0.5 second displacement is applied.

That is, features are extracted for second intervals 1-

2, 1.5-2.5, 2-3 and so on. Following features are

extracted for each time interval of 1 second duration:

1) Mean non linear energy (Greene, 2008)

2) Variance (Löfhede, 2008),

3) Power spectral density (Welch, 1967),

4) Total sum of absolute values of amplitudes.

If x(i) is the value of filtered EEG for sample i

residing in the window interval then mean non linear

energy (MNLE) for that window interval is given by

equation (3). For a burst pattern, mean non linear

energy value goes significantly higher from that of a

background or suppression EEG pattern.

MNLE = ∑(x

(i) – x(i-1) x(i+1)) for all

sample i lying within window interval

(3)

Similarly, variance (VAR), given in the equation (4),

has a significantly higher value in case of a burst

pattern occurrence as compared to its value during

background or suppression EEG pattern.

VAR = (1/(n-1)) ∑ (x(i)

– μ)

for all sample

i lying within window; μ is sample mean

(4)

Power spectral density (PSD) shows the distribution

of signal power with respect to frequency. Total

PSD value over bandwidth of signal under one

window interval is significantly higher during burst

pattern occurrence, as compared to its value in

background or suppression EEG.

Sum of absolute voltage values in signal under one

window interval has high value during burst and

comparatively much lower values during

background or suppression EEG.

All the feature extraction and subsequent

implementation is done in MATLAB version 7.8.0.

3.3 Burst Detection Algorithm

Generally, for visual detection of a burst pattern,

necessary sensitivity adjustments in display interface

are made in order to first make the so called general

amplitude output as a ground reference. Then bursts

are detected based on high signal fluctuations from

the average outcome. This principle is applied in our

burst detection algorithm.

In burst intervals, extracted feature values

deviate highly from their normal or average values

(i.e. values in background EEG patterns). To detect

burst portions, we need to determine two things:

AUTOMATED BURST DETECTION IN NEONATAL EEG

1) Meaning of average or background EEG

pattern and how it is represented by features

mentioned in section 3.2.

2) Benchmark of deviation of feature values in

burst portions as compared with average or

background EEG pattern.

We model average or background EEG pattern by

implementing separate circular queues for different

features like mean non linear energy, variance and

power spectral density. Each queue stores respective

feature values of last five seconds corresponding to

suppression or close to suppression intervals. Thus

effectively it stores ten feature values because

overlapping window is of 0.5 second displacement

and 1 second resolution. Initially each queue

contains median value of respective feature data

generated from total signal.

Current EEG portion under analysis is marked by

sliding window. If current EEG portion has feature

value less than or close to the mean value of feature

data currently stored in the queue, then we decide

that current EEG portion is from background or

average EEG pattern. In that case, current feature

data is stored in respective feature queue. Queue

update for each feature is thus independent of other

feature queue updates. Using a circular queue

enables replacement of least recent data with current

one, provided the queue is already full.

To determine whether current EEG portion is a

burst, we compare extracted feature values with

respect to mean feature values of respective queue.

Formally, we define val

nle, valvar, valpsd, and valamp as

values of mean non linear energy, variance, power

spectral density and sum of absolute voltages for

current EEG portion. These values change as queue

elements are updated with latest background EEG

data. Similarly, mean

nle, meanvar and meanpsd are

defined as mean values of queues storing non linear

energy, variance and power spectral density

respectively.

If current EEG portion generated feature values

(val

nle, valvar and valpsd) are greater than the mean

values of feature queues (mean

nle, meanvar and

mean

psd

respectively) by some multiples, then we

label current portion as possible burst. But, as we

model the EEG burst with respect to current channel

background EEG data, it may happen that current

possible burst portion has very low amplitude, thus

not visually identifiable as burst. This case can

happen when background EEG has very low activity

for some time. So we compare the sum of absolute

voltages for current EEG portion (val

amp) with

respect to a predefined threshold. If the voltage sum

is greater than the threshold then current region is

labeled as a burst. The algorithmic steps are shown

in figure 2.

Input:

Feature values of current EEG window.

Output:

Current EEG portion is burst or not (Boolean decision). If

true, we mark the burst start and end times.

Variables:

z1, z2, z3 are integers. th is voltage threshold.

z1 = z2 = z3 = 5 (experimentally set)

th = 15000 (experimentally set)

Algorithmic Steps:

1) If (valnle > z1 × meannle) and (valamp > th)

If (valvar > z2 × meanvar) or (valpsd > z3 × meanpsd)

Mark start of current time interval as burst start time.

2) If conditions in step 1 are not met and if there is an

ongoing burst interval then

Mark ongoing burst end time equal to midpoint of

current time interval.

3) If valnle is less than or very close to meannle then add

it in the circular queue for non linear energy values.

Similarly queues of variance and power spectral density

are updated if valvar is less than or close to meanvar and

valpsd is less than or close to meanpsd respectively.

Figure 2: Our proposed burst detection algorithm.

If, for time interval between x second to (x+1)

second, extracted features confirm start of a burst,

then burst start time is set as x second (according to

step 1). Now, if for the next analyzed time interval

(that is, between (x+0.5) second to (x+1.5) second),

features confirm end of the burst (according to step

2), then burst end time is marked as the midpoint of

current time interval; that is (x+1) second. So, in

effect, time interval x to (x+1) is marked as a burst

interval. Minimum burst duration is thus set to 1

second.

Finally, we mark the burst intervals generated from

above algorithm in a custom signal display interface

to visually check and compare with existing

approaches.

4 EXPERIMENTAL RESULTS

We executed our approach in the dataset mentioned

in section 3.1. We have also implemented NLEO

based algorithm (Palmu, 2010; Särkelä, 2002). We

perform both visual and statistical comparisons

between outcomes of these two algorithms. Input

data set was examined by doctors and burst patterns

were marked by them. We evaluate and validate

BIOSIGNALS 2011 - International Conference on Bio-inspired Systems and Signal Processing

performances of both algorithms with respect to

marked burst patterns.

Figure 3: Correct burst detection case (marked in yellow)

for channels P4-O2 and P3-O1 in our algorithm

corresponding to figure 1.

Figure 4: Burst detection in NLEO based algorithm for a

10 days old baby; it can’t detect bursts in FP2-F4 and FP1-

F3 channels (channel no 1 and 5 respectively from top in

display).

In figure 3, we show that our algorithm performs

correct burst detection in channels P4-O2 and P3-

O1, as corresponding to the false burst detection

region cases for NLEO based algorithm (which was

shown in figure 1). Rather than detecting whole

channel data as burst, due to static amplitude

thresholds, it uses the fact that, throughout for the

channels P4-O2 and P3-O1, background EEG

amplitude is quite high. So, corresponding burst

patterns are of quite high amplitude than other

channels.

Figures 4 and 5 show that our result is

comparable with the NLEO based algorithm even in

neonate EEG recording cases. For a 10 days old

baby, our algorithm detects correct burst cases in

multiple channels as compared to old NLEO based

(Palmu, 2010; Särkelä, 2002) approach, which

cannot detect burst in channel FP2-F4 or FP1-F3.

Figure 5: Burst detection in our algorithm as

corresponding to the case in Figure 4; it detects bursts in

FP2-F4 and FP1-F3 channels.

For very acute burst suppression pattern detection

also, our algorithm does better than existing

approach. Figures 6 and 7 show EEG of a 32 days

old neonate. NLEO based algorithm cannot detect

burst in any channel, whereas our algorithm detects

burst for most of the channels.

Figure 6: No burst detection in NLEO based algorithm for

a 32 days old neonate.

For statistical measure based performance

evaluation, we calculated sample sensitivity and

specificity. They are defined in equations (5) and (6)

respectively.

(True Positive * 100)

Sensitivity (%) =

(True Positive + False Negative)

(5)

(True Negative * 100)

Specificity (%) =

(True Negative + False Positive)

(6)

True positive means that a burst is detected by both

visual and automatic detection.

AUTOMATED BURST DETECTION IN NEONATAL EEG

Figure 7: Burst detection (marked in yellow) for our

algorithm corresponding to the case of figure 6.

True negative means that absence of burst is

detected by both visual and automatic detection.

False positive result occurs when automatic method

indicates a burst whereas visual detection cannot

find anything.

Lastly false negative case occurs when automatic

detection method indicates no burst whereas visually

a burst is marked.

The computation includes outcomes for all 16

channels. That is, for a particular multi-channel

burst, if burst for 4 channels are marked originally,

and our algorithm detects only 3 of them then

sensitivity is calculated as 75%.

We show tabular data of sensitivity for both old

NLEO based approach and our algorithm, executed

upon previously mentioned dataset. At first, multi

channel burst patterns for all the channels are

marked by doctors. We select the data files such that

there exists at least 10 visually identifiable multi

channel burst patterns. Then both algorithms are

executed to detect the percentage of bursts that are

correctly identified, for all the marked channels. It is

the required sensitivity value.

It is to be mentioned that, for NLEO algorithm,

maintaining same static burst detection threshold for

all input data gives poor result. So we calculated

separate optimum thresholds, specific to each of the

test data. These thresholds are then applied with

NLEO algorithm. Thresholds are set in such a

fashion that false positive cases are almost

eliminated. In our algorithm also, we found almost

zero false positive case for each of the test data.

Thus both algorithm exhibits almost same specificity

value (close to 100%) for all the test data. We

compare relative sensitivity values for these two

algorithms and show comparative results in Table 1.

We can see that in all cases except result 4,

sensitivity is higher in our algorithm. Also, NLEO

based outcome is highly dependent on choosing

correct burst detection threshold (which we did set

manually by observation for each experiment). On

the other hand, our algorithm’s dynamic adaptation

of thresholds based on channel data gives it slight

edge.

Table 1: Sensitivity comparison for 2 algorithms using

dataset of neonate and baby EEG

Sl No

Age

(D= days,

M= month)

No of multi-

channel burst

patterns seen

Sensitivity

with old

NLEO

algorithm

(%)

Sensitivity

with our

algorithm

(%)

1 6 D 12 94.18 95.35

2 10 D 10 67.44 88.37

3 16 D 25 89.36 95.74

4 39 D 24 95.16 93.0

5 3M 21 79.5 98.7

6 6M 30 86.5 94.6

7 6M 35 84.06 97.15

8 8M 39 92 97

If burst detection threshold is set quite low in NLEO

algorithm, then for high amplitude signals, it shows

continuous burst, increasing false positive rate

(similar to the case shown in figure 1). Our

algorithm is free from any such false positive case

detection.

5 CONCLUSIONS

We have described a simple dynamic threshold

based automatic burst detection algorithm. It can be

used in analyzing EEG bursts for neonates and also

for matured babies. So far, EEG data of babies up to

eight months are experimented. It can be further

tested and upgraded to include automated burst

detection for children of higher age, and possibly for

adult EEG also. Bursts of minimum one second

duration are detected in our algorithm. It can be

augmented to include sudden spikes of length less

than one second, for any further research.

ACKNOWLEDGEMENTS

This project is funded by Ministry of

Communications and Information Technology,

Government of India; under project grant number

1(4)/2009-ME&TMD. We are thankful to our

colleagues and to the doctors of neonatology

department, SSKM Hospital for helping us in data

collection, marking, and analysis.

BIOSIGNALS 2011 - International Conference on Bio-inspired Systems and Signal Processing

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AUTOMATED BURST DETECTION IN NEONATAL EEG