IMPROVEMENT AND VALIDATION OF AN AUTOMATED

NEONATAL SEIZURE DETECTOR

P. J. Cherian

, W. Deburchgraeve

, V. Matic

M. De Vos

, R. M. Swarte

, J. H. Blok

, P. Govaert

, S. Van Huffel

and G. H. Visser

Department of Clinical Neurophysiology, Erasmus MC

University Medical Center Rotterdam, ’s-Gravendijkwal 230, 3015CE, Rotterdam, The Netherlands

Department of Electrical Engineering (ESAT), Katholieke Universiteit Leuven

Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium

Department of Neonatology, Sophia Children’s Hospital, Erasmus MC, University Medical Center Rotterdam

Dr. Molewaterplein 60, 3015 GJ, Rotterdam, The Netherlands

Keywords: Neonatal EEG, Neonatal seizure detection, Epilepsy.

Abstract: We present the improvements made to and subsequent validation of an automated approach to detect

neonatal seizures. The evaluation of the algorithm has been performed on a new and extensive data set of

neonatal EEGs. Previously, we have classified neonatal seizures visually into two types: the spike train and

oscillatory type of seizures and developed two separate algorithms that run in parallel for their automated

detection. The first algorithm analyzes the correlation between high-energetic segments of the EEG,

whereas the second one detects increases in low-frequency activity (<8 Hz) and then uses an

autocorrelation. An improved version of our automated system (called ‘NeoGuard’) uses more informative

features for classification and optimized parameters for thresholding. The validation was performed on 756

hours of ‘unseen’ continuous EEG monitoring data from 24 neonates with encephalopathy and recorded

seizures. The seizure detection system showed a median sensitivity of 86.9 % per patient, positive predictive

value (PPV) of 89.5 % and false positive rate of 0.28 per hour. The modified algorithm has a high

sensitivity combined with a good PPV whereas false positive rate is much lower compared to the previous

version of the algorithm.

1 INTRODUCTION

Neonatal seizures occur in 1 to 3.5/1000 births and

they represent a distinctive indicator of abnormality

in the central nervous system – CNS (Volpe, 2001).

The etiologies are varied, with the majority being

caused by biochemical imbalances within the CNS,

hypoxic ischemic encephalopathy, intracranial

haemorrhages and infection, and developmental

(structural) defects. Neonatal seizures are associated

with major dysfunction of the CNS and result in

significant sequelae (Holmes, 1998; Miller, 2002).

Therefore, there is a high need for early detection of

the seizures. Seizures detected in the early stages of

life can be treated with anticonvulsant drugs and in

that way, hopefully, further damage to the brain can

be limited. In clinical practice, detection of the

seizures is accomplished by a combination of

clinical observation and visual assessment of the

EEG. However, clinical signs need not always

accompany neonatal seizures. They can manifest as

subtle (Connell, 1989; Malone, 2009) or subclinical

seizures, being only detected by EEG monitoring.

Many algorithms for detection of neonatal

seizures have been published. The best known

methods are based on computing a running

autocorrelation function (Liu, 1992), rhythmic

discharges detection (Gotman, 1994), modelling and

complexity analysis (Celka, 2002). Other approaches

have employed wavelets, frequency content,

entropy, etc., for feature extraction. These features

were then applied for supervised learning and

training of classifiers (Greene, 2007; Zarjam, 2003;

Aarabi, 2006).

At the moment, however, there is no neonatal

seizure detection algorithm which is widely accepted

in clinical practice. The design of a reliable seizure

detection system is a challenging task as neonatal

EEG during seizures has as extremely complex and

variable morphology. Moreover, great difference

among the seizure patterns can be present even

J. Cherian P., Deburchgraeve W., Matic V., De Vos M., M. Swarte R., H. Blok J., Govaert P., Van Huffel S. and H. Visser G..

IMPROVEMENT AND VALIDATION OF AN AUTOMATED NEONATAL SEIZURE DETECTOR.

DOI: 10.5220/0003127700310037

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2011), pages 31-37

ISBN: 978-989-8425-35-5

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

within the same patient (Lombroso, 1996;

Shewmon, 1990).

We have previously published (Deburchgraeve,

2008) an algorithm for automated neonatal seizure

detection. It is designed with an approach which

tries to mimic the decisions made by the clinical

neurophysiologist while visually examining EEG. In

order to detect a neonatal seizure, the human

observer searches for a pattern which shows a

visible change relative to the background EEG. An

additional main characteristic of all seizures is

repetitiveness, as there is always a recurrent pattern

which describes the seizure. Both features were

employed for the algorithm design.

Due to the nature of the problem, the neonatal

seizure detection system has to be very reliable and

robust. Therefore, constant improvement, validation,

and optimization of the algorithm are needed. The

modified version of our detection system, called

NeoGuard, was tested on a new, large set of unseen

EEG data. We present here the results of the

validation of our detection system.

2 METHODS

2.1 EEG Data Set

All EEG data were recorded at the Sophia Children's

Hospital – part of Erasmus MC, the University

Medical Center in Rotterdam, the Netherlands. The

data base is formed from 24 consecutive newborns

with presumed perinatal asphyxia who underwent

video–EEG monitoring for at least 24 hours and had

recorded seizures. The recordings mostly started

within 24 hours of birth. Digital video–EEG with

polygraphy, was registered continuously for 1-3

days using a Nervus

monitor (Taugagreining hf,

Reykjavik, Iceland). Seventeen scalp electrodes

were placed according to the full 10-20 International

System (Cherian, 2009). The sampling frequency

was 256 Hz. It is important to stress that we have

used a completely new data set for this study, with

no overlap with the one that has been described

previously (Deburchgraeve, 2008). All EEG data

was reviewed by a clinical neurophysiologist and the

seizures were visually scored for their onset,

amplitude, frequency, duration, rhythmicity, location

and spread. We defined as ‘definite seizures’

electrographic discharges that showed a clear

variation from background activity, displaying a

repetitive pattern of oscillations or sharp waves or a

mixture of both, lasting ≥10 seconds, with evolution

in amplitude and frequency over time. We classified

discharges as ‘dubious seizures’ when a) runs of

sharp waves /oscillations or a mixture of both

occurred arrhythmically (with marked variability in

the interval and morphology between individual

complexes for the major part of its duration) or b)

rhythmic discharges of shorter (<10 sec) duration or

periodically occurring sharp waves or mixed

patterns. It was difficult to identify the onset and

offset of such discharges and sometimes difficult to

clearly identify them as a variation from ongoing

EEG background. We chose to group them under

‘seizures’ as they were seen to recur paroxysmally

during the monitoring.

2.2 Updates of the Automated Seizure

Detection Algorithms

During the visual analysis of the neonatal seizures

we have identified two major morphological types.

The first one represents the spike train seizures (Fig.

1A), whereas the second one represents the

oscillatory seizures (Fig. 1B). We were able to

classify almost all neonatal seizures as one of the

two types or as their combination (Fig. 1C).

Figure 1: A. Spike train type seizure, B. Oscillatory type

seizure, C. Combination of both morphologies.

The most prominent difference between the two

seizure types is that the oscillatory type is

continuous in time, whereas the spike train type

consists of distinct, isolated spikes. Additionally, the

oscillatory type is characterized by low frequency

content and spikes represent a highly dynamic

signal. Therefore, two separate algorithms were

developed and different stages are discussed in detail

in our previous work (Deburchgraeve, 2008). The

basic idea to detect a spike train seizure is to

segment isolated spikes and to compare their

morphology. We will regard spike train as a seizure

if the overall similarity between spikes is sufficiently

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

Figure

2: Schematic overview of the complete neonatal seizure detection algorithm.

Figure 3: Schematic overview of segmentation steps of the spike train detection.

high. On the other hand, oscillatory seizures are

continuous and have low frequency values. At first,

we detect the oscillatory segments by filtering and

monitoring the increase of the low frequency content

(< 8 Hz). After that, we examine the presence of

oscillatory seizures by analysis of the

autocorrelation function of the corresponding signal

segment. The updated stages of the detection

algorithm are shaded in grey in Fig. 2. The most

important improvement concerns a change in the

segmentation strategy for the spike train type

detection. As far as the detection of the oscillatory

seizures is concerned, only the analysis of the

autocorrelation function has been changed. Details

on the other blocks can be found in the previous

paper (Deburchgraeve, 2008).

2.2.1 Segmentation of the Spike Train

Signal

The segmentation of the EEG sharp transients is

important for the reliability of the spike train

detection algorithm. This segmentation is performed

separately on each channel of EEG, on a window of

5 seconds duration. There is an overlap of 4 seconds

between subsequent windows under analysis. Fig. 3

shows a schematic overview of the updated

algorithm.

We use the non-linear energy operator (NLEO)

again to detect the local presence of a high

frequency activity. In its discrete form it is given by:

() () ( ) ( )

kaiser

xn x n xn xn

⎡⎤

=−−⋅+

⎣⎦

(1)

The key property of the NLEO can be derived if we

apply it on the discrete sinusoidal signal (Li, 2007):

(

)

(

)

()

22 22

cos

sin

kaiser

xn A n

xn A n A

ωθ

ωω

⎡⎤

≈≈

⎣⎦

(2)

When applied to spike train type seizure EEG, the

NLEO effectively amplifies the high-frequency

spikes while, on the other hand, attenuates the

background EEG. The NLEO calculates the local

energy of the signal using only a few samples.

However, the spikes of a neonatal spike train type

seizure vary in duration and can be of 50 ms length

up to 500 ms. In order to adjust the sensitivity of the

NLEO to the duration of the spikes, its output needs

to be smoothed. However, it is not possible to find a

single smoothing filter length that is adequate for

both short (50 ms) and long (500 ms) duration

spikes. This problem is solved by using a smoothing

filter bank with 6 Moving Average (MA) filters with

filter lengths of 2, 4, 8, 16, 32, and 64 samples

respectively. The output signal of one filter is the

input of the filter with next increasing MA filter

length. The output of the filter bank is the

summation of the outputs of each filter. This

generates a smooth signal in which short as well as

long spikes can easily be discriminated. Fig. 4C

displays the smoothing effect on a spike train type

seizure with spikes of >500 ms duration. The arrows

in Fig. 4B and C indicate that short peaks in the

NLEO output are conserved by the smoothing: only

variations of the NLEO output on a large time scale

are smoothed out. This is exactly the desired

behaviour of the algorithm: to be sensitive to spikes

of both short and long duration.

The goal of the next step is to find an adaptive

threshold to discriminate between high and low

energy values. After thresholding, the parts of the

signal with high energy are transformed to isolated

IMPROVEMENT AND VALIDATION OF AN AUTOMATED NEONATAL SEIZURE DETECTOR

Figure 4: Illustration of the steps of the segmentation.

segments with a certain position and length. The

threshold must be at a level that detects the

transients in the EEG without segmenting small,

insignificant variations in the energy signal. For this

purpose, segmentation is performed for a set of

thresholds between 0 and 1 with a step size of 0.02.

For each threshold, the number of segments above

the threshold is counted. The threshold that leads to

the maximum number of segments is kept as the

definite threshold. If several threshold levels lead to

the same number of segments, the lowest one is

taken.

2.2.2 Detection of the Oscillatory Seizure

Type – Autocorrelation Analysis

As described previously (Deburchgraeve, 2008),

algorithm for the oscillatory seizure type, has to

detect segments with significant increase in the

frequency band of 1–8 Hz. Autocorrelation function

is computed for these segments and new features are

extracted from it in this modified approach. In the

updated version, three features are used to

distinguish quasi–periodic segments:

- Regularity of the distances between the zero

crossings (Fig. 5A), defined as 'errorZeros'.

- Regularity of the distances between the peaks

(Fig. 5B), defined as 'errorPeaks'.

- Regularity of the normalized RMS values of

the peaks which are delimited by the zero crossings

(Fig. 5C), defined as 'errorRMS'.

We have selected these features due to the fact

that for an oscillatory signal, the phases of the

autocorrelation function are regular. Hence, for

oscillatory seizure activity, we may expect that the

defined errors have relatively small values.

Figure 5: Illustration of the extracted features of the

autocorrelation function.

Regularity was measured by means of a pair wise

comparison of all the distances or RMS values

involved. For this purpose, each difference between

an element indicated with a dark grey bar compared

with that indicated by a light grey bars is expressed

as a percentage of their difference in length or area.

(Fig. 5). For seizure detection, the thresholds on the

features were defined as:

- median([errorZeros,errorPeaks]) < 7% and,

- median(errorRMS) < 10%

The comparisons for the zero crossings and the

distances between the peaks can be grouped together

as both represent measures of distance. The

comparisons for the RMS values are treated

separately. All segments with properties below these

thresholds are regarded as a part of an oscillatory

seizure.

2.3 Validation of the Improved

Algorithm

Different approaches for the quantification of the

performance of neonatal seizure detection

algorithms have been proposed by various

researchers. Due to variations in patient population

and methods of data collection, it is difficult to

compare the results of the performance of various

algorithms in a fair way. Therefore, we have decided

to use several parameters to analyze the performance

of the neonatal seizure detector.

We defined the sensitivity per patient (

SensPP),

as the percentage of the number of seizures marked

by the clinical neurophysiologist that are detected:

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

()

det / 100%SensPP SZ PP SZtotPP=⋅

(3)

with SZtotPP representing the number of seizures

marked by the neurophysiologist for each patient,

and SZdetPP representing the number of

automatically detected seizures for that patient. A

seizure was considered detected when there was a

temporal overlap between the marked seizure and

the detection. The overall sensitivity (for all

patients) was calculated using 2 methods. The first

one, simply averages all sensitivities per patient

(SensT_PP). The second method (SensT) measures

the percentage of seizures detected of all seizures

present in the complete 756 hours dataset. That is,

SensT_PP represents the sensitivity at the patient

level, whereas SensT represents sensitivity at the

seizure level. The importance of the difference is

that in SensT_PP, a patient with only a few seizures

is considered to be equally important as a patient

with many seizures. On the other hand, SensT

considers all seizures equally important regardless of

the patient they occurred in.

In addition, we used Positive Predictive Value

(PPV). that is defined as the percentage of detected

events that match seizures:

()

/ 100%PPV EV SZ EV tot=⋅

(4)

with EV_tot the total number of detected events and

EV_sz the total number of detected seizures (i.e.,

events that overlapped with a seizure marked by the

clinical neurophysiologist). Occasionally, a single

seizure was detected several times by the algorithm.

All such events were combined into a single EV_sz

detection. In practical terms, PPV gives the

probability that the detector has detected a true

seizure for each detection. The duration of the event

is not taken into account. PPV is event-based and,

therefore, depends on the a priori likelihood of

seizures (‘prevalence’) in the dataset. Hence this

measure is difficult to compare between different

data sets. Nevertheless, it is an interesting

performance measure of the detector.

Last but not least, we have quantified a measure

of the number of False Positive detections per hour

(FP/h). This measure directly represents the

practical usability of the algorithm, because each FP

implies that somebody in the neonatal intensive care

unit (NICU) will have to check the patient and the

raw EEG recording unnecessarily.

3 RESULTS

During the analysis, we have examined 756 hours of

EEG data. Median duration of EEG recording was

25 h (range 17 to 78) per patient. The algorithm

gives an output of the number of events detected, the

position in time where the event was detected and

the duration of the detected events. A total of 2103

seizures were scored visually (median 67 per patient,

range 7-236). Detailed results of the validation are

presented in Table 1.

In four patients with severely abnormal EEG

background activity and predominantly dubious

seizures, the algorithm performed very poorly. As it

was doubtful whether this recurring paroxysmal

activity constituted genuine seizures, we excluded

these patients. These were the patients 12, 13, 21, 23

in Table 1. Examples of dubious seizure patterns are

presented in Fig. 6 and 7. In the remaining 20

patients, the algorithm showed a SensPP of 86.9%,

PPV of 89.5% and Fp/h of 0.28/h (in total 643 hours

of EEG data, 1263/1538 seizures detected, SensT

82.1%).

Table 1: Seizure detection results.

Sz det Sens Fp PPV Fp/h

1 52/53 98 21 84 0.88

2 10/18 56 1 92 0.04

3 28/48 58 19 60 0.42

4 30/34 88 34 47 2.00

5 56/63 89 0 100 0

6 12/13 92 4 75 0.17

7 104/109 95 0 100 0

8 8/8 100 0 100 0

9 93/98 95 0 100 0

10 6/7 86 6 50 0.26

11 110/112 98 6 95 0.29

12 0/210 0 45 0 0.09

13 1/70 1 8 11 0.33

14 47/50 94 3 94 0.13

15 30/72 42 7 81 0.17

16 18/33 55 15 55 0.65

17 95/113 84 1 99 0.04

18 169/200 97 10 94 0.42

19 14/44 32 12 54 0.41

20 10/27 37 69 13 3.45

21 12/156 7 0 100 0

22 170/200 85 10 94 0.42

23 9/129 7 80 10 3.2

24 201/236 85 31 87 0.47

4 DISCUSSION

AND CONCLUSIONS

In this paper, we have presented an improved

version of the previously designed neonatal seizure

IMPROVEMENT AND VALIDATION OF AN AUTOMATED NEONATAL SEIZURE DETECTOR

Figure 6: Dubious seizures characterized by brief rhythmic discharges and periodic sharp waves. Such seizures were

variably detected by the algorithm.

Figure 7: Dubious seizure over right central region characterized by a mixture of arrhythmic slow and sharp waves, not

detected by the algorithm.

detector. The validation was performed on a new

and large dataset, which has not been used

previously during the optimization of the algorithms.

These results confirm the suitability of the detection

system for long-term EEG monitoring in a NICU

setting, especially for detecting ‘definite seizures’,

that are similar to the discharges defined by most of

the published literature on neonatal seizures.

Seizures with very low amplitude and short

duration were missed by the algorithm and this has

been reported by other authors as well (Mitra, 2009).

More specifically, automatic detection of arrhythmic

seizures of low amplitude and predominantly

oscillatory morphology was poor, whereas

arrhythmic seizures with sharp wave morphology

were well-detected. As the morphology of the

neonatal EEG is extremely variable, it is difficult to

develop a patient-independent algorithm. Neonatal

seizure definition and classification is still a

developing field, and the performance of an

automated detector depends very much on the

predetermined definition of such discharges.

The clinical significance of the low amplitude

arrhythmic seizures occurring in neonates with

persistent, severely abnormal EEG background

activity (suggestive of severe underlying brain

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

injury) is debatable, and it is unlikely that detection

and treatment of such paroxysmal discharges

improves clinical outcome. More research needs to

be done to better understand the pathophysiology of

neonatal seizures and the clinical significance of

seizures in patients with varying severity of brain

injury. This is a prerequisite for identifying the types

of seizures whose treatment with antiepileptic drugs

will improve clinical outcome. Refinement of

automated seizure detection methods can then be

done, targeted at this subgroup.

ACKNOWLEDGEMENTS

Research Council KUL: GOA Ambiorics, GOA

MaNet, CoE EF/05/006

Optimization in Engineering (OPTEC), IDO 05/010

EEG-fMRI, IDO 08/013

Autism, IOF-KP06/11 FunCopt, several

PhD/postdoc & fellow grants;

Flemish Government:

* FWO: PhD/postdoc grants, projects: FWO

G.0302.07 (SVM), G.0341.07 (Data fusion),

G.0427.10N (Integrated EEG-fMRI) research

communities (ICCoS, ANMMM);

* IWT: TBM070713-Accelero, TBM070706-

IOTA3, TBM080658-MRI (EEG-fMRI), PhD

Grants; Belgian Federal Science Policy Office:

IUAP P6/04 (DYSCO, `Dynamical systems, control

and optimization', 2007-2011);ESA PRODEX No

90348 (sleep homeostasis)

EU: FAST (FP6-MC-RTN-035801), Neuromath

(COST-BM0601).

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IMPROVEMENT AND VALIDATION OF AN AUTOMATED NEONATAL SEIZURE DETECTOR