Epileptic Seizure Detection using Bipolar Singular Value

Decomposition

Mojtaba Bandarabadi

, Jalil Rasekhi

, César A. Teixeira

and António Dourado

CISUC, Center for Informatics and Systems of the University of Coimbra, Coimbra, Portugal

Department of Biomedical Engineering, Babol Noshirvani University of Technology, Babol, Iran

Keywords: Epilepsy, Seizure Detection, Singular Value Decomposition, Coherency, Synchronization, Bipolar

Electroencephalogram.

Abstract: We propose a robust method for automated detection of epileptic seizures using intracranial

electroencephalogram (iEEG) recordings with two electrodes. The state-of-the-art seizure detection methods

suffer from high number of false detections, even when designed to be patient-specific. The solution

reported here aims to achieve very low false detection rate, while providing a high sensitivity. Two adjacent

iEEG recordings are subtracted from each other to make the bipolar iEEG signal. The values achieved from

singular value decomposition (SVD) of the bipolar iEEG signal are used as measure. A threshold is

subsequently applied on the measure. Results indicate robustness of the proposed measure for seizure

detection. The method is applied on 5 invasive recordings containing 54 seizures in 780 hours of

multichannel iEEG recordings. On average, the results revealed 85.2% sensitivity and a very low false

detection rate of 0.02 per hour in long-term continuous iEEG recordings.

1 INTRODUCTION

Epilepsy, the second common brain disorder is

mainly characterized by recurrent and abrupt

seizures. The highly coherent neural activities play

the central role in the development of epileptic

seizures, which usually last from seconds to minutes.

Electroencephalogram (EEG) recordings are

commonly used in the study of brain, its functions

and related disease (Rho et al., 2010). Long-term

continuous multichannel recordings produce huge

amounts of data, sometimes up to several hundred

megabytes for a single recording channel. Real-time

monitoring of the long-term continuous EEG

recordings by EEG experts (visual inspection) can

be impossible, whereas offline analysis can also be

very costly, tedious and tiresome. By automatically

labeling the seizure onsets, long term monitoring,

diagnosis and treatment can be highly facilitated.

Researchers and neurologists will just be required to

refer to the labeled EEG recordings. On the other

hand early seizure detection could improve the

living conditions of epileptic patients. Automatic

drug injection or brain stimulation method can be

triggered by adequately fast onset detection

algorithm to suppress oncoming seizure

(Bandarabadi et al., 2014c).

There are many existing seizure detection

algorithms. They usually seek to optimize one of

two competing goals; (1) fast seizure onset

detection; the real-time detection of epileptic

seizures without or with a negligible delay from

onset initiation (Shoeb et al., 2004; Meier et al.,

2008; Kharbouch et al., 2011; Bandarabadi et al.,

2014b), and (2) accurate seizure event detection: the

accurate labeling of the occurrence of seizures with

high sensitivity and specificity (Varghese et al.,

2009; Sharma et al., 2014; Adeli et al., 2007;

Acharya et al., 2011; Hassanpour et al., 2004). The

first approach is best suitable for closed-loop

therapeutic as well as for patient care systems, where

only onset detection delay times of few seconds can

be tolerated. The second approach is much

appropriate for offline labeling of recorded EEGs for

future studies, and can tolerate longer detection lags.

High number of false detections is the main

drawback of most current approaches, which makes

them unacceptable for clinical applications.

Furthermore they have been applied mainly on short

recordings, and have not been validated

satisfactorily for long-term continuous recordings

with several weeks length, including extensive

interictal periods.

In the framework of the EPILEPSIAE project

178

Bandarabadi M., Rasekhi J., A. Teixeira C. and Dourado A..

Epileptic Seizure Detection using Bipolar Singular Value Decomposition.

DOI: 10.5220/0005193401780183

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2015), pages 178-183

ISBN: 978-989-758-069-7

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

(Klatt et al., 2012), the consortium has collected

long-term continuous intracranial/scalp EEG

(iEEG/sEEG) recordings of more than 275 patients.

The current database includes detailed information

about the epileptic seizures of all patients, such as

type, onset/offset time, propagation, and seizure

onset age. The recorded data of the patients was

visually inspected by epileptologist experts, a both

tedious and faulty process, requiring double checks.

Such a demand for robust automated method with

high sensitivity and very low false detection rate,

which would require the neurologist just to refer to

the detected epileptic seizures to extract extra

information, motivated our team to study and

develop new detection algorithms. We have recently

developed a new seizure detection method using

sub-band mean phase coherence (sub-band MPC)

(Bandarabadi et al., 2014a). The raw iEEG data of

two adjacent electrodes was first band-pass filtered

using forward-backward method to obtain desired

frequency bands. Subsequently, the mean phase

coherence (MPC) measure of each sub-band was

calculated. The proposed method could provide a

sensitivity of 79% with a low false detection rate of

0.05 h

-1

This paper makes two contributions. First, it

proposes a robust seizure detection method using

singular values extracted from space-differential

(bipolar) recordings to improve the parameters of

sensitivity and specificity. Second, it evaluates the

efficiency of proposed measure on long-term

continuous iEEG recordings that are longer than one

month.

2 METHODOLOGY

The methodology is based on singular values (SVs)

extracted from windowed bipolar iEEG signal, and

the phenomena of unique bipolar signal

manifestations during a seizure event. Figure 1

presents the block diagram of the proposed method

for automated seizure detection, including a manual

channel selection, a segmentation stage, building

bipolar iEEG, a singular value decomposition

(SVD), and a threshold box for decision-making.

Database

Two adjacent

electrodes

Segment into

2 seconds

Bipolar

signal

SVD

Threshhold

Figure 1: The block diagram of the detection algorithm.

2.1 Dataset Description

In order to evaluate the proposed method, we use

real iEEG data recorded using two adjacent

electrodes placed over the focal area, from European

database on epilepsy (Klatt et al., 2012). The five

candidate patients with refractory focal epilepsy

were monitored continuously for several days,

during their pre-surgical studies. Focal seizures are

localized to specific brain regions, while generalized

epileptic events may initiate and spread across the

whole brain tissue. The two electrodes are nearly

satisfactory when working with partial seizures,

however more electrodes should be considered for

the study of other seizure types.

Recordings were obtained with sampling rate of

1024 Hz at the epilepsy unit of the University

Hospital of Freiburg, Germany. Onset times, and

their initialization and spatial propagation on the

electrodes were marked by epileptologists by visual

inspection of iEEG recordings and using video

recordings of patients during their stay in hospital.

Information of both electrographic and clinical

onset/offset times is available in the database, and

electrographic onsets were considered here. Patient

characteristics are summarized in Table 1.

Table 1: Information for the 5 studied patients.

Patient ID

Gender

Patient age (y)

Onset age (y)

Localization of

seizures

Recording time

(h)

No. of seizures

Mean seizure

duration (s)

A F

29 10

RMT,RLT

183 9 82.3

B F

32 1

LMT

162.6 9 121.9

C F

11 3

RMT

155 14 122.7

D F

32 8

RBF,LMT,RMT

151.6 9 122.5

E F

18 6

L-T,L-F

127.8 13 86.5

Mean

24.4 5.6

780 54 107.1

RMT/LMT (right/left mesial temporal lobe), RLT (right lateral temporal

lobe), RBF (right basal frontal lobe), L-T (left temporal lobe), L-F (left

frontal lobe).

2.1 Bipolar iEEG Signal

The iEEG recordings are technically bipolar by

nature, since they are recorded with reference to a

fixed electrode. Positioning of electrodes and

reference channels can both affect the nature of the

recorded signal (Nunez et al., 1997). By tradition

however, these channels are called monopolar, and

the difference of two monopolar channels, selected

physically in close proximity (in the range of few

EpilepticSeizureDetectionusingBipolarSingularValueDecomposition

179

millimeters), is known as bipolar. For sufficiently

close-by configurations, the bipolar signal may be

considered as an approximation of the tangential

component of brain’s electric field. In contrast to the

monopolar EEG, the bipolar approach is less

susceptible to artifacts (Aarabi et al., 2007). Bipolar

processing can remove common mode interferences

mounted evenly on two adjacent electrodes. These

common mode interferences may include power line

noise (50 or 60Hz and their harmonics) and

movement artifacts (EMG). Furthermore it provides

better spatial resolution in contrast to the monopolar

iEEG recordings (Srinivasan et al., 1996; Nunez et

al., 1997; Tang et al., 2007). Bipolar recordings

better reduce the volume conduction effects

compared to the monopolar recordings, by acting as

a high-pass spatial filter (Nunez et al., 1997).

Moreover, topographical variations invisible to

monopolar recordings can be identified using bipolar

schemes (Baranov-Krylov and Shuvaev, 2005).

Bipolar channels were derived by differencing two

immediately adjacent electrodes, selected from

candidate probe array on focal area. Arrays can be in

the form of grid, strip, or depth probes.

2.2 Singular Value Decomposition

SVD as a common computational tool employed in

signal processing and pattern recognition, acts as a

mathematical factorization of data matrices obtained

from the patients, to highlight the dominant

properties of their underlying patterns. The core idea

of SVD is to take a collection of data, find the

patterns having the highest correlation with that

data, and then sort these patterns in a descending

order based on their importance. In fact, SVD

decomposes data to its correlated parts, with the

larger singular values (SVs) corresponding to those

parts with more energy (Bandarabadi et al., 2010).

The process decomposes the original matrix M into

the product of three sparse matrices (1),

....mn mm mn nn

UV

(1)

where

 is singular value matrix, U and V are left

and right singular vector matrices respectively. U

and V are orthogonal matrices, and



is a

rectangular diagonal matrix with its nonnegative real

elements sorted in a descending way (2).

, if







 







12 m





 

(2)

The singular values (



) indicate the significance of

the corresponding left/right singular vectors. The

pair of singular vectors related to the highest SV,

contain more information about the dominant

patterns than other singular vector pairs (Hassanpour

et al., 2004). By highlighting the dominant epileptic

activities within a bipolar iEEG data, SVD can be

used as a tool for detecting epileptic events. In order

to apply SVD, the raw EEG data should be first

expressed in the form of a square matrix. Hankel

operator is a square matrix with constant skew

diagonals, and is employed here to build such a

matrix. Suppose

[, ,..., ]

xxX  as a segment of

EEG signal, and n being a positive even integer.

Then the Hankel matrix of X can be written as (3).

2/2

3/21

/2 /2 1 1

...

nn n

xx x















(3)

Since the computational cost of SVD is high, the

iEEG signal is downsampled from 1024 to 512 Hz to

boost the computation time. The iEEG signal is

segmented into 2-sec windows with 50% overlap, to

provide feature samples every second. The length of

window is selected by a tradeoff between two

extremes: it should be long enough to cover the

trends related to brain’s current state, and short

enough to be considered as quasi-stationary. The

Hankel matrix of the bipolar iEEG is first built, after

which SVD is calculated to obtain the SVs.

Considering 2-sec windows having 1024 samples,

the Hankel matrix would have the size of 512*512

elements. The SVD operator will thus produce 512

SVs (



, i=1,…, 512), ordered in a descending way.

The main characteristic of an epileptic seizure is

the highly coherent activity of the neurons,

generating nearly the same electrical voltages by two

very close bunches of neurons. This highly coherent

and synchronous state during seizure events,

specifically prior to seizure termination (Schindler et

al., 2007a; Schindler et al., 2007b), leads to a

significant increase in the level of common mode

signal of the adjacent channels, taking more similar

waveforms. SVs represent the level and importance

of the energies contained within the correlated parts

of signal. As a result of excessive coherency during

seizure, the energy of the resulting bipolar signals

and their correlated parts will decrease. Figure 2

shows the extracted SVs of sample seizure.

BIOSIGNALS2015-InternationalConferenceonBio-inspiredSystemsandSignalProcessing

180

2.3 Preprocessing of Features

The average of each singular value for the first 60

minutes of recordings and for each patient was

calculated, and the SVs were normalized by dividing

to that average. The range of SVs (



, i=1,…, 512)

were equalized by this normalization (Figure 3).

Afterward, the 32 best performing SVs were

selected and average of their normalized values was

used as a single measure. Specifically the SVs from

9 to 40 performed better in our study for seizure

detection, and were considered to make a unique

measure.

Furthermore the coherent epileptic neuronal

activities last for several seconds, thus smoothing

the feature vector by a rectangular moving average

window of 4 consecutive samples, decreases the

likelihood of short coherent events that are not ictal

from reaching threshold. The smoothing would

greatly reduce the number of false alarms.

Figure 2: SVs (65-128) extracted from 10 minutes of

bipolar iEEG signal contains one seizure. Vertical red

lines indicate onset and offset times. The SVs first start to

increase by seizure development, and then suddenly

decrease approaching the seizure termination.

Figure 3: Normalized SVs of a sample seizure from

patient B. The range of SVs is equalized after

normalization. Vertical red lines indicate onset and offset

times.

The singular values mostly start to increase with

seizure initiation, while suddenly decrease once

seizures are well developed and approach their

termination. Although looking for increase in

singular values provide less detection delays, they

generate higher numbers of false alarms and lower

sensitivity values than looking for decrease in

singular values, when used for detecting seizure

events. Therefore the inverse of the measure,

obtained from average of normalized SVs from 9 to

40, was considered as a candidate measure to

highlight this decrease.

2.4 Alarm Generation

A threshold based classiﬁer is used for the detection

of epileptic seizures. Threshold value is selected for

each patient separately, ranging from 1.5 to 2.5, and

are applied on the candidate feature. Upon the

measure passing of the threshold, an alarm will be

raised, after which further alarm generation will be

blocked for 4 minutes. This limitation guarantees the

raising of just a single true alarm per seizure.

3 RESULTS

Sensitivity (SS) and false detection rate (FDR) of the

raised alarms were used to evaluate the methods.

Sensitivity is the fraction of correctly detected

seizures within the total seizures, and the FDR value

is the number of false detections per time unit

(hour). Table 2 presents the results of seizure event

detection using two methods, first from bipolar SVs,

and the other using sub-band MPC method

(Bandarabadi et al., 2014a), obtained from same

patients and same channels.

Table 2: Results obtained for 5 studied patients.

Bipolar SVD Sub-band MPC

FDR

A 100 0.02 78 0.06

B 100 0.01 78 0.05

C 71.4 0.08 71 0.09

D 66.7 0.01 66 0.02

E 100 0 100 0.04

Mean 85.2 0.02 79 0.05

SS: Sensitivity of raised alarms in percent.

FDR: False detection rate of raised alarms per hour

The results of bipolar singular values provide on

average, a sensitivity of 85.2% and a FDR of 0.02

per hour (16 false alarms in 780 h of recordings),

while the previously proposed method (sub-band

MPC) could averagely provide a sensitivity of 79%

EpilepticSeizureDetectionusingBipolarSingularValueDecomposition

181

Figure 4: Proposed measure for entire recording of patient E. Black line is the measure, and the vertical dotted red lines are

seizure onsets. The optimum threshold value is 2 for this patient, which is indicated by horizontal dotted blue line.

and a FDR of 0.05 h

-1

. The results were achieved by

a tradeoff between SS and FDR. However, slightly

higher sensitivity could be reached by setting lower

threshold values, which lead to higher FDRs. Figure

4 illustrates the proposed measure extracted from

whole recording of patient E.

4 CONCLUSIONS

When two adjacent iEEG signals become

increasingly correlated, difference of those signals

(bipolar iEEG) will contain less energy, causing the

SVs of bipolar signal to decrease. Therefore the

observation of sudden decreases in the SVs would

coincide with seizure termination. Moreover,

according to the results, the SVs extracted from

bipolar iEEG signals were apparently robust to the

changes in the state of the iEEG data throughout the

patient’s daily life, producing just 18 false alarms in

780 hours of iEEG recordings. Furthermore, we

observed that patterns of coherency are recurring

evenly for all of the seizures for each particular

patient. This indicates that the build-up, propagation,

and termination of the seizures for a specific patient

follow a common neuronal mechanism.

Furthermore, channel selection affects

significantly the sensitivity parameter of proposed

algorithm. If the recording channels are not placed

close enough to the focus, the seizure spread may

not reach that channel, thus decreasing average

sensitivity. Overall, both placement and number of

selected iEEG channels can substantially affect

detection sensitivity and delays and had be taken

into consideration. In this work, the seizure focuses

were known. Additionally all patients were suffering

from partial epilepsy. Therefore the selection of two

channels on the focus was suggested to satisfactorily

detect seizure events.

ACKNOWLEDGEMENTS

This work was partially supported by EU FP7

211713 EPILEPSIAE Project and iCIS project

(CENTRO-07-0224-FEDER-002003). MB would

particularly like to acknowledge the Portuguese

Foundation for Science and Technology (FCT -

SFRH/BD/71497/2010).

REFERENCES

Aarabi, A., Grebe, R. & Wallois, F. 2007. A multistage

knowledge-based system for EEG seizure detection in

newborn infants. Clinical Neurophysiology, 118(12),

pp 2781-2797.

Acharya, U. R., Sree, S. V. & Suri, J. S. 2011. Automatic

detection of epileptic EEG signals using higher order

cumulant features. Int J Neural Syst, 21(5), pp 403-14.

Adeli, H., Ghosh-Dastidar, S. & Dadmehr, N. 2007. A

wavelet-chaos methodology for analysis of EEGs and

EEG subbands to detect seizure and epilepsy.

IEEE

Trans Biomed Eng,

54(2), pp 205-11.

Bandarabadi, M., Karami-Mollaei, M., Afzalian, A. &

Ghasemi, J. 2010. ECG Denoising Using Singular

Value Decomposition.

Australian Journal of Basic

and Applied Sciences,

4(7), pp 2109-2113.

Bandarabadi, M., Rasekhi, J., Teixeira, C. A. & Dourado,

A. 2014a. Sub-band Mean Phase Coherence for

Automated Epileptic Seizure Detection.

In: Zhang, Y.-

T. (ed.)

The International Conference on Health

Informatics.

Springer International Publishing.

Bandarabadi, M., Teixeira, C. A., Netoff, T. I., Parhi, K.

K. & Dourado, A. Robust and low complexity

algorithms for seizure detection. Engineering in

0 20 40 60 80 100 120

0.5

1.5

2.5

Time (hour)

Amplitude of feature

Preprocessed singular value

BIOSIGNALS2015-InternationalConferenceonBio-inspiredSystemsandSignalProcessing

182

Medicine and Biology Society (EMBC), 2014 36th

Annual International Conference of the IEEE, 26-30

Aug. 2014 2014b. 4447-4450.

Bandarabadi, M., Teixeira, C. A., Rasekhi, J. & Dourado,

A. 2014c. Epileptic Seizure Prediction Using Relative

Spectral Power Features.

Clinical Neurophysiology,

0), pp.

Baranov-Krylov, I. N. & Shuvaev, V. T. 2005. Effects of

selective visual attention in the parietal and temporal

areas of the human cortex using evoked potential data.

Neuroscience and Behavioral Physiology, 35(2), pp

159-164.

Hassanpour, H., Mesbah, M. & Boashash, B. 2004. Time-

frequency feature extraction of newborn EEG seizure

using SVD-based techniques. Eurasip Journal on

Applied Signal Processing,

2004(16), pp 2544-2554.

Kharbouch, A., Shoeb, A., Guttag, J. & Cash, S. S. 2011.

An algorithm for seizure onset detection using

intracranial EEG.

Epilepsy & Behavior, 22,

Supplement 1(0), pp S29-S35.

Klatt, J., Feldwisch-Drentrup, H., Ihle, M., Navarro, V.,

Neufang, M., Teixeira, C., Adam, C., Valderrama, M.,

Alvarado-Rojas, C., Witon, A., Le Van Quyen, M.,

Sales, F., Dourado, A., Timmer, J., Schulze-Bonhage,

A. & Schelter, B. 2012. The EPILEPSIAE database:

An extensive electroencephalography database of

epilepsy patients. Epilepsia, 53(9), pp 1669-1676.

Meier, R., Dittrich, H., Schulze-Bonhage, A. & Aertsen,

A. 2008. Detecting epileptic seizures in long-term

human EEG: a new approach to automatic online and

real-time detection and classification of polymorphic

seizure patterns.

Journal of Clinical Neurophysiology,

25(3), pp 119-31.

Nunez, P. L., Srinivasan, R., Westdorp, A. F., Wijesinghe,

R. S., Tucker, D. M., Silberstein, R. B. & Cadusch, P.

J. 1997. EEG coherency: I: statistics, reference

electrode, volume conduction, Laplacians, cortical

imaging, and interpretation at multiple scales.

Electroencephalography and Clinical Neurophysio-

logy,

103(5), pp 499-515.

Rho, J. M., Sankar, R. & Stafstrom, C. E. 2010.

Epilepsy:

Mechanisms, Models, and Translational Perspectives

CRC Press/Taylor & Francis Group.

Schindler, K., Elger, C. E. & Lehnertz, K. 2007a.

Increasing synchronization may promote seizure

termination: Evidence from status epilepticus.

Clinical

Neurophysiology,

118(9), pp 1955-1968.

Schindler, K., Leung, H., Elger, C. E. & Lehnertz, K.

2007b. Assessing seizure dynamics by analysing the

correlation structure of multichannel intracranial EEG.

Brain, 130(1), pp 65-77.

Sharma, P., Khan, Y. U., Farooq, O., Tripathi, M. &

Adeli, H. 2014. A Wavelet-Statistical Features

Approach for Nonconvulsive Seizure Detection.

Clinical EEG and Neuroscience, 45(4), pp 274-284.

Shoeb, A., Edwards, H., Connolly, J., Bourgeois, B., Ted

Treves, S. & Guttag, J. 2004. Patient-specific seizure

onset detection.

Epilepsy & Behavior, 5(4), pp 483-

498.

Srinivasan, R., Nunez, P., Tucker, D., Silberstein, R. &

Cadusch, P. 1996. Spatial sampling and filtering of

EEG with spline Laplacians to estimate cortical

potentials.

Brain Topography, 8(4), pp 355-366.

Tang, Y., Chorlian, D. B., Rangaswamy, M., Porjesz, B.,

Bauer, L., Kuperman, S., O'Connor, S., Rohrbaugh, J.,

Schuckit, M., Stimus, A. & Begleiter, H. 2007.

Genetic influences on bipolar EEG power spectra.

International Journal of Psychophysiology, 65(1), pp

2-9.

Varghese, G. I., Purcaro, M. J., Motelow, J. E., Enev, M.,

McNally, K. A., Levin, A. R., Hirsch, L. J., Tikofsky,

R., Paige, A. L., Zubal, I. G., Spencer, S. S. &

Blumenfeld, H. 2009. Clinical use of ictal SPECT in

secondarily generalized tonic–clonic seizures. Brain,

132(8), pp 2102-2113.

EpilepticSeizureDetectionusingBipolarSingularValueDecomposition

183