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Student Researcher and Supervisor

This dissertation is taken by Fatemeh Bamdad, Master of Science in Biomedical

Engineering at University of Surrey,UK, with background of Biomedical engineering

at Islamic Azad University of Tehran, Iran, under the supervision of Dr Daniel

Abásolo,Senior Lecturer in Biomedical Engineering, University of Surrey,UK.

Project Title

Non-linear analysis of the electroencephalogram in epileptic patients.

Project Objectives

The aim of this project is analysing the electroencephalogram (EEG) in epileptic

patients with non-linear dynamics techniques, estimating the effectiveness of these

methods in detection and possible prediction of epileptic seizures. We are going to

focus on Approximate Entropy method in order to analysis the signal.

Background to the Project

Epilepsy is described as the spontaneous and unpredictable attack of seizures. This

seizure depicts the extreme and hyper-synchronized activity of neurons in the brain.

Epilepsy is one of the common neurodegenerative disorders which around 1% of the

world’s population suffer from this disease [1]. The seizures may occur in almost

every cortical region. These seizures never make the patient aware of occurrences. In

some cases during a surgical process the region of the brain that probably generates

seizures can be removed. These kinds of surgeries are both risky and expensive and of

course have their own side effects. In some other patients a long-term treatment with

antiepileptic drugs is needed. Nowadays, the neurologists are trying to find a way to

predict the occurrence of epileptic seizures for the reason that therapeutic possibilities

would change dramatically by means of anticipation [2].

For several decades, electroencephalography has been used to pave the way for

investigation of brain electrical activity in different physiological and pathological

states [3].

The electroencephalogram (EEG) is a complex signal with statistical properties

including time and space. This signal has various characteristics, such as the existence

of limit cycles (alpha activity, ictal activity), index of the percentage of burst

supersession (which is related to depth of anaesthesia and percentage of

consciousness), jump phenomena (hysteresis), amplitude dependent frequency

behaviour (the smaller the amplitude the higher the EEG frequency) and frequency

harmonics, are can be listed of typical properties of nonlinear systems. Observation of

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different kinds of nonlinearities in the EEG recorded from epileptogenic brain can

clearly prove the concept that epileptogenic brain is a nonlinear system [4].

By introducing the nonlinear techniques, researchers have accentuated the importance

of this issue that the EEG of the epileptic brain is a nonlinear signal with both

deterministic and perhaps chaotic properties [4].

On an electroencephalogram (EEG), the period during which a seizure happens is

referred to as the ictal period; the period between seizures is, thus, the interictal period.

One of the sign of epilepsy is the presence of spikes in the EEG during this interictal

period [5].

During the generation of a seizure, harmonized epileptic brain activity begins to be

observed in a small region of brain and this activity widens to other brain regions. This

process gradually changes the EEG wave. By analysing this wave we aim to detect and

possibly predict seizures. This would improve the quality of life for epileptic patients

who cannot be treated effectively.

The identification of an epileptic seizure can also be achieved by the observation of the

increase in synchronized activity and increased signal complexity [6].

Although the traditional linear analysis, such as Fourier transforms and power

spectrum analysis has been used for seizure prediction, it has some limitations.

Consequently non-linear methods have superseded the old fashion linear analysis.

Several non-linear methods have been known in recent several decades. The ‘chaos

theory’ expresses the non-linear behaviour of a dynamical system which becomes

possible to study self-organization and pattern formation in the complex neuronal

networks of the brain [7].

The most common methods of chaos analysis include the correlation dimension [8],

Lyapunov exponents [9] and reversion curves and related analysis [10]. Although these

standards express the general non-linear dynamics of a system, they cannot illustrate

the slight differences between dynamical states, especially when the data is short and

noisy. In particular, we have to bear in mind that EEG signals are always contaminated

with noise [11]. Besides, another disadvantage of using the chaos analysis is that an

inappropriate selection of the data may affect the results [12]. Also, to obtain a reliable

response with these methods, we need long and stationary data while the EEG signals

can only be considered stationary for short periods [12]. Recently, in some cases

researchers have used both linear and non-linear methods at the same time to diagnose

the epilepsy, but this combination method is complex and expensive for clinical usage

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[13]. The gloss of all of the results via any kinds of these methods in terms of

‘functional sources’ and ‘functional networks’ illustrates three basic patterns of brain

dynamics [7] Other non-linear techniques without the drawbacks of the chaos theory

techniques are needed to analyse the EEG:

(i) Normal, resting state in healthy volunteer , steady dynamics during a no-task; this

situation is described by a high dimensional complexity and a relatively low and

fluctuating level of synchronization of the neuronal networks;

(ii) Hyper synchronous, highly non-linear dynamics of epileptic seizures;

(iii) Dynamics of degenerative encephalopathy with an abnormally low level of

between area synchronization [7].

Literature review of the subject

Studies conducted by different researchers illustrate how many different methods were

applied to diagnosis and/or prediction of seizure in epileptic patients. Some of the most

important of these methods are listed below.

As far as history is concerned, the use of nonlinear time series analysis emerges in the

early 1980s. They used largest Lyapunov exponent to illustrate changes in brain

dynamics. Researchers in their first studies have applied correlation dimension as a

measure for neuronal complexity in the EEG or the correlation density to predict the

seizure a shortly before the occurrence. These studies traced by other methods

including dynamical similarity and dynamical entrainment. Recently, an effort on

prediction of seizure by means of certain signal patterns (bursts) and changes in signal

energy has been accomplished [14].

In 1998 Osorio et al. have introduced a method to access automated seizure detection.

Their method based on frequency analysis. They combined linear and non-linear

filtering techniques as well as using discrete wavelet transform. In order to minimize

the noise, they first analyzed intracranial signals and then developed for scalp

recordings. Their incipient results revealed that this new method is the fastest and

most reliable to date. Their experiment was on 5 patients, and totally 20 seizure

segments and 7 interictal segment recorded. By means of this method, the seizure

detection, on average, was 16 seconds before occurrence of seizure. No seizures losing

and no false detections were reported [15].

At the same time, 1998, Lehnertz and Elger used effective correlation dimension

method [4]. First of all they focused on the index of Lyapunov and found the positive

Lyapunov exponent in EEG signals of epileptic patients. In the second step, they

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studied intracranial and scalp EEG recordings in patients whom afoul of intractable

seizures in their mesial temporal origin. They used the non-linear extension of an

autoregressive (AR) model. This model is using to illustrate the existence of self-

sustained nonlinear oscillations during the seizure (ictal EEG). In this step they

clarified that nonlinearities in interictal EEG generated by the epileptogenic focus [4].

In 2001, Le Van Quyen et al. analysed 23 patients with temporal-lobe epilepsy [16].

They used the similarity index method and predicted the seizure about 7 minutes

before occurrence. This method measures the similarity between pairs of EEG

windows and calculates the cross correlation integral between the two dynamics. The

similarity index is a number below or equal to 1. Depends on the stationary of the EEG

signal or changes in the dynamical state the yield respectively will be equals to below

1 or 1 [16].

American Clinical Neurophysiology Society in 2001 published an article in which

Jerger et al. compared seven different methods of early seizure detection [17]. They

worked on both linear and non-linear methods including analysis of power spectra,

cross-correlation, principal components, phase, wavelets, correlation integral and

mutual prediction. They concluded that all seven methods were successful. They did

not show clear main differences between linear and non-linear methods, but they

signified that the analysis of phase performed slightly better than the other methods.

The phase analysing is more sensitive in detecting weakly coupled nonlinear systems

[17].

In the late 2002, Mormann et al. used another method which was synchronization

decrease [14]. They recorded the signals from 18 epilepsy patients and used moving

window techniques in order to analysing their datasets. By analysing the

synchronization between EEG signals from different regions of the brain they revealed

that a large majority of analyzed seizures are recognizable by a decrease in

synchronization between specified recording sites [14].

Thomas Maiwald et al. published an article in 2004 in which they compared three

different non-linear methods to predict the epileptic seizure [3]. These three methods

were Dynamical similarity index, Effective correlation dimension and Increments of

accumulated energy. They stated that by means of three main characteristics we can

determine the best way for prediction of seizure including maximum false prediction

rate (FPR max), the seizure prediction horizon (SPH) and seizure occurrence period

(SOP). By calculating each three characteristics for all three methods, they deduced the

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dynamical similarity index achieves sensitivity between 21 and 42%. This method was

the most sensitive one among these three methods. The drops of the effective

correlation dimension were between 13 and 30%, which was the least sensitive

methods, and finally but yet importantly method, increments of the accumulated

energy lie between 18 and 31% [3].

Entropy, a measure of ‘‘irregularity” or ‘‘uncertainty”, was initially introduced by

Shannon (1948) [18]. The famous Shannon entropy formula (η) is calculated as

follows:

𝜂 = − Ʃ𝑘 𝑃𝑘 log𝑃𝑘 (1)

where pk is the probabilities of a datum in bin k. It can be calculative from this

equation that the entropy is high for a broad uniform probability distribution and low

for a narrow peaked distribution [18].

This method also detects changes when an episodic behaviour happened while it

cannot be noticeable in peak incidences or amplitudes [19].

Approximate Entropy (ApEn) was introduced by Pincus at 1991 [19]. This is a non-

linear method for analysing the irregularity in time series signals [19]. The more

irregular the signal is, the larger the ApEn values are, and vice versa [18]. This method

can be applied to relatively short, noisy data sets [19].

One of the most important aspects of applying ApEn is it can discriminate both general

classes of correlated stochastic processes and noisy deterministic systems [19].

ApEn can be considered as a member of statistics family which emerges to provide a

finite sequence, statistically valid formula on the road to discern data sets. The ApEn

measures the regularity and estimates both dominant and subordinated patterns in data

[19], [20].

Preliminary evidence has shown its usefulness in different EEG studies [20].

In 2001, Christoph Bandt and Bernd Pompe introduce a new method which was

permutation entropy [21]. Calculation of time series is simple and fast, in addition the

Permutation entropy is a noise alimentation standard [21], [22].

The satisfactory utilization of this method has been reported in the analysis of

biomedical signals [23], [24].

The Gabor atom density (GAD) and Autoregressive measure of synchrony (S measure)

are two different but complement methods which respectively refer to quantification

of the time–frequency components of the EEG and characterize the complexity of the

EEG signal and allows for characterization of the degree of synchrony of the EEG

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signal. These methods have been used to recognize if there is an identifiable preictal

period or not. By means of these methods, the researchers deduced that both GAD and

S measures reveal ictal and prolonged postictal changes; however, there were no

significant preictal changes in either complexity or synchrony. Any application of

methods to detect preictal changes must be tested on seizures sufficiently separated to

avoid residual postictal changes in the potential preictal period [6].

A fuzzy rule-based system is another method for automated epileptic seizure detection

which has been employed by A.Aarabi et al. in 2009 [18]. This system based on

knowledge extracted from Invasive EEG (IEEG) and then spatio-temporally integrated

using the fuzzy rule-based system. This method discriminates the boundaries between

interictal and ictal intracranial EEG patterns. Since it is needed to place the intracranial

electrodes in to brain or on the cortex, patient should impose on surgical procedure.

This issue is the most important disadvantage of this method as it is invasive and

always has a risk of infection and cerebral oedema. Beside, the utilization of this

system has many advantages that should not be neglected such as: there was only one

missed seizure and the system sensitivity was 98.7% [18].

Details of investigation

We estimate five steps to meet our objectives.

The first step is reviewing the EEG database which is going to be used in the project.

We have used the collection of data collected by Ralph G. Andrzejak et al, Department

of Epileptolog University of Bonn, Germany [25].

Figure1. Scheme of the locations of surface electrodes according to the international

10-20 system [25].

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They collected five sets (specified A-E) and each set contains 100 single channel

Electroencephalography (EEG) segments of 23.6-sec duration. The segments taken

from surface EEG recordings of five healthy volunteers using a standardized electrode

placement scheme is relative to sets A and B. Volunteers were relaxed in an awake

state, while their eyes were open in A and their eyes were closed in B. Sets C, D and E

are relevant to EEG archive of pre surgical diagnosis. The archive was EEGs from five

patients whom under the complete seizure control after reaction of one of the

hippocampal formations, which was therefore correctly diagnosed to be the

epileptogenic zone. Set D contains segments recorded within the epileptogenic zone,

and Set C includes the segments from hippocampal formation of the opposite

hemisphere of the brain. Set E include seizure activity whilst sets C and D consist

activity measured continuously during seizure. The sampling rate was 173.61Hz Band-

pass filter settings were 0.53–40 Hz [25].

The second step is to review published methods for analysis of the EEG in epileptic

patients.

In the third step we are going to review published non-linear analysis methods for

biomedical signal processing.

We will write software programs in Matlab®

to analyse the EEG with non-linear

methods in the fourth step.

Finally, yet very importantly step, we will validate the implemented methods with

EEG signals from the epileptic patients’ database.

Resources needed

As the data of this project are already available, the only resource needed will be

Matlab® software to meet our objective. Since my background is Biomedical

Engineering I have been using this software during the Bs degree and I have installed

it on my laptop.

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Date 15

Nov

30

Nov

15

Dec

30

Dec

15

Jan

30

Jan

15

Feb

30

Feb

15

Mar

30

Mar

15

Apr

29

Apr

15

May

30

May

15

Jun

30

Jun

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Jul

30

Jul

05

Aug

Literature search

Writing the draft of interim

Submission the interim

Project planning

Reviewing the EEG database

Reviewing published methods for analysis of the EEG in

epileptic patients

Reviewing published non-linear analysis methods for

biomedical signal processing

Write software program in Matlab®

Extraction non-linear feature and analysis table

Estimating the effectiveness of above methods

Software testing

Writing the draft

Correction the draft

Submission

Project Plan

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References

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pp 22­30.

[2] Rieke Ch, Mormann F, Andrzejak R, Kreuz T, David P, Elger CH, Lehnertz K.

(2003). Discerning nonstationarity from nnonlinearity in sseizure-free and preseizure

EEG recordings from epilepsy patients. IEEE transactions on biomedical engineering

volume 50, No.5, pp 634-639

[3] Maiwald T,Winterhalder M, Aschenbrenner-Scheibe R, Henning U.Voss, Schulze-

Bonhage A, Timmer J. (2004). Comparison of three nonlinear seizure prediction

methods by means of the seizure prediction characteristic. Physica D 194, pp 357–368

[4] Lehnertz EK, Elger CE. (2000). Chaos in the brain? World Scientific, Singapore,

in press, pp 1-22.

[5] Slutzky MW, Cvitanovic P, Mogul DJ. (2003). Manipulating Epileptiform Bursting

in the Rat Hippocampus Using Chaos Control and Adaptive Techniques.IEEE

transactions on biomedical engineering, volume .50, No.5, pp.559-570

[6] Jouny CHC, Franaszczuk PJ, Bergey GB. (2005). Signal complexity and synchrony

of epileptic seizures: is there an identifiable preictal period?.Clinical

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[11] Protopopescu V, Hively L.(2005). Phase space dissimilarity measures of

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[22] Cao Y, Tung W, Gao J, Protopopescu V, Hively M. (2004). Detecting dynamical

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[23] Frank B, Pompe B, Schneider U, Hoyer D. (2006). Permutation entropy

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electroencephalogram: a measure of anaesthetic drug effect. British Journal of

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