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Epileptic State Detection: Pre-ictal,

Ictal, Inter-ictal

Apdullah YAYIK* Yakup KUTLU

Esen YILDIRIM Serdar YILDIRIM

Mustafa Kemal University

*Presenter

Propose of Study

Epileptic State Detection The goal of this study is discrimination of three states

of an epileptic patient: before (pre-ictal), during (ictal)

and after seizure (inter-ictal) using artificial

intelligence techniques. For this purpose, EEG

database obtained from Children's Hospital Boston

(CHB) is used.

What is EEG Signal

Electroencephalography (EEG) is the recording of

electrical activity along the scalp.

EEG measures voltage fluctuations resulting from ionic

current flows within the neurons of the brain

What is Epilepsy

Epilepsy is explained by recurring instant seizures due to the

instantaneous development of synchronous firing in the cerebrals

cortex caused by lasting cerebral abnormality .

Database

Children's Hospital Boston

5 Male (3-22 ages), 17 Female (1.5-19 ages)

Second Order Difference Plot

Second-Order Difference

x axis =X(n +1) -X(n)

y axis = X(n+2)-X(n+1)

-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1 4

32

3-3-3-1 6-5-1-0 1-1-1-0 6-5-3-0

46

5

3

0

Circle Region Parameters

Classification

Artificial Neural Network

3 hidden layers (16-9-3) as shown in Figure, sigmoidtransfer functions and back propagation algorithm isused.

Cross-Validation

o Cross validation determines how the results of a statistical analysis will

generalize to a new data set.

Classification Results

Classifier Class Precision RecallROC

Area

Kappa

Statistic

Mean

Absolute

Error

Root Mean

Squared

Error

Overall

Accuracy

(%)

Multi-Layer

Neural

Network

pre-seizure 99.00% 99.00% 0.99

0.98 0.01 0.07 98.70Ictal 97.00% 97.60% 0.99

Inter-Ictal 100.00% 100.00% 0.99

k-Nearest

Neighbor

pre-seizure 94.10% 94.10% 0.95

0.92 0.03 0.18 94.71Ictal 92.10% 92.10% 0.93

Inter-Ictal 98.00% 98.00% 0.99

Naive Bayes

pre-seizure 98.10% 100.00% 0.91

0.95 0.02 0.11 95.14Ictal 97.30% 94.10% 0.83

Inter-Ictal 96.10% 97.00% 0.96

Related Works

Study Year Database FeaturesClassification Problem

(Epileptic States)Classifier

Classification

Accuracy (%)

[20] 2009 Freiburg dataset [19] Wavelet Transform pre-ictal and inter-ictal Convolutional NN 71,00

[1] 2009 Individual Approximate Entropy normal and ictal Elman NN 93,33

[5] 2011National Taiwan

University HospitalApproximate Entropy

normal and inter-ictal and

ictal SVM 98.1

[6] 2012Andrzejak Dataset

[22]

Discrete Wavelet

Transformnormal and ictal ANN 98,87

[7] 2012Andrzejak Dataset

[22]Approximate Entropy normal and ictal ANN 93,43

[8] 2012Andrzejak Dataset

[22]

Fast Independent

Component Analysis normal and ictal ANN 99,50

[9] 2013Andrzejak Dataset

[22]

Discrete Wavelet

Transformnormal and ictal SVM 98,00

[21] 2014Andrzejak Dataset

[22]

Empirical Mode

Decomposition

Second-Order Difference

normal and ictal ANN 95,00

This

Study2014 CHB Dataset [10] Second Order Difference pre-ictal, ictal and inter-ictal ANN 98,70

Results

This study shows that the state of an epileptic

patent can be classified as before, during and

after seizure (pre-ictal, ictal and inter-ictal) using

SODP features and machine learning

algorithms.

Questions?

Apdullah YAYIK

apdullahyayik@gmail.com

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