Epileptic Seizures Prediction.pptx

Epileptic Seizures Prediction:
Challenges and Methods
A SEMINAR SUBMIT TED BY:
HUSSEIN M. HUSSEIN

Seminar Outline
1. Introduction
2. Seizures Prediction
3. Prediction Challenges
4. Prediction Methods
5. Conclusion Remarks
EPILEPTIC SEIZURES PREDICTION: CHALLENGES AND METHODS 2

Introduction
We will cover these topics:
 What is Epilepsy?
 Types of Epilepsy
 EEG Signals

What is Epilepsy?
 Epilepsy is a central nervous system (neurological) disorder
in which brain activity becomes abnormal, causing seizures
or periods of unusual behavior, sensations, and sometimes
loss of awareness.
 Anyone can develop epilepsy. Epilepsy affects both males
and females of all races, ethnic backgrounds, and ages.
 Epilepsy is the second most common neurological disorder
after stroke. Around 50 million people worldwide have
epilepsy.
 Famous people with epilepsy:
 NAPOLEON
 BEETHOVEN
 SIR ISAAC NEWTON
 Leonardo Da Vinci
 Alfred Nobel

Types of Epilepsy
 Focal seizures
 When seizures appear to result from abnormal activity in just one area
of your brain, they're called focal seizures. These seizures fall into two
categories:
 Focal seizures without loss of consciousness.
 Focal seizures with impaired awareness.
 Generalized seizures
 Seizures that appear to involve all areas of the brain are called
generalized seizures. Six types of generalized seizures exist.
 Absence seizures.
 Tonic seizures.
 Atonic seizures.
 Clonic seizures.
 Myoclonic seizures.
 Tonic-clonic seizures..

EEG Signals
 An electroencephalogram (EEG) is a test
that measures electrical activity in the
brain using small, metal discs
(electrodes) attached to the scalp. Brain
cells communicate via electrical
impulses and are active all the time,
even during sleep. This activity shows up
as wavy lines on an EEG recording.
 An EEG is one of the main diagnostic
tests for epilepsy and even seizure
prediction.

Seminar Progress
1. Introduction

 Seizure prediction refers to the ability to forecast the future
occurrence of a seizure.
 Seizure prediction uses pattern recognition methods to
distinguish preictal samples from interictal samples in real-
time.

 Vagus nerve stimulation (VNS) is a medical treatment that
involves delivering electrical impulses to the vagus nerve. It is
used as an add-on treatment for certain types of intractable
epilepsy and treatment-resistant depression.
 VNS devices are used to treat drug-resistant epilepsy
and treatment-resistant major depressive disorder.
 Seizure prediction algorithms when combined with
closed-loop seizure prevention systems will be ideal to
stop a seizure before it occurs.
 prevention systems will be ideal to stop a seizure
before it occurs.

Seminar Progress
1. Introduction

 EEG signal processing.
 The Complexity, Non-Linearity, and Non-stationarity of EEG
signal.
 Patient Monitoring and EEG Recording data
 Short duration of Pre-Ictal.
 Useful EEG database:
 CHB-MIT scalp database.
 Siena Scalp database.
 Freiburg iEEG database.
 No standard framework:
 Different Pre-ictal lengths in research.
 Different segment sizes in research.
 EEG is patient-specific.
 Require training for each patient.

Seminar Progress
1. Introduction

4. Prediction
Methods
We will cover these topics:
 PCA, ICA
 EMD Methods
 Dynamical Methods
 Machine Learning Methods
 Deep Learning Methods

Principal component analysis (PCA)
 Principal component analysis (PCA) is a mathematical
procedure for dimensionality reduction. If the data are from
different sources, it finds a correlation between the data and
transforms it into a set of linearly uncorrelated variables.
 For epileptic seizure detection, because the seizure usually
starts from a small region and spreads all over the brain
eventually, most of the EEG electrodes are correlated to one
another in some way.
 PCA is effective for data analysis related to epileptic seizure
detection for fast processing, especially for seizure onset
detection.

Independent component analysis (ICA)
 Independent component analysis (ICA) is a computational
method for separating a multivariate signal into additive
subcomponents. This is done by assuming that at most one
subcomponent is Gaussian and that the subcomponents are
statistically independent of each other.

EMD Methods
 EMD is a method of breaking down a signal without leaving
the time domain. It can be compared to other analysis
methods like Fourier Transforms and wavelet
decomposition. The process is useful for analyzing natural
signals, which are most often non-linear and non-stationary.
 The fact that the functions into which a signal is
decomposed are all in the time domain and of the same
length as the original signal allows for varying frequencies in
time to be preserved. Obtaining IMFs from real-world signals
is important because natural processes often have multiple
causes, and each of these causes may happen at specific
time intervals.

Chaos Theory and Dynamical Analysis
 Chaotic systems have an apparently noisy behavior but are
in fact ruled by deterministic laws. They are characterized by
their sensitivity to initial conditions. This means that similar
initial conditions give completely different outcomes after
some time.
 Calculate the degree of determinism (or random nature),
complexity, chaoticity.
 The correlation dimension, Lyapunov exponents, and
Kolmogorov entropy.

Machine Learning Methods
 Machine learning (ML) is a type of artificial intelligence (AI).
Machine learning algorithms use historical data as input to
predict new output values.
 The data are the extracted features with labels “Pre-ictal”
and “Inter-ictal”.
 An SVM is an efficient tool for nonlinear binary classification
problems as it projects the nonlinear problem to a higher-
dimensionality space where the problem can more likely be
treated as a linear problem.
 The SVM then creates a hyperplane between the two
classes, separating them with the largest possible distance.

Deep Learning Methods
 Deep Learning Methods has the capability of dealing with
noisy raw EEG signals and has inherent features of learning
and classification property.
 Convolutional Neural Network (CNN), Long Short Term
Memory (LSTM)
 Require large numbers of training samples.

Seminar Progress
1. Introduction

 Epilepsy and prediction of seizures are discussed.
 Challenges and some methods are clarified.
 Since today there is no medically approved method.
 Since EEG is patient-specific discriminating features will vary
among patients.
 Require searching for the best discriminating features for
given data.

Thank You!
Epileptic Seizures Prediction: Challenges and Methods

Epileptic Seizures Prediction.pptx

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