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Research Proposal on

A new offline and online classifier of extracted EEG signal

Abstract:

Seizure is a general term that refers to a sudden malfunction in the brain that causes someone to

collapse, convulse, or have another temporary disturbance of normal brain function, often with a lossor change in consciousness. Epilepsy is a chronic disorder of the central nervous system that

predisposes individuals to experiencing recurrent seizures. Since 1970 numerous techniques have

been applied for detecting epileptic seizure activities. This proposal is made to introduce a new

method intended to ease the burden of intractable seizures. The method uses machine learning to

construct patient-specific classifiers that are capable of rapid, sensitive, and specific detection of

seizure activities. The classification method uses a set of features or parameters to characterize each

problem and finally draws the conclusion with a specific anomalous history (if any).

Introduction:

The human brain is obviously a complex system and exhibits rich spatiotemporal dynamics. Among

the noninvasive techniques for probing human brain dynamics, electroencephalography (EEG)provides a direct measure of cortical activity with millisecond temporal resolution. EEG is a record of

the electrical potentials generated by the cerebral cortex nerve cells. There are two different types of

EEG depending on where the signal is taken in the head: scalp or intracranial. There are electrodes

which are placed on the scalp or implanted in the brain by which the voltage of the neuron current is

measured. Then the voltage difference between electrodes is amplified before being transmitted to a

computer program to display the tracing of the voltage potential recordings. Epileptic seizure is an

abnormality in EEG recordings and is characterized by brief and episodic neuronal synchronous

discharges with dramatically increased amplitude. This anomalous synchrony may occur in the brain

locally (partial seizures), which is seen only in a few channels of the EEG signal, or involving the

whole brain (generalized seizures), which is seen in every channel of the EEG signal. EEG signals

involve a great deal of information about the function of the brain. But classification and evaluationof these signals are limited. Since there is no definite criterion evaluated by the experts, visual

analysis of EEG signals in time domain may be insufficient. Therefore, some automation and

computer techniques have been used for analyzing the signal characteristics.

Method:

The EEG data will be taken from 24h EEG recordings from both epileptic patients and normal

subjects. The patient and subjects will be consisted of different aged people. For analysis purposes

four/five bipolar channels will be taken as reference. Recording will be done under video control to

have an accurate determination of the different stages of the seizures. The data from recordings will

be passed through a band pass filter (1-70Hz). The filtered EEG signal will be segmented to 6s (no of

samples) durations. The EEG found from the specified channels will be digitized for processing.

Process Used to EEG signals:

In the earlier days of automatic, EEG processing included Fourier transform which dictated some

observations that the EEG spectrum contains some characteristic waveforms that fall primarily within

four frequency bandsdelta (

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method is more suitable. It is well suited to locating transient events, which may occur during

epileptic seizures. The capability of this mathematical microscope to analyze different scales of

neural rhythms is shown to be a powerful tool for investigating small-scale oscillations of the brain

signals.

Feature Extraction:This portion is an overview of measures that are commonly used to characterize EEG time series.

These are univariate linear/nonlinear measures and bivariate linear/nonlinear measures. Univariate

linear measures included Statistical moments, Spectral band power analysis, Spectral edge frequency

analysis, Auto correlation and Autoregressive modeling. Univariate nonlinear measures included

Correlation sum, Correlation dimension, Correlation density, Correlation entropy, Marginal

predictability, Dynamical similarity index, State space dissimilarity measures, Surrogate time series

and surrogate correction and Loss of recurrence. Bivariate linear measures included Maximum Linear

cross-correlation and Linear-coherence. Bivariate nonlinear measures included Non-linear

interdependence, Dynamical entrainment and Phase synchronization. Most commonly features which

are tried by the experts while EEG analysis are Correlation entropy, Spectral band power analysis and

Autoregressive modeling. Rest of the features can be taken into account during my research and a

comparison can be drawn after analysis.

Classifications of EEG signals:

There are several methods which are utilized to classify the extracted features as seizure or non-

seizure events. Below is given the short description of each method.

1) Naive Bayes Classifier:

The Naive Bayes classifier is designed for use when features are independent of one another within

each class, but it appears to work well in practice even when that independence assumption is not

valid. It classifies data in two steps followed by training and prediction step.

2) k-NN Classifier:

k-NN classifier is based on learning by analogy. Given an unknown sample, it searches the pattern

space neighbors that are the closest to the unknown sample. Closeness is defined in terms of distance.

The unknown sample is assigned the most common class among its neighbors.

3) Decision Trees:

Decision trees find explicit and understandable rules-like relationships among the input and output

variables using search heuristics. Search heuristics use recursive partitioning algorithms to split the

original data into finer subsets. The algorithm has to find the optimum number of splits and determine

where to partition the data to maximize the information gain. Decision trees are built of nodes,

branches, and leaves that indicate the variables, conditions, and outcomes, respectively.

4) Particle swarm optimization:

In computer science, particle swarm optimization (PSO) is a computational method that optimizes a

problem by iteratively trying to improve a candidate solution with regard to a given measure of

quality. PSO optimizes a problem by having a population of candidate solutions, here dubbed

particles, and moving these particles around in the search-space according to simple mathematical

formulae over the particle's position and velocity. Each particle's movement is influenced by its local

best known position and is also guided toward the best known positions in the search-space, which

are updated as better positions are found by other particles. This is expected to move the swarm

toward the best solutions.

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5) Logistic regression:

Logistic regression can be considered a special case of linear regression models. However, the binary

response variable violates normality assumptions of the general regression models. A logistic

regression model specifies that an appropriate function of the fitted probability of the event is a linear

function of the observed values of the available explanatory variable.

6) LLS and LDA:

Linear least squares (LLS) method finds a best fitting linear model that minimizes the mean square

error between the system output and the desired output. Linear discriminant analysis (LDA) uses

hyperplane to find the linear combination of features that best separates two or more classes of

objects or events. Usually within-class, between-class, and mixture scatter matrices are used to

formulate the criterion of searching the hyperplane that the distance between means of classes is

minimized and the interclass variance is maximized.

7) Neural networks:

MLPNN (Multi-Layer Perceptron Neural Network) is widely used in biological phenomena. This

computing system is made up of large number of simple, highly interconnected processing elements.

The architecture of MLPNN may contain two or more layers. Input layer is the first layer which its

number of neurons is equal to the number of selected specific features. Output layer is the last layer

which determines the desired output classes. The intermediate layers (hidden) may be added to

increase the ability of MLPNN mostly useful for nonlinear systems. In the present study, MLPNN

consisted of one input layer, one hidden layer with 21 nodes and one output layer. Back propagation

algorithms are used to train a MLPNN.

8) SVM:

Support vector machine (SVM) uses a hyperplane to identity classes. The hyperplane that maximizes

the margin, i.e., the distance from the nearest training points, is selected by SVM. Maximizing the

margins is known to increase the generalization capabilities. The SVM performs structural risk

minimization and creates a classifier with minimized VC dimension.

Advantages and Disadvantages of different Classifiers:

Classifier Advantage Disadvantage

Neural

Network

They can handle problems with very

many parameters, and they are able to

classify objects well even when the

distribution of objects in the N-

dimensional parameter space is very

complex

They are notoriously slow, especially in

the training phase but also in the

application phase.

K-NN They are easy to implement. They can

also give quite good results if the

features are chosen carefully

The method is also rather slow if the

training set has many examples. The most

serious shortcoming of nearest neighbor

methods is that they are very sensitive to

the presence of irrelevant parameters.

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Classifier Advantage Disadvantage

Decision Trees Decision trees are usually much faster

in the construction (training) phase

than neural network methods, and they

also tend to be faster during the

application phase.

They are not as flexible at modeling

parameter space distributions having

complex distributions as either neural

networks or nearest neighbor methods. In

fact, even simple shapes can cause thesemethods difficulties.

Naive Bayes

Classifier

An advantage of the naive Bayes

classifier is that it only requires a small

amount of training data to estimate the

parameters (means and variances of the

variables) necessary for classification.

The disadvantage is that the classifier can

model only simple distributions.

Logistic

Regression

It can add explicit interaction and

power terms, It does not require that

the independents be interval and

unbounded

it requires much more data to achieve

stable, meaningful results

SVM Produce very accurate classifiers, lessover fitting, robust to noise

Computationally expensive, thus runsslow.

Conclusion:

Though it requires huge no of literature surveys in various branches of mathematics and sciences I

had been trying to figure out some of the EEG features and classifiers throughout the proposal. I will

be manipulating all the features and addressing those classifiers during my research period. With my

best knowledge on MATLAB, I hope, I will be able to establish a new methodology on EEG seizure

detection. I must confess that the only picture I have in my mind from my undergraduate years till

now is that of a dedicated researcher in my chosen field of advanced research; being fully aware of

the intellectual rigor and fortitude it claims. I firmly believe in the lesson life taught me over past few

years: our lives are too short to relish our dreams; making reality out of dreams is what does matter. I

just need the right opportunity to prove myself worthwhile.