SEPARATION OF BACKGROUND ACTIVITY

AND TRANSIENT PHENOMENON IN

EPILEPTIC EEG USING MATHEMATICAL

MORPHOLOGY

CHANNA BASAVA KOLKUR 1BI11IT400

NITHEESHA. S 1BI11IT406

SHIVAKUMAR. G C 1BI11IT408

SHIVAKUMARA. B 1BI11IT409

PRESENTED BY:

Under the Guidance of:

ASWATHAPPA .P

Epilepsy is a neurological condition which affects the brain.

Approximately 1 in 20 children (and adults) will have a seizure during their life time. This does not necessarily mean they have epilepsy. 1-2 % of world population suffers from epilepsy.

PURPOSE OF THE PROJECT

Around 50-60% of children will grow out of their epilepsy by the time they become adults.

There are children who will continue to have seizures. Some will also have other physical or learning disabilities.

EEG

Electroencephalogram (EEG) is a

measurement of voltages generated by

neurons of brain.

EEG data is collected by placing a set of

electrodes on the scalp or directly on the

surface of brain.

EEG signal information is widely used in

the diagnosis of neurological disorders

such as epilepsy.

BLOCK DIAGRAM

EPILEPTIC

Epileptic EEG data contains transient activities, such as spikes,

muscle activities, eye movements and artifacts.

Epileptic EEG can be divided into two components:

1) Background activity and

2) Transient phenomenon.

Examples of EEG signal background activity (above) and transient activity (below).

1. BACKGROUND ACTIVITY:

The background activity is a spontaneous non-

paroxysmal wave generated from the human brain.

2. TRANSIENT PHENOMENON:

Transients change abruptly from background

activity and may be spikes, sharp waves, eye blinks, muscle

artifacts and others.

TYPES OF EPILEPSY DETECTION

WAVELET TRANSFORM

NEURAL NETWORK

MORPHOLOGICAL

Morphological filtering was chosen over other methods.

Morphological filtering can precisely determine the spikes with a very

high accuracy rate.

It can decompose raw EEG signal into several physical parts.

Background activity and spike component are separated and the main

morphological characteristic of spikes is then retained.

MORPHOLOGICAL FILTERING

1) Dilation

2) Erosion

3) Opening

4) Closing

BASIC OPERATORS

OPENING AND CLOSING

The solid line is the signal, the

thick solid line is filtered result

combined with morphological

opening followed by closing.

DILATION

dil1(i,:)=imdilate(sig,se);

erd1(i,:)=imerode(sig,se);

EROSION

o Different operators smooth or extract different parts of the signal depending

on the shape of a structuring element. Thus one of the tasks is the selection

of the structuring element which separates the spiky areas of the signal.

o Combination of the morphological operators can produce a filter which

separates an original signal into two signals: one signal is defined by the

structuring element and the other is the residue of the signal. Thus the task

is the selection of morphological filter.

SPIKE DETECTION USING MORPHOLOGICAL FILTER

PROCEDURE

Load the signal

Adding noise signal.

Filtered EEG signal.

We are applying the morphological filter to find epilepsy.

Depending on the threshold, the epilepsy is detected.

MATLAB OUTPUT

1.) NORMAL

An original EEG signal Noise signal

Noise with original EEG signal

Noise less EEG signal

Output of a normal person

2.) ABNORMAL

An original EEG signal

Noise signal

Correlation of denoising and decomposed signal.

De-noised signal

Output of a normal person

ADVANTAGES

Morphological filter is an efficient tool in signal processing.

Morphological filtering can precisely determine the spikes with a very high accuracy rate.

Morphology and a threshold based estimation method can estimate the number and location of epileptic spikes in an EEG signal very fast in real time.

Minimizing computational cost

Less time consumption

1.) The morphological filter has relatively low performance overhead.

Because it's simply looking at a finished scene and doing its work,

it will smooth out rough transitions even if they don't occur along

polygon boundaries.

2.) While erosion can be used to eliminate small clumps of undesirable

foreground pixels, e.g. `salt noise', quite effectively, it has the big

disadvantage that it will affect all regions of foreground pixels

indiscriminately.

DISADVANTAGES

CONCLUSION

i. EEG analysis contributes to the normal working of human brain. The EEG

signal patterns also help us study the characteristics of the various

abnormalities.

ii. Epilepsy is a Central Nervous system neurological disorder marked by

sudden recurrent episodes of sensory disturbance, loss of consciousness,

associated with abnormal electrical activity in the brain.

iii. The morphological filter, we have developed a new approach to separate

transients and background activities in the EEG data.

iv. Compared to traditional methods, provides a powerful tool for analyzing

Epileptic EEG.

FUTURE WORK

Combination of MORPHOLOGICAL FILTER and WAVELET

TRANSFORM can be more accurate.

Developing methods for noise detection (i.e. rapid eye movements).

Developing methods for spike classification according to their

shape.

High level analysis methods (i.e. epilepsy classification, a new

drowsiness scale, etc.)

THANK YOU