Seizure Prediction System:

photohomoeopathAI and Robotics

Nov 24, 2013 (3 years and 9 months ago)

86 views

Seizure Prediction System:

An Artificial Neural Network
Approach

David Gilpin

Chris Moore

Advised by: Pradeep Modur, MD

The Problem


Epileptic (grand mal) seizure can happen
anytime, anywhere



There is no warning to its imminent onset


Many electroencephalographers have
increased interest in computer based
recognition


Any warning could give time for
preparation or prevention

Facts on Seizures


Seizures affect 0.5% of the population regularly


1.5
-
5.0% of the population may have a seizure in
their lifetime


No identifiable cause


EEG data appear to synchronize prior to a seizure


Some treatment available


No reliable prevention method exists


Project Overview


EEG data has specific seizure “predictors” within
(spikes)


Signal processing can analyze spikes


Results of analysis are normalized


Normalized data is used to train a neural network


Trained network tested with EEG data containing
both epileptic and non
-
epileptic activity


Background / Research


The use of an Artificial Neural Network in
seizure
detection


Project Goal

Use the ANN approach to detect pre
-
seizure
events (spikes), prior to the onset of a
seizure, in order to give an epileptic patient
warning that a seizure is imminent

Project Demands / Wishes


Demands


Successfully detect spikes for prediction of
seizures


Wishes


Detect severity of seizure


Become a fully automated system (implantable)


Project Timeline

January

February

March

April

Background
Research

Testing Different Data
Analysis methods

Implementation of
signal processing and
Neural Network

Testing / fine
tuning of neural
network.

Project
presentation

Materials


Persyst
®


Data Acquisition


Microsoft Excel
®


Data Formatting


Matlab
®

Signal Processing Toolkit


Extraction of Data Parameters


Matlab
®

Neural Network Toolkit


Design of Artificial Neural Network


Data Acquisition and
Formatting


EEG data taken from VUMC patients over
24 hour periods


Data exported from Persyst® into a text file


Data converted into M
-
file for use with
Matlab


Data collected @ 200 Hz in 2 second epochs

Signal Processing


Extraction of Five Parameters:


Rising Time


Falling Time


Duration of Spike


Max Peak
-
To
-
Peak


Peak Frequency (FFT)


Standard 20 EEG signal


1 channel EKG signal

Neural Network


Normalized parameters used as inputs


3 layered feed
-
forward back
-
propagation network:


5 node input layer


5 node hidden layer


Output layer with 2 outputs (1 = seizure 0 = no seizure)


~100 sample parameter sets used to train network


~20


30 simulation samples

Current Status


Signal Processing


Designing “Context Calculator”


Normalizing Data


Neural Network


Formatting Inputs for implementation


Making sure weights are assigned properly

Future Work


Upon completion of network training, we
will simulate network with many sets of test
data


Analysis of the network will be done to
make sure every node is operating properly


After finalizing the network the project will
move towards automation

Main References


Webber, W.R.S., et al. An approach to seizure detection using an
artificial neural network (ANN). Electroenceph. Clin. Neurophysiol.,
1996, 98: 250
-
272


Pradhan, N., et al. Detection of Seizure Activity in EEG by an
Artificial Neural Network., Computers and Biomedical Research,
1996, 29: 303
-
313


Rumelhart, D. Parallel Distributed Processing, 1986: The MIT Press.


Eberhart, R.C., Dobbins, R.W. Neural Network PC Tools, 1990:
Academic Press, Inc.