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 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® Microsoft Excel® Matlab® Signal Processing Toolkit 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 Neural Network 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.