1. Seizure Prediction System: An Artificial Neural Network Approach
David Gilpin
Chris Moore
Advised by: Pradeep Modur, MD

2. The Problem Epileptic (grand mal) seizure can happen anytime, anywhere
There is no reliable, physical warning to its imminent onset
Many electroencephalographers have increased interest in computer based recognition
Any warning could give time for preparation or prevention

3. Facts on Seizures Seizures affect 0.5% of the population regularly
% 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

4. 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

5. Background / Research
The use of an Artificial Neural Network in seizure detection

6. 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

7. Project Demands / Wishes
Successfully detect spikes for prediction of seizures
Wishes
Detect severity of seizure
Become a fully automated system (implantable)

8. Project Timeline January February March April Background Research
Testing Different Data Analysis methods
Implementation of signal processing and Neural Network
Finalizing parameter extraction
Normalize Data
Training neural network
Testing / fine tuning of neural network.
Project presentation

9. 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

10. Data Acquisition and Formatting
EEG data taken from different VUMC patients over 24 hour periods
Data exported from Persyst® into a text file
Data converted into M-file for use with Matlab
Data 200 Hz in 2 second epochs

11. 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

12. Algorithm Design All 20 channels are analyzed at same time
Signal processing algorithm selects “candidate” spikes—it does not classify!
Many waveforms look like a spike (eyeblinks, artifacts, muscle twitches)
Algorithm then extracts the six parameters for the candidate spikes
Target spike waveform values to be used in network are selected by Dr. Modur

13. 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 1 output
~100 sample parameter sets used to train network
~20 – 30 simulation samples
Output threshold range from
If above threshold, spike; if below, no spike

14. Fine Tuning
Look at visible nodes; make sure all weights are functioning
Data from many, different spikes and patients will reduce individuality
Training proceeds with “split data” technique
Spike can occur anywhere from 10 secs to 10 min. before seizure

15. Final Product (Ideal)
Device would be implantable with external output computer; wireless connection
If and when seizure is detected, computer responds with ‘beep’ or vibration
Computer then gives several options based on severity of spike:
VNS
Other medical treatment
Nothing at all

16. Channel Synchronization
Seizure patterns with spikes appearing in even or odd numbered channels
Spikes that only occur in one channels are more likely to be artifacts
Higher number of spike channels, higher confidence level (only takes two, though)
Certain combinations rule out seizure entirely (e.g. eyeblinks, muscle twitches)
Simple rule-based program in C++ or MATLAB can sort through all channels

17. Patent Search
A patent does exist on a seizure warning and prediction system
System does not use neural network approach
Uses chaotisity profiles to determine reduced randomness in signal
Gives time and severity of seizure
Inventors: Iasemidis, Leonidas D; Sackellares, James. Appl. No:
Persyst® has developed detection system; is not neural network based

18. Market Cost Analysis Cost of prediction program = $2000
(approximated from Persyst®)
Cost of implantable detector = $1000
(approximated from Cyberonix®)
Cost of readout computer = $400
(approximated from Palm®)
Total Cost = $3400

19. Cost vs. Benefits Costs Benefits Price of device Could save lives
Surgical risks of implantation
Could prevent seizures from happening
Hassle of wearing implantable device
Take the “randomness” out of a seizure
Risks of device failure; false pos./neg.

20. Current Status Signal Processing Neural Network
Redesigning “Context Calculator”
Normalizing Data
Neural Network
Formatting Inputs for implementation
Making sure weights are assigned properly

21. 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
Also researching cluster analysis as a possible collaborative approach

22. Main References
Webber, W.R.S., et al. An approach to seizure detection using an artificial neural network (ANN). Electroenceph. Clin. Neurophysiol., 1996, 98:
Pradhan, N., et al. Detection of Seizure Activity in EEG by an Artificial Neural Network., Computers and Biomedical Research, 1996, 29:
Rumelhart, D. Parallel Distributed Processing, 1986: The MIT Press.
Eberhart, R.C., Dobbins, R.W. Neural Network PC Tools, 1990: Academic Press, Inc.