Seizure Prediction System:Seizure Prediction System:An Artificial Neural Network An Artificial Neural Network

ApproachApproach

David Gilpin

Chris Moore

Advised by: Pradeep Modur, MD

The ProblemThe 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

Facts on SeizuresFacts 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 OverviewProject 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 / ResearchBackground / Research

The use of an Artificial Neural Network in seizure detection

Project GoalProject 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 / WishesProject Demands / Wishes

Demands– Successfully detect spikes for prediction of

seizures

Wishes– Detect severity of seizure– Become a fully automated system (implantable)

Project TimelineProject 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

MaterialsMaterials

Persyst®

– Data AcquisitionMicrosoft Excel®

– Data FormattingMatlab® Signal Processing Toolkit

– Extraction of Data ParametersMatlab® Neural Network Toolkit

– Design of Artificial Neural Network

Data Acquisition and Data Acquisition and FormattingFormatting

EEG data taken from different VUMC patients over 24 hour periods

Data exported from Persyst® into a text fileData converted into M-file for use with

Matlab– Data collected @ 200 Hz in 2 second epochs

Signal ProcessingSignal Processing

Extraction of Five Parameters:– Rising Time– Falling Time– Duration of Spike– Max Peak-To-Peak – Peak Frequency (FFT)

Standard 20 EEG signal1 channel EKG signal

Algorithm DesignAlgorithm 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

Neural NetworkNeural 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 .5-.65 If above threshold, spike; if below, no spike

Fine Tuning 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

Final Product (Ideal)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

Channel SynchronizationChannel 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

Patent SearchPatent 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: 400982

Persyst® has developed detection system; is not neural network based

Market Cost AnalysisMarket 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

Cost vs. BenefitsCost 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.

Current StatusCurrent Status

Signal Processing– Redesigning “Context Calculator”– Normalizing Data

Neural Network– Formatting Inputs for implementation– Making sure weights are assigned properly

Future WorkFuture 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

Main ReferencesMain 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.