Epileptic Seizure
Detection System
Team Members
 Valerie Kuzmick, Biomedical Engineering
 John Lafferty, Computer Engineering
 April Serfass, Biomedical Engineering
 Doug Szperka, Computer Engineering
 Benjamin Zale, Computer Engineering
Advisors
 Prawat Nagvajara, PhD, Computer Engineering
 Karen Moxon, PhD, Biomedical Engineering
 Jeremy Johnson, PhD, MCS/ECE
Problem: Epilepsy

Chronic Brain Function Disorder

Characterized by Seizures

Over two million suffering from epilepsy


1% of US population
Current Treatments NOT Effective for
20% (400,000 patients) of Epileptics
VISION:
Complete System
Data
Acquisition
System
Seizure
Detection
Unit
Stimulation
Device
Design Challenge
Data
Acquisition
System
Seizure
Detection
Unit
Stimulation
Device
Prevention of Seizures

NCP Brain ‘Pacemaker’
– Intermittent electrical pulses 24 hours a day
– Implanted under the collarbone
– Delivers electrical signals to the brain via
vagus nerve in the neck
– When patient senses seizure coming, he or
she can activate the stimulator manually
Developed Solution

Prototype
– Microprocessor-based device that
detects the neural activity associated
with an epileptic seizure

Results
– Seizure Detection: 100% Accuracy
– Low False Positive Rate
Solutions for Seizure
Detection

Analysis of EEG
Data With ANN
– Advantages

Noninvasive
– Disadvantages



Signal detection far
from epicenter of
seizure
Loss of signal fidelity
through bone & scalp
65% detection rate

Analysis of Multiple
Single-Neuron Data
– Disadvantages

Invasive
– Advantages



Signal detection at the
epicenter of seizure
Ideal signal fidelity via
direct recording from
neurons
Preliminary data suggest
100% detection rate
Method of Solution
Data Collection & Analysis
 Algorithm Development
 Software Simulation
 Detection Unit Implementation

Data Collection

Certified laboratory rat handlers
– IACUC approved protocol

Electrodes surgically implanted
– Temporal lobes

PTZ administration
– Seizures induced
Data Collection
EIGHT-ARRAY ELECTRODE
RECORDING
DEVICE
TEMPORAL LOBE
Multiple Single Neurons
Analysis

Videotape
– Seizure/No Seizure

NEX (NeuroExplorer)
– Rate Histograms
– Bin Size/Smooth Data

Excel
– Imported NEX Files
– Seizures Distinguished
– Consolidation for Algorithm Development
Analysis
Algorithm Development

Research from EEG Seizure Detectors
– Artificial Neural Network (ANN)
– Signal Processing Techniques

Artificial Neural Network
– MATLAB Toolkit
– Created Various Feedforward Neural
Networks

Highest detection rate was 60%
Cross Correlation Solution
Neural activity becomes synchronized
during a seizure
 Cross correlate data over a window of
time

– Shows synchronization of neural action
potentials
Graphed the sum of pair-wise cross
correlation
 Shape of the cross-correlation is
determining factor

Data Conversion
Data Conversion
Cross Correlation Solution
Standard Deviation

Statistic that tells you how tightly
all the various data points are
clustered around the mean
– Small standard deviation

Data points are pretty tightly bunched together
– Large standard deviation

Data points are spread apart
Cross Correlation Solution
Non Seizure Data
Seizure Data
Threshold Value

Experimentally determined dividing line
between seizure and non-seizure

Algorithm Summary
– Data streamed into bins of finite length
– Cross Correlate
– Determine 1st standard deviation of cross
correlated data
– Smaller than threshold value = SEIZURE
Simulation

Used MATLAB to Simulate
– Used Saved Data as Inputs
– Allowed Varying of Algorithm Parameters
– Saved Results of Each Run to File

Final Parameters from Results
– Bin Size
– Bins per Window Size
– Threshold Value
Simulation Results
50ms Bin Size and 128 Bins per Window
 Promising Results

– Threshold Value was the Same
– Detected 100% of Observed Seizures
– Low False Positive Rate of 0.3% ~ 4.3
min/day
– Detected Seizures 4.5s Early on Average


Some as early as 17s
Few detected late – 2.5s was the latest
Simulation Results
Seizure vs Baseline Histogram
Rat #2
Basline Data
Seizure Data
Baseline Cumulative %
Number of Occurrances
Seizure Cumulative %
2500
120.00%
100.00%
2000
80.00%
1500
60.00%
1000
40.00%
500
20.00%
0
.00%
9975
9450
8925
8400
7875
7350
6825
6300
5775
5250
4725
4200
3675
3150
2625
2100
1575
1050
525
0
Standard Deviation
9 Channels - 50ms Bins - 128 Bins/Window
Detection Unit Implementation

Implement algorithm to execute
on dedicated microprocessor
– Speed
– Prototyping

QED RM5231 RISC Processor
– MIPS Instruction Set
– V3 Hurricane Evaluation Board
Hardware

Hurricane Evaluation Board
– Inserted into PCI slot of Windows-
based computer
– Communication Protocols
 PCI
 Serial
Embedded Software

ANSI C for portability

Compiled into Motorola S-Record
format

Downloaded to board via serial
port
Dataflow Diagram
Action
Potential
Data
NEX
Excel
RatStat
(Hardware
Simulation)
Data
Concatenator
SerialComm
Hurricane
Evaluation
Board
(Prototype)
Simulation
Output
Prototype
Output
Host PC Software

Automates Data Transmission
– Sums data into bins
– Generates S-Records of data
– Transmits data to evaluation board via serial
port connection
– Tells evaluation board to execute embedded
software
– Captures and reports seizure notification
from evaluation board
Host PC Software
Economic Analysis

Prototype Development
– Approximately $141,500 in equipment

Future Commercial Development
– Needs to be System-on-a-Chip Solution
– Data Acquisition System:
– Seizure Detection Unit:
– NCP Brain Pacemaker:
$ 8,000
$ 1,000
$11,000
– Entire System: $20,000 or less to be
marketable and profitable
Results
Cross Correlation Cross Correlation
Window
Window
(seconds)
(bins)
Average
Execution Time
(milliseconds)
32
1.6
13.2
64
3.2
50.3
128
6.4
182
256
12.8
718
Prototype
does not operate in real time when data is streamed
Conclusions

Collected and Evaluated Approximately 1
Hour of Data from Three Specimens
– Only 45 minutes (2 Rats / 3 Trials) usable
– Remaining data corrupted

100% Seizure Detection Rate

0.3% False Positive Rate

Seizures Predicted on an Average of 4.5
Seconds Beforehand
Automatic Seizure
Detection System
Team Members





Valerie Kuzmick, Biomedical Engineering
John Lafferty, Computer Engineering
April Serfass, Biomedical Engineering
Doug Szperka, Computer Engineering
Benjamin Zale, Computer Engineering
Epileptic Episodes
PreSeizure
Seizure
Epileptic Episode
Encompasses Pre-Seizure and
Seizure
 Highly correlated neural action
potential data

Neural Action Potentials
Phase Angle Mapping
Results Indicate Seizure Detection Rate Greater
than 90%
Magnitude (dB)
Frequency Content
Frequency (Hz)
Frequency Content
Phase Angle
Seizure Signature
Action Potentials
Weighted Sum of
Pattern Recognition
Time (seconds)
Prototype
Data
Acquisition
System
Seizure
Detection
Unit



Stimulation
Device
Receives Binary Data
Processes Data Using
Custom Algorithm
Detects and Outputs
Results