Epileptic Seizure Epileptic Seizure Detection SystemDetection 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: EpilepsyProblem: 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:VISION:Complete SystemComplete System

Data AcquisitionSystem

Seizure Detection Unit

Stimulation Device

Design ChallengeDesign Challenge

Data AcquisitionSystem

Seizure Detection Unit

Stimulation Device

Prevention of SeizuresPrevention of SeizuresNCP 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 SolutionDeveloped 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 Solutions for Seizure DetectionDetection

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 SolutionMethod of Solution

Data Collection & AnalysisAlgorithm DevelopmentSoftware SimulationDetection Unit Implementation

Data CollectionData Collection

Certified laboratory rat handlers– IACUC approved protocol

Electrodes surgically implanted– Temporal lobes

PTZ administration– Seizures induced

Data CollectionData Collection

RECORDINGDEVICE

EIGHT-ARRAY ELECTRODE

TEMPORAL LOBE

Multiple Single NeuronsMultiple Single Neurons

AnalysisAnalysis

Videotape– Seizure/No Seizure

NEX (NeuroExplorer)– Rate Histograms– Bin Size/Smooth Data

Excel– Imported NEX Files– Seizures Distinguished– Consolidation for Algorithm Development

AnalysisAnalysis

Algorithm DevelopmentAlgorithm 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 SolutionCross 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 ConversionData Conversion

Data ConversionData Conversion

Cross Correlation SolutionCross Correlation Solution

Standard DeviationStandard 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 SolutionCross Correlation Solution

Non Seizure Data Seizure Data

Threshold ValueThreshold 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

SimulationSimulation

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 ResultsSimulation 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 ResultsSimulation ResultsSeizure vs Baseline Histogram

Rat #2

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500

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1050

1575

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3150

3675

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9975

Standard Deviation9 Channels - 50ms Bins - 128 Bins/Window

Nu

mb

er

of

Oc

cu

rra

nc

es

.00%

20.00%

40.00%

60.00%

80.00%

100.00%

120.00%

Basline Data

Seizure Data

Baseline Cumulative %

Seizure Cumulative %

Detection Unit ImplementationDetection Unit Implementation

Implement algorithm to execute on dedicated microprocessor– Speed– Prototyping

QED RM5231 RISC Processor– MIPS Instruction Set– V3 Hurricane Evaluation Board

HardwareHardwareHurricane Evaluation Board

– Inserted into PCI slot of Windows-based computer

– Communication Protocols PCI Serial

Embedded SoftwareEmbedded Software

ANSI C for portability

Compiled into Motorola S-Record format

Downloaded to board via serial port

Dataflow DiagramDataflow Diagram

ActionPotential

DataNEX Excel

DataConcatenator

RatStat(HardwareSimulation)

SimulationOutput

SerialComm

HurricaneEvaluation

Board(Prototype)

 

PrototypeOutput

Host PC SoftwareHost 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 SoftwareHost PC Software

Economic AnalysisEconomic Analysis

Prototype Development– Approximately $141,500 in equipment

Future Commercial Development– Needs to be System-on-a-Chip Solution

– Data Acquisition System: $ 8,000– Seizure Detection Unit: $ 1,000– NCP Brain Pacemaker: $11,000

– Entire System: $20,000 or less to be marketable and profitable

Results Results Cross Correlation

Window (bins)

Cross Correlation Window

(seconds)

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

ConclusionsConclusions

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 Automatic Seizure Detection SystemDetection System

Team Members

Valerie Kuzmick, Biomedical Engineering John Lafferty, Computer Engineering April Serfass, Biomedical Engineering Doug Szperka, Computer Engineering Benjamin Zale, Computer Engineering

Epileptic EpisodesEpileptic Episodes

Encompasses Pre-Seizure and Seizure

Highly correlated neural action potential data

Epileptic Episode

Seizure Pre-

Seizure

Neural Action PotentialsNeural Action Potentials

Phase Angle MappingPhase Angle Mapping

Results Indicate Seizure Detection Rate Greater than 90%

Frequency ContentFrequency Content

Frequency (Hz)

Mag

nit

ud

e

(dB

)

Frequency ContentFrequency Content

Phase AnglePhase Angle

Seizure SignatureSeizure Signature

Pattern RecognitionPattern Recognition

Time (seconds)

Weig

hte

d S

um

of

Act

ion

Pote

nti

als

PrototypePrototype

Data AcquisitionSystem

Seizure Detection Unit

Stimulation Device

Receives Binary Data

Processes Data Using Custom Algorithm

Detects and Outputs Results