Title: Epileptic Seizure Detection System
1Epileptic 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
2Problem 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
3VISIONComplete System
Data AcquisitionSystem
Seizure Detection Unit
Stimulation Device
4Design Challenge
Data AcquisitionSystem
Seizure Detection Unit
Stimulation Device
5Prevention 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
6Developed Solution
- Prototype
- Microprocessor-based device that detects the
neural activity associated with an epileptic
seizure - Results
- Seizure Detection 100 Accuracy
- Low False Positive Rate
7Solutions 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
8Method of Solution
- Data Collection Analysis
- Algorithm Development
- Software Simulation
- Detection Unit Implementation
9Data Collection
- Certified laboratory rat handlers
- IACUC approved protocol
- Electrodes surgically implanted
- Temporal lobes
- PTZ administration
- Seizures induced
10Data Collection
EIGHT-ARRAY ELECTRODE
RECORDING DEVICE
TEMPORAL LOBE
11Multiple Single Neurons
12Analysis
- Videotape
- Seizure/No Seizure
- NEX (NeuroExplorer)
- Rate Histograms
- Bin Size/Smooth Data
- Excel
- Imported NEX Files
- Seizures Distinguished
- Consolidation for Algorithm Development
13Analysis
14Algorithm 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
15Cross 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
16Data Conversion
17Data Conversion
18Cross Correlation Solution
19Standard 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
20Cross Correlation Solution
Non Seizure Data
Seizure Data
21Threshold 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
22Simulation
- 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
23Simulation 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
24Simulation Results
25Detection Unit Implementation
- Implement algorithm to execute on dedicated
microprocessor - Speed
- Prototyping
- QED RM5231 RISC Processor
- MIPS Instruction Set
- V3 Hurricane Evaluation Board
26Hardware
- Hurricane Evaluation Board
- Inserted into PCI slot of Windows-based computer
- Communication Protocols
- PCI
- Serial
27Embedded Software
- ANSI C for portability
- Compiled into Motorola S-Record format
- Downloaded to board via serial port
28Dataflow Diagram
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Action Potential Data
NEX
Excel
RatStat (Hardware Simulation)
Data Concatenator
SerialComm
Hurricane Evaluation Board (Prototype) Â
Simulation Output
Prototype Output
29Host 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
30Host PC Software
31Economic 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
32Results
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
33Conclusions
- 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
34Automatic 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
35Epileptic Episodes
- Encompasses Pre-Seizure and Seizure
- Highly correlated neural action potential data
36Neural Action Potentials
37Phase Angle Mapping
Results Indicate Seizure Detection Rate Greater
than 90
38Frequency Content
Magnitude (dB)
Frequency (Hz)
39Frequency Content
40Phase Angle
41Seizure Signature
42Pattern Recognition
Weighted Sum of Action Potentials
Time (seconds)
43Prototype
Data AcquisitionSystem
Seizure Detection Unit
Stimulation Device
- Receives Binary Data
- Processes Data Using Custom Algorithm
- Detects and Outputs Results