1. 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

2. 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

3. VISION: Complete System Data Acquisition System Seizure Detection Unit Stimulation Device

4. Design Challenge Data Acquisition System Seizure Detection Unit Stimulation Device

5. 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

6. 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

7. 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

8. Method of Solution Data Collection & Analysis Algorithm Development Software Simulation Detection Unit Implementation

9. Data Collection Certified laboratory rat handlers – IACUC approved protocol Electrodes surgically implanted – Temporal lobes PTZ administration – Seizures induced

10. Data Collection RECORDING DEVICE EIGHT-ARRAY ELECTRODE TEMPORAL LOBE

11. Multiple Single Neurons

12. Analysis Videotape – Seizure/No Seizure NEX (NeuroExplorer) – Rate Histograms – Bin Size/Smooth Data Excel – Imported NEX Files – Seizures Distinguished – Consolidation for Algorithm Development

13. Analysis

14. 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%

15. 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

16. Data Conversion

18. Cross Correlation Solution

19. 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

20. Cross Correlation Solution Non Seizure DataSeizure Data

21. Threshold Value Experimentally determined dividing line between seizure and non-seizure Algorithm Summary – Data streamed into bins of finite length – Cross Correlate – Determine 1 st standard deviation of cross correlated data – Smaller than threshold value = SEIZURE

22. 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

23. 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

24. Simulation Results

25. Detection Unit Implementation Implement algorithm to execute on dedicated microprocessor – Speed – Prototyping QED RM5231 RISC Processor – MIPS Instruction Set – V3 Hurricane Evaluation Board

26. Hardware Hurricane Evaluation Board – Inserted into PCI slot of Windows- based computer – Communication Protocols PCI Serial

27. Embedded Software ANSI C for portability Compiled into Motorola S-Record format Downloaded to board via serial port

28. Dataflow Diagram Action Potential Data NEXExcel Data Concatenator RatStat (Hardware Simulation) Simulation Output SerialComm Hurricane Evaluation Board (Prototype) Prototype Output

29. 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

30. Host PC Software

31. Economic 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

32. Results Cross Correlation Window (bins) Cross Correlation Window (seconds) Average Execution Time (milliseconds) 32 1.6 13.2 643.2 50.3 128 6.4182 25612.8 718 Prototype does not operate in real time when data is streamed

33. 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

34. 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

35. Epileptic Episodes Encompasses Pre-Seizure and Seizure Highly correlated neural action potential data Epileptic Episode Seizure Pre- Seizure

36. Neural Action Potentials

37. Phase Angle Mapping Results Indicate Seizure Detection Rate Greater than 90%

38. Frequency Content Frequency (Hz) Magnitude (dB)

39. Frequency Content

40. Phase Angle

41. Seizure Signature

42. Pattern Recognition Time (seconds) Weighted Sum of Action Potentials

43. Prototype Data Acquisition System Seizure Detection Unit Stimulation Device Receives Binary Data Processes Data Using Custom Algorithm Detects and Outputs Results