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Epileptic Seizure Detection System

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

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Epileptic Seizure Detection System

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  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 AcquisitionSystem Seizure Detection Unit Stimulation Device

  4. Design Challenge Data AcquisitionSystem 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 anepileptic seizure • Results • Seizure Detection: 100% Accuracy • Low False Positive Rate

  7. Solutions for Seizure Detection • 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 • 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

  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 EIGHT-ARRAY ELECTRODE RECORDING DEVICE 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

  17. 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 Data Seizure 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 1st 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 NEX Excel RatStat (Hardware Simulation) Data Concatenator SerialComm Hurricane Evaluation Board (Prototype) Simulation Output 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 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. Pre- Seizure Seizure Epileptic Episode Epileptic Episodes • Encompasses Pre-Seizure and Seizure • Highly correlated neural action potential data

  36. Neural Action Potentials

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

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

  39. Frequency Content

  40. Phase Angle

  41. Seizure Signature

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

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

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