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Feedback Stimulation to Stop Seizure Activity

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  • Ibrahim
  • Katy
  • Katy
  • Katy
  • Shikha
  • Shikha
  • Shikha
  • Shikha
  • Ibrahim
  • Ibrahim
  • Ibrahim
  • Ibrahim
  • Ibrahim
  • Ibrahim
  • Ibrahim
  • Steve
  • Steve
  • Steve
  • Ibrahim
  • Ibrahim
  • Ibrahim
  • Katy
  • Katy
  • Steve
  • Steve
  • Transcript

    • 1. Feedback Stimulation toFeedback Stimulation to Stop Seizure ActivityStop Seizure Activity Team MembersTeam Members Ibrahim Khansa Shikha Katy Reed Steven Skroch AdvisorAdvisor Dr. Willis Tompkins ClientClient Dr. Paul Rutecki - Neurology
    • 2. Background: EpilepsyBackground: Epilepsy A recurring neurological disorder characterized by random firing of nerve cells in the brain which cause a temporary shutdown of normal brain function •Symptoms: •Small jerks, temporary loss of awareness, violent grand mal events •Can occur in any part of the brain http://www.ncbi.nlm.nih.gov/disease/Epilepsy.html
    • 3. Epilepsy TreatmentEpilepsy Treatment •Current treatment: Anti-seizure/Anticonvulsant medications •Not effective for 30% of patients •Disruptive Side effects •Early electrical stimulation of the brain may abort seizures
    • 4. Client’s ResearchClient’s Research •Study the possibility of aborting spontaneous seizures in slices of rat hippocampus, by electrical stimulation •Problems •Slices are stimulated at fixed time intervals, NOT in response to seizures •Solution: •Automate stimulation •Monitor neuron activity before onset in order to predict seizure  Stimulate the slices in response to the seizure http://www.brainconnection.com/topics/?main=gal/hippocampus-2 Location of the hippocampus in the human brain
    • 5. Problem StatementProblem Statement A reliable epileptic seizure prediction/detection algorithm is needed. When a seizure is predicted or detected, the algorithm needs to generate an electrical stimulus, analogous to a cardiac defibrillation current.
    • 6. Device AbilitiesDevice Abilities •Detect signs of an imminent seizure, or alternatively, detect the seizure in progress. •Deliver an adequate stimulus as soon as seizure onset is detected •Feedback: Monitor the effects of the stimulus, and stimulate again if needed •Compatibility with existing hardware and software to be interfaced with hippocampus slices in a Petri dish.
    • 7. Design OverviewDesign Overview •Four major components of the feedback stimulation algorithm •Seizure prediction •Seizure detection •Stimulation •Feedback loop and training set •Three major design alternatives
    • 8. Design OverviewDesign Overview •Four major components of the feedback stimulation algorithm •Seizure prediction •Seizure detection •Stimulation •Feedback loop and training set •Three major design alternatives
    • 9. Prediction ModalitiesPrediction Modalities •Possibility of seizure prediction still in research phase •May be possible to detect changes up to 10 minutes before seizure onset •No definite changes in EEG frequency or amplitude
    • 10. Prediction ModalitiesPrediction Modalities •Navarro et al: Analysis of Similarity method Drop in the index of similarity just before the seizure
    • 11. Design OverviewDesign Overview •Four major components of the feedback stimulation algorithm •Seizure prediction •Seizure detection •Stimulation •Feedback loop and training set •Three major design alternatives
    • 12. Detection ModalitiesDetection Modalities •Changes in EEG signal at seizure onset: •Amplitude Increase •Slight •May give a lot of false positives •Frequency Increase •Line length Increase •Encompasses both amplitude and frequency increase
    • 13. Data AcquiredData Acquired •Seizures induced in slices of rat hippocampus •Data acquired using a glass electrode and a LabVIEW detection module •Real-time frequency spectrum computed
    • 14. EEG Frequency Spectrum Normal (Interictal) Just before a seizure (Preictal) Seizure (Ictal)
    • 15. Design OverviewDesign Overview •Four major components of the feedback stimulation algorithm •Seizure prediction •Seizure detection •Stimulation •Feedback loop and training set •Three major design alternatives
    • 16. Brain StimulationBrain Stimulation • A “reset” mechanism • All neurons in a region stimulated at once  All neurons in refractory period  No further random firing possible No further firing possible during the refractory period
    • 17. StimulationStimulation •Square pulses: •Frequency 100-150 Hz •Pulse duration 20-100 μs Source: Responsive Cortical Neurostimulation (Axon) •Stimulation has to be administered early (before the seizure, or just after onset)
    • 18. Design OverviewDesign Overview •Four major components of the feedback stimulation algorithm •Seizure prediction •Seizure detection •Stimulation •Feedback loop and training set •Three major design alternatives
    • 19. Feedback LoopFeedback Loop Predict/Detect Seizure Give 4-6 pulses Wait 1-2 seconds Seizure stopped? No Yes Done Export data to training set
    • 20. Receiver OperatingReceiver Operating CharacteristicCharacteristic ROC 1-Specificity (Rate of False Positives) Sensitivity(RateofTruePositives) Before training set After training set
    • 21. Design OverviewDesign Overview •Four major components of the feedback stimulation algorithm •Seizure prediction •Seizure detection •Stimulation •Feedback loop and training set •Three major design alternatives
    • 22. Design Alternative 1Design Alternative 1 Detection electrode Digidata 1322A (Client’s existing DAQ) Acquire and analyze data with C++Stimulation electrode Seizure detection triggers signal generator •Advantages •Inexpensive •Fast, allows low-level control •Limitations •May be cumbersome
    • 23. Design Alternative 2Design Alternative 2 Detection electrode Digidata 1322A (Client’s existing DAQ) Input data into MATLAB in real- time and analyze If seizure detected, send a trigger Stimulation electrode Signal generator outputs square pulses 555 timer •Advantages •Inexpensive •Matlab allows extensive signal analysis •Limitations •Digidata cannot be interfaced directly with Matlab
    • 24. Design Alternative 3Design Alternative 3 •Acquire and stimulate using LabVIEW •Client need not purchase LabVIEW •Advantages: •Simple, versatile and user-friendly •Can easily build learning set when seizure not detected •Limitations: •Cannot use clients’s DAQ
    • 25. Future WorkFuture Work •Build a complete feedback loop •Implement the Analysis of Similarity prediction algorithm •Choose the optimal DAQ •Test the completed algorithm on live hippocampus slices
    • 26. ReferencesReferences Grill W. (2001). Extracellular excitation of central neurons: implications for the mechanisms of deep brain stimulation. Thalamus and Related Systems, (1), pp.269-77. Navarro V. (2002). Seizure anticipation in human neocortical partial epilepsy. Brain, (125), pp.640-55. Jerger K. (2001). Early seizure detection. Journal of Clinical Neurophysiology, 18(3), pp.259-68. Le Van Quyen M. (2001). Anticipation of epileptic seizures from standard EEG recordings. Lancet, (357), pp.183-88. Staley K. (2004). Mechanisms of fast ripples in the hippocampus. The Journal of Neuroscience, 24(40), pp.8896-8906. http://www.epilepsynse.org.uk/pages/info/leaflets/drug.cfm#co ntraception
    • 27. Questions?Questions?