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Two Algorithms for Real-Time Seizure Prediction and Detection, for an Implanted, Closed-Loop, Epilepsy Prosthesis In Viv

Two Algorithms for Real-Time Seizure Prediction and Detection, for an Implanted, Closed-Loop, Epilepsy Prosthesis In Vivo. P. Rajdev 1 , S. Raghunathan 2 , P. Irazoqui 3 1, 2 Graduate Student, Purdue University, 3 Asst. Professor, Purdue University . Outline. Motivation Experimental Setup

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Two Algorithms for Real-Time Seizure Prediction and Detection, for an Implanted, Closed-Loop, Epilepsy Prosthesis In Viv

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  1. Two Algorithms for Real-Time Seizure Prediction and Detection, for an Implanted, Closed-Loop, Epilepsy Prosthesis In Vivo P. Rajdev1, S. Raghunathan2, P. Irazoqui3 1, 2 Graduate Student, Purdue University, 3 Asst. Professor, Purdue University

  2. Outline • Motivation • Experimental Setup • Prediction Algorithm • Algorithm • Digital Signal Processor • Thresholds • Results • Hardware Constraints in Implantable Applications • Event Based Seizure Detection • Algorithm • Hardware Implementation • Results • Applications and Future Directions

  3. Motivation • Real time Implementation • Algorithm with low computational complexity. • Implementable on Digital Signal Processor or Application Specific Integrated Circuit. • Local Field Potential (LFP) Vs Electroencephalogram (EEG) • Disadvantages of EEG • Disadvantages of LFPs

  4. Experimental Setup • Animal Model of epilepsy: Kainate model • Commonly used model for temporal lobe epilepsy. • Primary site of action of kainic acid is the CA3 cells of the hippocampus. • Pathological, clinical, and electrographic characteristics of the seizures caused by kainic acid treatment strongly resemble those seen in human temporal lobe epilepsy

  5. Digital Signal Processor • The prediction algorithm was first developed in Matlab. • A real-time implementation was then realized on a floating point TMS320C6713T digital signal processor (DSP). • DSP chip operating at 225MHz, along with 16Mbytes of SDRAM, 415Kbytes of Flash memory and a JTAG emulator.

  6. Weiner Prediction Based Algorithm • Four-step process • signal enhancement, • adaptive auto-regressive modeling and prediction, • envelope detection, • and a binomial decision rule.

  7. Algorithm (contd…) • Implemented a real-time wiener-prediction based algorithm on a digital signal processor. • Quasi-stationary signal • Adaptive nature of the algorithm ensures that the prediction coefficients provide effective prediction of baseline activity. • Lower Computational complexity • In an autoregressive (AR) model, the future value is modeled as a linear combination of the p past values of the signal.

  8. Algorithm (contd…) 1400 Mean =124.36 1200 1000 Mean = 22.45 40 sec prior to seizure onset 800 600 400 Mean = 4.93 200 0 Ictal Baseline Pre-ictal

  9. Thresholds 100 2 Sensitivity False positive rate (FPR) Latency Sensitivity False positives (/hr) 50 1 Latency (sec) 0 0 1 1.2 1.4 1.6 1.8 2 Lambda (λ) Lambda (λ)

  10. Results

  11. Results

  12. Power consumed / Battery life • Total area /size • Programmability/Communication link to an external monitor • Integration capabilities Hardware constraints in an implantable application Goals for feedback algorithm: • Simplicity in implementation • Good sensitivity • Adaptability, allowing for patient to patient variations • Integration capabilities

  13. Temporal evolution and spread • Radial spread at speeds up to 60cm/sec (Jung , 2003) • 2-70 seconds from hippocampal focus to neo-cortex (Spencer ,1987) • Animal studies indicate a delay of ~ 20 s before spreading away from the temporal lobe focus (Litt, 2003)

  14. Event based seizure detection • Amplitude of recorded signal (Kamp ) • Measure of frequency content obtained from inter-event interval (IEIth ) • Measure of rhythmicity obtained from sustained levels of increasing amplitude, high frequency content in recorded signal (NStage)

  15. Distributing the event threshold (Kamp) Distributing the IEI threshold (IEIth)

  16. Hardware implementation

  17. Hardware implementation- circuit timing results

  18. Where do the hardware trade-offs figure?

  19. Optimizing threshold selection

  20. Results

  21. *references available on request

  22. ASIC Design/ Where are we going? Microchip Reid Harrison Lab, Univ. of Utah Center for Wireless Integrated Microsystems, Univ. of Michigan

  23. Applications of device Integration with multi-channel neural recording devices Integration with implantable neural stimulators Seizure focus identification and tracking

  24. Acknowledgments Research partners Robert Worth, M.D., Ph.D. Thomas Sutula, Ph.D. Jenna Rickus, Ph.D. Edward Bartlett, Ph.D. Kaushik Roy, Ph.D. Funding Cyberonics, Inc. Wallace H. Coulter Early Career Award Additional industry collaborators Texas Instruments • BCI Lab members • Professor: Pedro P. Irazoqui • Research Associate: Casey Ellison • Post Doctorate: Kate Musick • PhD Students • Travis Hassell, BME • Eric Chow, ECE • Pooja Rajdev, BME • Shriram Raghunathan, BME • Matt Ward, BME • Brooke Beier, BME • Art Chlebowski, BME • Bhupendra Manola, BME • Masters Students • Matthew Graves, BME • Adam Kahn, BME • Gabriel Albors, BME https://engineering.purdue.edu/BCILab

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