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Introduction and motivation Comparitive investigation:

Predictability of epileptic seizures - Content -. Introduction and motivation Comparitive investigation: Predictive performance of measures of synchronization Statistical validation of seizure predictions: The method of measure profile surrogates Summary and outlook.

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Introduction and motivation Comparitive investigation:

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  1. Predictability of epileptic seizures - Content - • Introduction and motivation • Comparitive investigation: Predictive performance of measures of synchronization • Statistical validation of seizure predictions: The method of measure profile surrogates • Summary and outlook

  2. Predictability of epileptic seizures - Introduction: Epilepsy - • ~ 1 % of world population suffers from epilepsy • ~ 22 % cannot be treated sufficiently • ~ 70 % can be treated with antiepileptic drugs • ~ 8 % might profit from epilepsy surgery • Exact localization of seizure generating area • Delineation from functionally relevant areas • Aim: Tailored resection ofepileptic focus

  3. Intracranially implanted electrodes

  4. L R EEG containing onset of a seizure (preictal and ictal)

  5. L R EEG in the seizure-free period (interictal)

  6. Predictability of epileptic seizures - Motivation I - Open questions: • Does a preictal state exist? • Do characterizing measures allow a reliable detection of this state? Goals / Perspectives: • Increasing the patient‘s quality of life • Therapy on demand (Medication, Prevention) • Understanding seizure generating processes

  7. Predictability of epileptic seizures - Motivation II - State of the art: • Reports on the existence of a preictal state, mainly based on univariate measures • Gradual shift towards the application of bivariate measures • Little experience with continuous multi-day recordings • No comparison of different characterizing measures • Mostly no statistical validation of results

  8. Predictability of epileptic seizures - Motivation III - Why bivariate measures? • Synchronization phenomena key feature for establishing the communication between different regions of the brain • Epileptic seizure: Abnormal synchronization of neuronal ensembles • First promising results on short datasets: “Drop ofsynchronization” before epileptic seizures * * Mormann, Kreuz, Andrzejak et al., Epilepsy Research, 2003; Mormann, Andrzejak, Kreuz et al., Phys. Rev. E, 2003

  9. Predictability of epileptic seizures - Procedure - Continuous EEG – multichannel recordings Calculation of a characterizing measure Investigation of suitability for prediction by means of a seizure prediction statistics - Sensitivity Performance - Specificity Estimation of statistical significance

  10. Chan. 1 Chan. 2 Predictability of epileptic seizures - Moving window analysis - Window

  11. Chan. 1 Chan. 2 Predictability of epileptic seizures - Moving window analysis - Window

  12. Chan. 1 Chan. 2 Predictability of epileptic seizures - Moving window analysis - Window

  13. Chan. 1 Chan. 2 Predictability of epileptic seizures - Moving window analysis - … Window

  14. Reliable seperation preictal interictal impossible ! Predictability of epileptic seizures - Example: Drop of synchronization as a predictor - Time [Days] For this channel combination: sensitive not sensitive not specific specific

  15. Clearly improved seperation preictal interictal Significant ? Seizure times surrogates Predictability of epileptic seizures - Example: Drop of synchronization as a predictor - Selection of best channel combination : Time [Days]

  16. Predictability of epileptic seizures - Content - • Introduction and motivation • Comparitive investigation: Predictive performance of measures of synchronization • Statistical validation of seizure predictions: The method of measure profile surrogates • Summary and outlook

  17. Predictability of epileptic seizures - Procedure - Continuous EEG – multichannel recordings Calculation of a characterizing measure Investigation of suitability for prediction by means of a seizure prediction statistics - Sensitivity Performance - Specificity Estimation of statistical significance

  18. I. Database Seizures Time [h]

  19. Predictability of epileptic seizures - Procedure - Continuous EEG – multichannel recordings Calculation of a characterizing measure Investigation of suitability for prediction by means of a seizure prediction statistics - Sensitivity Performance - Specificity Estimation of statistical significance

  20. II. Bivariate measures - Overview - Synchronization Directionality • Cross Correlation Cmax • Mutual Information I • Indices of phase synchronization • based on • and using • Nonlinear interdependencies SsandHs • Event synchronization Q • - Shannon entropy (se) • - Conditional probabilty (cp) • Circular variance (cv) - Hilbert phase (H) - Wavelet phase (W) • Nonlinear interdependencies SaandHa • Delay asymmetry q

  21. Cmax I Cmax I Cmax I 1.0 1.0 1.0 0.5 0.5 0.5 0.0 0.0 0.0 II. Bivariate measures - Cross correlation and mutual information - * * * * * *

  22. II. Bivariate measures - Phase synchronization -

  23. II. Bivariate measures - Nonlinear interdependencies - No coupling: X

  24. II. Bivariate measures - Nonlinear interdependencies - Strong coupling:

  25. II. Bivariate measures - Event synchronization and Delay asymmetry I - Chan. 1 Chan. 2 Time [s]

  26. Predictability of epileptic seizures - Procedure - Continuous EEG – multichannel recordings Calculation of a characterizing measure Investigation of suitability for prediction by means of a seizure prediction statistics - Sensitivity Performance - Specificity Estimation of statistical significance

  27. III. Seizure prediction statistics - Steps of analysis - • Measure profiles of all neighboring channel combinations • Statistical approach: • Comparison of preictal and interictal • amplitude distributions • Measure of discrimination: Area below the • Receiver-Operating-Characteristics (ROC) - Curve Mormann, Kreuz, Rieke et al., Clin Neurophysiol 2005

  28. III. Seizure prediction statistics: ROC Sensitivity 1 - Specificity

  29. III. Seizure prediction statistics: ROC Sensitivity 1 - Specificity

  30. III. Seizure prediction statistics: ROC Sensitivity 1 - Specificity

  31. III. Seizure prediction statistics: ROC Sensitivity 1 - Specificity

  32. III. Seizure prediction statistics: ROC Sensitivity 1 - Specificity

  33. III. Seizure prediction statistics: ROC Sensitivity 1 - Specificity

  34. III. Seizure prediction statistics: ROC Sensitivity 1 - Specificity

  35. III. Seizure prediction statistics: ROC Sensitivity 1 - Specificity

  36. III. Seizure prediction statistics: ROC Sensitivity 1 - Specificity

  37. III. Seizure prediction statistics: ROC Sensitivity ROC-Area 1 - Specificity

  38. III. Seizure prediction statistics: ROC Sensitivity ROC-Area Sensitivity ROC-Area Sensitivity ROC-Area Sensitivity ROC-Area 1 - Specificity

  39. III. Seizure prediction statistics: Example Time [days] e Sensitivity ROC-Area 1 - Specificity

  40. III. Seizure prediction statistics - Parameter of analysis - • Smoothing of measure profiles (s = 0; 5 min) • Length of the preictal interval (d = 5; 30; 120; 240 min) • ROC hypothesis H • - Preictal drop (ROC-Area > 0, ) • - Preictal peak (ROC-Area < 0, ) For each channel combination 2 * 4 * 2 = 16 combinations Optimization criterion for each measure:Best mean over patients Mormann, Kreuz, Rieke et al., Clin Neurophysiol 2005

  41. Predictability of epileptic seizures - Procedure - Continuous EEG – multichannel recordings Calculation of a characterizing measure Investigation of suitability for prediction by means of a seizure prediction statistics - Sensitivity Performance - Specificity Estimation of statistical significance

  42. IV. Statistical Validation - Problem: Over-optimization - Given performance: Significant or statistical fluctuation? Good measure: „Correspondence“ seizure times -measure profile To test against null hypothesis: Correspondence has to be destroyed Randomization of seizure times Randomization of measure profiles I. Seizure times surrogates II. Measure profile surrogates

  43. IV. Statistical Validation - Seizure times surrogates - • Random permutation of the time intervals between actual seizures: Seizure times surrogates • Calculation of the seizure prediction statistics for the original as well as for 19 surrogate seizure times ( p=0.05) Andrzejak, Mormann, Kreuz et al., Phys Rev E, 2003

  44. - Results: Measure profiles of phase synchronization - Channel combination Time [days]

  45. Results - Evaluation schemes - • Discrimination of amplitude distributions Interictal Preictal • Global effect: • All Interictal All Preictal (1) • Local effect: • Interictal per channel comb Preictcal per channel comb (#comb) Mormann, Kreuz, Rieke et al., Clin Neurophysiol 2005

  46. - First evaluation scheme - Channel combination Time [days]

  47. Results: First evaluation scheme | ROC-Area | Measures

  48. Results - Evaluation schemes - • Discrimination of amplitude distributions Interictal Preictal • Global effect: • All Interictal All Preictal (1) • Local effect: • Interictal per channel comb Preictcal per channel comb (#comb) Mormann, Kreuz, Rieke et al., Clin Neurophysiol 2005

  49. - Second evaluation scheme - Channel combination Time [days]

  50. - Second evaluation scheme - Channel combination Time [days]

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