1 / 80

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.

dinah
Download Presentation

Introduction and motivation Comparitive investigation:

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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]

More Related