introduction and motivation comparitive investigation:

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Introduction and motivation

Comparitive investigation:

Predictive performance of measures of synchronization Statistical validation of seizure predictions: The method of measure profile surrogates

Summary and outlookPredictability of epileptic seizures- Content -1 ~ 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 of epileptic focusPredictability of epileptic seizures- Introduction: Epilepsy -2Inzidenz der Epilepsie vergleichbar mit Diabetes

exact localization of seizure generating area (epileptic focus)current gold standard: EEG recording of seizure origin

Epilepsy surgery: tailored resection of epileptic focusIntracranially implanted electrodes

3Tiefenelektroden sowie Elektroden auf der GehirnoberflcheInsbesondere Tiefenelektroden TL und TR (bei Beispielen)EEG containing onset of a seizure (preictal and ictal)

LR4Verschiedene Kanle (Spannung) gegen die Zeit, nicht Elektroden!!!Anfallsbeginn zeigen, zurckgehen zur Voranfallsphase (hier allerdings nur die letzten Sekunden zu sehen), allgemein in der Grenordnung von Minuten bis hin zu Stundencharakteristische Vernderungen schon vorher? bergang zum interiktualen

EEG in the seizure-free period (interictal)LRPredictability 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 patients quality of lifeTherapy on demand (Medication, Prevention)Understanding seizure generating processes6Zentrale Fragestellung dieser ArbeitExistiert ein priktualer Zustand?Wenn ja:Ist eine verlssliche Detektion dieses Zustandes mit Hilfe charakterisierender Mae mglich?

Aus Patientensicht Anflle unvorhergesehen, deutliche Einschrnkung der Lebensqualitt sowie grosse psychische Belastung(Angst vor Anfllen)Lieber tagelange Ruhe und Sicherheit und nur einige Minuten Angst vor dem Anfall statt dauernder Ungewissheit

Durch Implantation von Elektroden eine der seltenen Mglichkeiten gegeben zur Erforschung anfallsgenerierender Prozesse im menschlichen GehirnPredictability of epileptic seizures- Motivation II -State of the art:

Reports on the existence of a preictal state, mainly based on univariate measuresGradual 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 results7Langzeitdaten (Grenordnung einige Tage), bisher berwiegend Verwendung kurzer Datenstze (maximal einige Stunden)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 of synchronization before epileptic seizures ** Mormann, Kreuz, Andrzejak et al., Epilepsy Research, 2003; Mormann, Andrzejak, Kreuz et al., Phys. Rev. E, 2003Continuous 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 significancePredictability of epileptic seizures- Procedure -9

Predictability of epileptic seizures- Moving window analysis -WindowChan. 1Chan. 210Fensterlnge 20 sec.

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



sensitivenotsensitivenotspecificspecificFor this channel combination:Reliable seperation preictal interictal impossible !Predictability of epileptic seizures - Example: Drop of synchronization as a predictor -Time [Days]14

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



Summary and outlookPredictability of epileptic seizures- Content -16Der Titel des Vortrages spiegelt auch seine Zweiteiligkeit wieder. Zum einen

Im Rahmen dieser Arbeit neu entwickelte Methode der Maprofil-SurrogateContinuous EEG multichannel recordings




Estimation of statistical significancePredictability of epileptic seizures- Procedure -17

I. DatabaseSeizuresTime [h]18Continuous EEG multichannel recordings




Estimation of statistical significancePredictability of epileptic seizures- Procedure -19Cross Correlation Cmax

Mutual Information I

Indices of phase synchronizationbased on

and using

Nonlinear interdependencies Ss and Hs

Event synchronization Q

SynchronizationDirectionality Nonlinear interdependencies Sa and Ha

Delay asymmetry q- Shannon entropy (se)- Conditional probabilty (cp) Circular variance (cv)- Hilbert phase (H)- Wavelet phase (W)

II. Bivariate measures- Overview -

20

II. Bivariate measures- Cross correlation and mutual information -1.00.50.0CmaxI**1.00.50.0CmaxI**1.00.50.0CmaxI**21

II. Bivariate measures- Phase synchronization -22

II. Bivariate measures- Nonlinear interdependencies -No coupling:X23

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

II. Bivariate measures- Event synchronization and Delay asymmetry I -Time [s]Chan. 1Chan. 225Continuous EEG multichannel recordings




Estimation of statistical significancePredictability of epileptic seizures- Procedure -26III. 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 theReceiver-Operating-Characteristics (ROC) - Curve Mormann, Kreuz, Rieke et al., Clin Neurophysiol 200527

III. Seizure prediction statistics: ROCSensitivity1 - Specificity28









III. Seizure prediction statistics: ROCSensitivity1 - SpecificityROC-Area37

III. Seizure prediction statistics: ROCROC-AreaROC-AreaROC-AreaROC-Area1 - SpecificitySensitivitySensitivitySensitivitySensitivity38

III. Seizure prediction statistics: ExampleSensitivity1 - SpecificityROC-AreaTime [days]e39For each channel combination 2 * 4 * 2 = 16 combinationsIII. 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, )

Optimization criterion for each measure: Best mean over patients Mormann, Kreuz, Rieke et al., Clin Neurophysiol 2005Continuous EEG multichannel recordings




Estimation of statistical significancePredictability of epileptic seizures- Procedure -41IV. 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 I. Seizure times surrogatesII. Measure profile surrogatesRandomizationof measure profilesRandomizationof seizure times42IV. 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, 200343

- Results: Measure profiles of phase synchronization -Time [days]Channel combination44Nun zu den Ergebnissen:Hier fr ein Ma (Phasensynchronisation) und einen Patienten (A) beispielhaft alle Profile benachbarter KanalkombinationenGrosse Variabilitt ber Kombinationen, teilweise recht auffllige Antikorrelationen zwischen benachbarten KanalkombinationenDiscrimination of amplitude distributions Interictal Preictal

Global effect:All Interictal All Preictal (1)

Local effect:Interictal per channel comb Preictcal per channel comb (#comb)Results- Evaluation schemes - Mormann, Kreuz, Rieke et al., Clin Neurophysiol 200545Globaler Effekt: Vernderungen vor Anfllen im ganzen GehirnLokaler Effekt: passend zu den hier untersuchten fokalen Epilepsien

- First evaluation scheme -Time [days]Channel combination46Hier fr ein Ma (Phasensynchronisation) und einen Patienten (A) beispielhaft alle Profile benachbarter KanalkombinationenGrosse Variabilitt ber Kombinationen, teilweise recht auffllige Antikorrelationen zwischen benachbarten Kanalkombinationen

Results: First evaluation scheme | ROC-Area |Measures47Introduce measures, synchronisation + directionalityMean over patients, all non-significantWundern sie sich nicht, dass Sie kaum was sehen, denn hier gibt es nichts zu sehen. Alle ROC-Werte sind recht niedrig und zudem auch allesamt nicht-signifikantDiscrimination of amplitude distributions Interictal Preictal

Global effect:All Interictal All Preictal (1)

Local effect:Interictal per channel comb Preictcal per channel comb (#comb)Results- Evaluation schemes - Mormann, Kreuz, Rieke et al., Clin Neurophysiol 200548Globaler Effekt: Vernderungen vor Anfllen im ganzen GehirnLokaler Effekt: passend zu den hier untersuchten fokalen Epilepsien

- Second evaluation scheme -Time [days]Channel combination



Results: Preictal and interictal distributionse52Sehr grosse Variabilitt: einige fast identisch (z.B. TR09-TR10), einige deutlich verschieden (z.B. TR08-TR09)

Results: Second evaluation scheme| ROC-Area |Measures53Distribution over channel combinations: Maximum, median and minimum ROC-value for each patientPredictability of epileptic seizures - Summary I: Comparison of measures -General tendency regarding predictive performance:- Phase synchronization based on Hilbert Transform- Mutual Information, cross correlation- - Nonlinear interdependenciesMeasures of directionality among measures of synchronizationNo global effect, but significant local effects

Introduction and motivation



Summary and outlookPredictability of epileptic seizures- Content -* Kreuz, Andrzejak, Mormann et al., Phys. Rev. E (2004)55Der Titel des Vortrages spiegelt auch seine Zweiteiligkeit wieder. Zum einen

Im Rahmen dieser Arbeit neu entwickelte Methode der Maprofil-Surrogate Mostly not sufficient data for Out of sample study (Separation in training- and test sample)

In sample Optimization (Selection)(Best parameter, best measure, best channel, best patient, )

Statistical fluctuations difficult to estimateSeizure prediction- Problem : Statistical validation -56Continuous EEG multi channel recordings

Calculation of characterizing measures


Estimation of statistical significancePredictability of epileptic seizures- Procedure -- Patient A (18 channel combinations)- Phase synchronization und event synchronization Q- ROC, same optimization, for every channel combination- Method of measure profile surrogates

57Fr jede Kanalkombination, d.h. lokaler EffektIV. 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 I. Seizure times surrogatesII. Measure profile surrogatesRandomizationof measure profilesRandomizationof seizure times58

Measure profile surrogatesZeit [Tage]Time [days]Time [days]59Formulation of constraints in cost function E

Minimization among all permutations of the original measure profile

Iterative scheme: Exchange of randomly chosen pairsMeasure profile surrogates- Simulated Annealing I -Schreiber, Phys. Rev. Lett., 1998Cooling scheme (Temp. T0), abort at desired precisionProbability of acceptance:

60

Measure profile surrogates- Simulated Annealing II -18 channel combinations(Phase synchronization)Cost functionTemperatureIteration steps61Measure profile surrogates Simulated Annealing III - Properties to maintain:

Recording gaps are not permuted

Ictal and postictal intervals are not permuted

Amplitude distribution Permutation

Autocorrelation Cost function

62

Measure profile surrogates- Original autocorrelation functions (Phase sync.) -Time [days]63

Measure profile surrogates- Original autocorrelation functions (Phase sync.) -Time [days]64Dementsprechend wurde das Timelag tau gewhlt

Measure profile surrogatesTime [days]65

Measure profile surrogatesTime [days]66Measure profile surrogates- Two evaluation schemes -Each channel combination separately

Selection of best channel combination67

Results: Phase synchronization|ROC|68Fr jede Kanalkombination: ROC-Werte fr die 19 Surrogate sowie das Original (rot)

Zunchst nur Kanalkombinationen mit hchsten Werten markieren

Results: Event synchronization|ROC|69Zunchst nur Kanalkombinationen mit hchsten Werten markieren

Betraglich sehr hohe Werte, aber nicht immer signifikant nicht redundante Information

Results: Phase synchronization|ROC|70Phasensynchronisation: 8 signifikante Kanalkombinationen

Results: Event synchronization|ROC|71Event Synchronisation: 5 signifikante KanalkombinationenResults- Each channel combination separately -Phase synchronization:Event synchronization:Nominal size: p = 0.05 (One-sided test with 19 surrogates)Independent tests: q = 18 (18 channel combinations)At least r rejections:Significant,Null hypothesis rejected !

72

Results- ES II: Selection of best channel combination -Event synchronizationPhase synchronization73Measure profile surrogates- Two Evaluation schemes -Each channel combination separatelyNull hypothesis H0 I :Measure not suitable to find significant number of local effectspredictive of epileptic seizures.Null hypothesis H0 II :Measure not suitable to find maximum local effectspredictive of epileptic seizures.

Selection of best channel combination74Measure profile surrogates- Two Evaluation schemes -Each channel combination separatelyNull hypothesis H0 I :Measure not suitable to find significant number of local effectspredictive of epileptic seizures.Null hypothesis H0 II :Measure not suitable to find maximum local effectspredictive of epileptic seizures.

Selection of best channel combination75

Results- ES II: Selection of best channel combination -Event synchronizationPhase synchronization76

Results - Selection of best channel combination -Significant!Null hypothesis H0 II rejectedNot significant!Null hypothesis H0 II acceptedEvent synchronizationPhase synchronization| ROC-Area || ROC-Area |77Measure profile surrogates- Summary II: Measure profiles surrogates -Method for statistical validation of seizure predictions

Test against null hypothesis Level of significance

Estimating the effect of In sample optimizationPhase synchronization more significant than event synchronization.Given example:

Discrimination of pre- and interictal intervals:78 Introduction and motivation



Summary and outlookPredictability of epileptic seizures- Content -79Der Titel des Vortrages spiegelt auch seine Zweiteiligkeit wieder. Zum einen

Im Rahmen dieser Arbeit neu entwickelte Methode der Maprofil-SurrogatePredictability of epileptic seizures- Summary and outlook - Retrospective investigation: Evidence of significant changes before seizures Measures good enough for prospective application ???

introduction and motivation comparitive investigation:

Documents

brain epileptic seizure

seizure preictal

preictal state

epilepsy research

elektroden eine

nicht elektroden

area delineation

different regions