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Tensor decompositions for modelling epileptic seizures in EEG

Tensor decompositions for modelling epileptic seizures in EEG. Borbála Hunyadi Daan Camps Maarten De Vos Laurent Sorber Sabine Van Huffel Wim Van Paesschen Lieven De Lathauwer. Outline. Introduction Epileptic seizures EEG Tensor decompositions CPD BTD Signal model

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Tensor decompositions for modelling epileptic seizures in EEG

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  1. Tensor decompositions for modelling epileptic seizures in EEG Borbála Hunyadi Daan Camps Maarten De Vos Laurent Sorber Sabine Van HuffelWim Van Paesschen Lieven De Lathauwer

  2. Outline Introduction Epileptic seizures EEG Tensor decompositions CPD BTD Signal model Oscillatory behaviour Sum of exponentially damped sinusoids Simulation study Real EEG examples Conclusions

  3. Epilepsy Manifestation: epileptic seizures severe clinical symptoms Epileptic seizure: abnormal, synchronous activity of a large group of neurons Can be recorded in the EEG

  4. Seizures and EEG Repetitive, oscillatory pattern Evolution in Amplitue Frequency Topography Expert visual analysis Determinte seizure type, epilepsy syndrome Important for proper treatment

  5. Seizures and EEG • Repetitive, oscillatory pattern • Evolution in • Amplitue • Frequency • Topography • Expert visual analysis • Determinte seizure type, epilepsy syndrome • Important for proper treatment • BUT! Artefacts...

  6. Nature of EEG s1 s2 EEG X = AS x1⁞ xm Mixture andindirect measurement sn Key considerations: • Low SNR • Retrieve patterns of interest relying on a structured signal model • Appropriate representation and decomposition

  7. Tensor decompositions c1 c2 cR = + + ... + b1 b2 bR T a1 a2 aR cR c2 c1 I3 I3 I3 I3 L2 L1 LR BRT B1T B2T I2 = + + ... + I2 I1 I2 I2 I1 I1 I1 T A2 A AR A1 CPD: BTD-(L,L,1):

  8. Signal model: oscillatory behaviourBTD of wavelet expanded EEG tensors channel time frequency CWT-CPD (Acar 2007, De Vos 2007) CWT-BTD

  9. Signal model: sum of exp. damped sinusoidsBTD of Hankel expanded tensors channel hankel H-BTD (De Lathauwer, 2011)

  10. Simulation study 3 scenarios Stationary ictal pattern Ictal pattern with evolving frequency Ictal pattern propagating towards remote brain regions Ictal pattern superimposed on background EEG pattern muscle artefact (extracted from healthy EEG) Increasing noise levels (SNR: 1-0.1)

  11. Simulation studyStationary ictal pattern • sinusoidal CWT-CPD or H-BTD-(1,2,2) is optimal • CWT-BTD can be useful to model artefact sources • H-BTD performs best to reconstruct time course • All models equally good for retrieving the spatial map

  12. Simulation studyIctal pattern with evolving frequency • CWT-BTD or H-BTD is the optimal model (L=?), while CPD cannot capture the frequency evolution • CWT-BTD retrieves the TF matrices better than CPD (ICWT problem!) • All models equally good in retrieving the localisation

  13. Simulation studyPropagating ictal pattern • Fit a dipole on the reconstructed EEG • CWT-BTD-(2,1,2) can reveal both sources • fit 2 dipoles • fit 1 moving dipole • CPD retrieves 1 source located in between the 2 simulated sources

  14. Clinical examplesSevere artefact

  15. Clinical examplesEvolution in frequency

  16. Clinical examplesSpatial evolution

  17. Conclusion CWT-CPD Model stationary sources Onset localisation CWT-BTD Sources with evolving frequency or spatial distribution High power, complex artefacts H-BTD Seizure with fixed topography with arbitrary time course Precise reconstruction of time course

  18. Future work Automatic model selection Applications: Onset localisation: automatic model selection is needed Test on large real EEG dataset Seizure detection: find optimal model with trial-error and use the model to detect subsequent seizures

  19. Thank you! Any questions?

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