1. Brain tumor classification based on EEG hidden dynamics Authors: Rosaria silipo, Gustavo Deco, Helmut Bartsch Advisor: Dr. Hsu Graduate: Yu-Wei Su

2. Outline Motivation Objective Brain tumor classification and rest EEG A nonlinear characterization of the hidden dynamic of the EEG signal A cumulant based measure of information flow Nonlinear Markov models as an approximation of the structure of the underlying system

3. Outline( cont.) A hierarchy of Markov models The clinical application EEG time series hidden dynamics One- and two- dimensional analysis Conclusion Opinion

4. Motivation Meningeoma or malignant glioma across EEG cannot be stated Tumor is deeply in the brain or small with respect to the closest electrodes, no pathological change in EEG record

5. Objective Analysis of hidden dynamic of the rest EEG time series, to extract more information about pathological vs. normal status of the EEG records Nonlinear analysis can provide complex structure of the underlying system

6. Brain tumor classification and rest EEG The ElectroEncephaloGraphic(EEG) signal records the background activity of the brain The corresponding EEG time series consists of an periodic signal, call α-rthythm When a brain tumor arises, the EEG record of the brain somehow changes

7. Brain tumor classification and rest EEG (cont.)

8. Malignant glioma and meningeoma produce similar alterations on the EEG signal Malignant glioma consists of tumoral cells developing and expanding inside the brain Meningeoma represents an external mass of any nature pressing against the brain from outside

9. The framework of the experiment

10. A nonlinear characterization of the hidden dynamic of the EEG signal

11. A nonlinear characterization of the hidden dynamic of the EEG signal( cont.) Information flow is the loss of information in the observed variables, that is the decay of the statistical dependences between the whole past system and a point r steps ahead in the future Information flow indirectly describes the evolution of the system, that is hidden dynamic

12. A nonlinear characterization of the hidden dynamic of the EEG signal( cont.) Φ, the correspondence of the information flow with the signal’s hidden dynamic, is very complicated mathematical expression An approximate relationship statistically describes the system’s hidden dynamic with given measure of information flow

13. A nonlinear characterization of the hidden dynamic of the EEG signal( cont.) Model is defined as null hypothesis and a set of surrogate data is consistently with the null hypothesis To ascertain whether the null hypothesis is adequate to explain the hidden dynamic of the system Information flow is calculate for both the original observed variables and the surrogate time series

14. A nonlinear characterization of the hidden dynamic of the EEG signal( cont.) The null hypothesis is accepted, the model is supposed to adequately approximate the structure of the underlying system Higher order cumulants is adopted as measure of information flow

15. A nonlinear characterization of the hidden dynamic of the EEG signal( cont.) Markov models, to describe and predict the evolution of a system Markov model is assumed as null hypothesis about nonlinear structure A hierarchy Markovian hypotheses, starting with the Markov model with lowest order and increasing the order whenever the null hypothesis is rejected

16. Markov chain

17. A cumulant based measure of information flow

18. A cumulant based measure of information flow( cont.) The condition of statistical independence leads to

19. A cumulant based measure of information flow( cont.) The statistical dependences between n 1,…,n N past observations of the time series{x t } 1,…,{x t } n and the point r steps ahead in time series {x t } k

20. A cumulant based measure of information flow( cont.) m k (r)=0 represent a complete independence of time series k at future time t+r from the pasts of the whole system Increasing positive values of m k (r) indicate an increasing stronger dependence

21. Nonlinear Markov models as an approximation of the structure of the underlying system N-dimensional nonlinear Markov model of order{M 1,…,M N } is supposed to generate time series

22. Nonlinear Markov models as an approximation of the structure of the underlying system( cont.) Three two-layered feedforward NN are trained to estimate the parameters,, of the H Gaussians To approximate the Kth conditional density of the Markov model Three two-layer perceptrons are fed with{M 1,…M N } past values of the observed time series and produce H weights, H means, H variances

23. Nonlinear Markov models as an approximation of the structure of the underlying system( cont.) After NN training, Markov model can produce new sequences of data,, by MonteCarlo method new random values New sequence form a surrogate data set, s=10

24. A hierarchy of Markov models The t k (r) compares the m k (r) of the original time series K and of the ith surrogate instance of the kth time series for lookahead r If the assumption is rejected, the order of the model is increased, {M 1,…,M N }, until accepted if |t k (r)|<1.833(p-value=0.9),1<=r<=10

25. The clinical application Twenty minutes of 25-channel EEG, sampled at 250 Hz

26. One- and two- dimensional analysis Patient 1( no diagnosed pathology)

27. One- and two- dimensional analysis( cont.)

28. Patient 2(no diagnosed pathology)

29. One- and two- dimensional analysis( cont.)

30. Patient 3(dorsal meningeoma)

31. One- and two- dimensional analysis( cont.)

32. Patient 4 (frontal right meningeoma)

33. One- and two- dimensional analysis( cont.)

34. Patient 5 (dorsal glioma)

35. One- and two- dimensional analysis( cont.)

36. Patient 6( frontal right glioma)

37. One- and two- dimensional analysis( cont.)

38. Conclusion The algorithm has a quite complicated structure Supply different descriptions of the inter- dependence of the two brain hemispheres Stable EEG α-rhythm means very complex structure of the underlying system A loss of structure, when the glioma/meningeoma is located close to the thalamus region

39. Opinion Increasing reading ability Provide idea to medical research Apply data mining to the location of the brain tumor