brain tumor classification based on eeg hidden dynamics

Brain tumor classification based on EEG hidden dynamics

Authors: Rosaria silipo, Gustavo Deco, Helmut Bartsch

Advisor: Dr. Hsu

Graduate: Yu-Wei Su

Outline

Motivation Objective Brain tumor classification and rest EEG A nonlinear characterization of the hidden dyna

mic of the EEG signal A cumulant based measure of information flow Nonlinear Markov models as an approximation

of the structure of the underlying system

Outline( cont.)

A hierarchy of Markov models The clinical application EEG time series hidden dynamics One- and two- dimensional analysis Conclusion Opinion

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

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

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

Brain tumor classification and rest EEG (cont.)

Brain tumor classification and rest EEG (cont.)

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

The framework of the experiment

A nonlinear characterization of the hidden dynamic of the EEG signal

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

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

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

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

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

Markov chain

A cumulant based measure of information flow

A cumulant based measure of information flow( cont.)

The condition of statistical independence leads to

A cumulant based measure of information flow( cont.)

The statistical dependences between n1,…,nN past observations of the time series{xt}1,…,{xt}n and the point r steps ahead in time series {xt}k

A cumulant based measure of information flow( cont.)

mk(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 mk(r) indicate an increasing stronger dependence

Nonlinear Markov models as an approximation of the structure of the underlying system

N-dimensional nonlinear Markov model of order{M1,…,MN} is supposed to generate time series

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{M1,…MN} past values of the observed time series and produce H weights, H means, H variances

khu

kh k

h

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

Nkktx ,...,1|}ˆ{

NkkMx ,...,1|1}ˆ{

A hierarchy of Markov models

The tk(r) compares the mk(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, {M1,…,MN}, until accepted if |tk(r)|<1.833(p-value=0.9),1<=r<=10

)(,ˆ rmik

The clinical application

Twenty minutes of 25-channel EEG, sampled at 250 Hz

One- and two- dimensional analysis

Patient 1( no diagnosed pathology)

One- and two- dimensional analysis( cont.)

One- and two- dimensional analysis( cont.)

Patient 2(no diagnosed pathology)

One- and two- dimensional analysis( cont.)

One- and two- dimensional analysis( cont.)

Patient 3(dorsal meningeoma)

One- and two- dimensional analysis( cont.)

One- and two- dimensional analysis( cont.)

Patient 4 (frontal right meningeoma)

One- and two- dimensional analysis( cont.)

One- and two- dimensional analysis( cont.)

Patient 5 (dorsal glioma)

One- and two- dimensional analysis( cont.)

One- and two- dimensional analysis( cont.)

Patient 6( frontal right glioma)

One- and two- dimensional analysis( cont.)

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

Opinion

Increasing reading ability Provide idea to medical research Apply data mining to the location of the

brain tumor