Prediction of Epileptic Seizures PhD Conversion Seminar Elma O’Sullivan-Greene Life Sciences, NICTA VRL Dept. Electrical & Electronic Engineering, The University of Melbourne Supervisors: Prof. Iven Mareels Dr. Levin Kuhlmann A/Prof. Anthony Burkitt Dr. Chung-Yao Kao

Talk Outline 1.Epilepsy: a disorder of the brain 2.Data available for engineering analysis 3.Current approaches to epileptic seizure prediction, and their limitations 4.Work completed and in progress 5.Proposed avenues for project

Talk Outline 1.Epilepsy: a disorder of the brain 2.Data available for engineering analysis 3.Current approaches to epileptic seizure prediction, and their limitations 4.Work completed and in progress 5.Proposed avenues for project

Epilepsy: a disorder of the brain Epilepsy is a neurological disorder –Characterised by recurrent “seizures” –Associated with abnormally excessive or synchronous neuronal activity in the brain Most common serious neurological condition –Prevalence of epilepsy varies across geographical regions within the range of 0.5% to 4% of the total population (WHO) Current Treatment –AED (Antiepileptic Drugs) - undesirable side-effects –Surgical removal of the epileptic brain tissue

Motivation For Seizure Prediction The ability to predict seizures would have a profound impact on the quality of life of epilepsy suffers. Our proposed solution –An Implantable device incorporating seizure prediction short-term electric stimulation treatment for seizure prevention Continuous electric stimulation is in use, and shows good results in many patients (unknown side effects for long term use) No robust seizure prediction algorithm has been published to date

Talk Outline 1.Epilepsy: a disorder of the brain 2.Data available for engineering analysis 3.Current approaches to epileptic seizure prediction, and their limitations 4.Work completed and in progress 5.Proposed avenues for project

Data Source: Electroencephalography (EEG) Recordings of the fluctuating electric fields of the brain –Electric fields due to ionic currents in the extra cellular fluid –Neurons (nerve cells) choose when to fire impulses based on this ionic current information

Data Source: Electroencephalography (EEG) Recordings of the fluctuating electric fields of the brain Scalp EEG data Intracranial EEG data

Talk Outline 1.Epilepsy: a disorder of the brain 2.Data available for engineering analysis 3.Current approaches to epileptic seizure prediction, and their limitations 4.Work completed and in progress 5.Proposed avenues for project

Can Seizures Be Predicted? Evidence for a definable pre-ictal (pre-seizure) period –Clinically undisputed indicative systematic changes are present in some patients prior to seizure onset Mood changes, nausea, headache –Several signal processing studies argue that a pre-ictal state can be defined based on Measures of synchronisation between EEG channels Non-linear dynamics measures

Current Prediction Approaches Linear Approaches Spectral analysis Linear Modelling Energy measures –Minimal success: brain function nonlinear? Nonlinear Approaches –Based on state space reconstruction Dimension Lyapunov Exponents Entropy –Minimal success: initial promising results failed to be reproduced with other data sets

State Space Reconstruction/ Delay Embedding N at least O(10 15 )

State Space Reconstruction/ Delay Embedding Combine to reconstruct an N-dimensional system

Limitations of Delay Reconstruction The original framework (Takens’/ Aeyels) for delay reconstruction requires: –Stationarity of the dataset –Noise free data set –A time series from an autonomous dynamical system –Low dimensionality of underlying dynamical system However the EEG is ultimately an unsuitable signal for this framework –Highly non-stationary data set –High levels of measurement noise in EEG recordings (artefact) –The brain is not an autonomous system (brain processes external inputs) –No conclusive evidence that the brain/ epileptic events are low dimensional

Talk Outline 1.Epilepsy: a disorder of the brain 2.Data available for engineering analysis 3.Current approaches to epileptic seizure prediction, and their limitations 4.Work completed and in progress 5.Proposed avenues for project

Work Completed and in Progress Modification of the EEG signal for delay reconstruction Addressing the noise limitation –Taking the difference between 2 closely spaced intracranial electrodes Cancel common mode input from far away dynamical subsystems (Stark) Representation of local dynamics Significant reduction in common mode artefact (50 Hz mains pick-up) Consider the Brain as: spatially distributed system interacting distinct local subsystems

Work Completed and in Progress Modification of the EEG signal for delay reconstruction Addressing the low dimensionality limitation –Hypothesis: The brain is lower-dimensional during a seizure –Perhaps there is enough stationary data in the period just prior to a seizure to warrant a reconstruction

An existing seizure prediction algorithm: Dynamical Similarity Index (DSI) –Le Van Quyen (1999) –Creates templates of brain dynamics from delay reconstruction of EEG data –Seizure anticipation state declared for large sustained deviations of dynamic template from reference (far from seizure)

Work Completed and in Progress Application of modified EEG signal to DSI algorithm –Reference template from pre-ictal data (low-dimensional/stationarity considerations) –EEG signal used: difference between 2 closely spaced intracranial electrodes (noise consideration) Preliminary results –Sensitivity: 25%-100% across 3 patients –False Positive rate: FP/hr across 3 patients

Work Completed and in Progress No major improvement seen with preliminary results over original DSI algorithm Why? –Pre-ictal low dimensionality of underlying system is an unproven hypothesis –Other noise: muscle artefact, cardiac artefact … Conclusion –Future prediction methods should concentrate on non-delay- reconstruction based methods

Talk Outline 1.Epilepsy: a disorder of the brain 2.Data available for engineering analysis 3.Current approaches to epileptic seizure prediction, and their limitations 4.Work completed and in progress 5.Proposed avenues for project

Project Proposal Nonlinear System analysis without reconstruction –Data-driven pathway Brain System Unknown state space system, F x k+1 =F (x k, ω k ) Measured EEG Data Represented by the function, H z k =H (x k, ω k ) Epilepsy Prediction Represented by the function, G y k+1 =G (x k, ω k ) ?

Project Proposal - Entropy via Data Compression An entropy measure as a prediction candidate –Low-dimensional object indicative of underlying brain state Entropy, as measured in the brain, can be viewed as –a measure of how “chaotic” the brain system is –a measure of information transfer in the brain

Entropy via Data Compression Techniques Instead of computing entropy via delay reconstruction…. Estimating entropy via Data-Compression Techniques –Markov Model –Context-Tree Model –Model based on Independent Component Analysis (ICA) Let observed time-series data (EEG) be an element of a finite alphabet of symbols Advantages of this approach –More robust in the presence of noise –Does not require stationarity of the data set –Can be applied to High Dimensional Systems

Entropy Estimation from a Markov Model Markov model: –Estimates future symbols based on k-past past symbols –Symbolic time series analysis

Entropy Estimation from a Weighted Context Tree Weighted Context tree: –Estimates future symbols based on k-past past symbols –Each node or “context” contains information of symbol history –Automated recursive weight probability associated with each context –Contexts automatically discarded on basis of improved performance –Entropy: h = L / N L=Source Code length N=Time

Entropy Estimation from an ICA based model Measured EEG Channels: x = A s Find the transformation of the data W = A -1 such that the coding lengths of the components are minimised Non-linear independent component methods Using several EEG channels: spatial information Statistically Independent components x = f( s ) y = h( x )

Seizure Prediction Proposal Have discussed Entropy as a seizure prediction candidate as estimated from data compression techniques. Next: An alternative probabilistic approach to data-based seizure prediction……

Bifurcation Phenomenon – prediction by tracking the trajectory of bifurcation parameter, μ, over time Bifurcation part of thalmo-cortical brain model, Robinson (2003) Seizure Prediction: Decision Markov Process Motivation for a statistical decision model: Dynamical systems representations of the epileptic brain: Probabilistic Transitions between two chaotic attractors Normal Epileptic Phase portrait of computer model of brain’s thalmo-cortical network, Lopes Da Silva (2003)

Seizure Prediction: Decision Markov Process 3 state model Transition probabilities t ij assigned through analysis of EEG data Potential for intervention applications: control input to minimise the transition to seizure state

Conclusion: Research Proposal Proposed Avenues for Seizure Prediction: –Entropy as estimated from data compression techniques Markov Process Context Tree Independent Component Analysis –Decision Markov Process Potential for the theoretical expansion of dynamical system time-series analysis – for the application of real world biological data

Thank you for your attention Questions?