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Prediction of Epileptic Seizures PhD Conversion Seminar

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Title: Prediction of Epileptic Seizures PhD Conversion Seminar


1
Prediction of Epileptic Seizures PhD Conversion
Seminar
Elma OSullivan-Greene Life Sciences, NICTA
VRL Dept. Electrical Electronic Engineering,
The University of Melbourne elmao_at_ee.unimelb.edu.a
u
Supervisors Prof. Iven Mareels Dr.
Levin Kuhlmann A/Prof. Anthony Burkitt
Dr. Chung-Yao Kao
2
Talk Outline
  • Epilepsy a disorder of the brain
  • Data available for engineering analysis
  • Current approaches to epileptic seizure
    prediction, and their limitations
  • Work completed and in progress
  • Proposed avenues for project

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

4
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

5
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

6
Talk Outline
  • Epilepsy a disorder of the brain
  • Data available for engineering analysis
  • Current approaches to epileptic seizure
    prediction, and their limitations
  • Work completed and in progress
  • Proposed avenues for project

7
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

8
Data Source Electroencephalography (EEG)
  • Recordings of the fluctuating electric fields of
    the brain
  • Scalp EEG data
  • Intracranial EEG data

9
Talk Outline
  • Epilepsy a disorder of the brain
  • Data available for engineering analysis
  • Current approaches to epileptic seizure
    prediction, and their limitations
  • Work completed and in progress
  • Proposed avenues for project

10
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

11
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

12
State Space Reconstruction/ Delay Embedding
N at least O(1015)
13
State Space Reconstruction/ Delay Embedding
Combine to reconstruct an N-dimensional system
14
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

15
Talk Outline
  • Epilepsy a disorder of the brain
  • Data available for engineering analysis
  • Current approaches to epileptic seizure
    prediction, and their limitations
  • Work completed and in progress
  • Proposed avenues for project

16
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

17
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

18
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)

19
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 1-6.6 FP/hr across 3 patients

20
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

21
Talk Outline
  • Epilepsy a disorder of the brain
  • Data available for engineering analysis
  • Current approaches to epileptic seizure
    prediction, and their limitations
  • Work completed and in progress
  • Proposed avenues for project

22
Project Proposal
  • Nonlinear System analysis without reconstruction
  • Data-driven pathway

Brain System Unknown state space system,
F xk1F (xk , ?k)
Measured EEG Data Represented by the function,
H zkH (xk , ?k)
Epilepsy Prediction Represented by the function,
G yk1G (xk , ?k)
?
23
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

24
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

25
Entropy Estimation from a Markov Model
  • Markov model
  • Estimates future symbols based on k-past past
    symbols
  • Symbolic time series analysis

26
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 LSource Code
    length NTime

27
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 )
28
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

29
Seizure Prediction Decision Markov Process
  • Motivation for a statistical decision model
  • Dynamical systems representations of the
    epileptic brain
  • Bifurcation Phenomenon prediction by tracking
    the trajectory of bifurcation parameter, µ, over
    time
  • Bifurcation part of thalmo-cortical brain
    model, Robinson (2003)
  • Probabilistic Transitions between two chaotic
    attractors
  • Normal Epileptic
  • Phase portrait of computer model of brains
    thalmo-cortical network, Lopes Da Silva (2003)

30
Seizure Prediction Decision Markov Process
  • 3 state model
  • Transition probabilities tij assigned through
    analysis of EEG data
  • Potential for intervention applications control
    input to minimise the transition to seizure state

31
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

32
  • Thank you for your attention
  • Questions?
  • elmao_at_ee.unimelb.edu.au
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