Prediction of Epileptic Seizures PhD Conversion Seminar Elma O’Sullivan-Greene Life Sciences, NICTA VRL Dept. Electrical & Electronic Engineering, The University of Melbourne [email protected]Supervisors: Prof. Iven Mareels Dr. Levin Kuhlmann A/Prof. Anthony Bur Dr. Chung-Yao Kao
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Prediction of Epileptic Seizures PhD Conversion Seminar
Prediction of Epileptic Seizures PhD Conversion Seminar. Elma O’Sullivan-Greene Life Sciences, NICTA VRL Dept. Electrical & Electronic Engineering, The University of Melbourne [email protected]. Supervisors: Prof. Iven Mareels Dr. Levin Kuhlmann A/Prof. Anthony Burkitt - PowerPoint PPT Presentation
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Prediction of Epileptic Seizures PhD Conversion Seminar
Elma O’Sullivan-Greene
Life Sciences, NICTA VRL
Dept. Electrical & Electronic Engineering, The University of Melbourne
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(1015)
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: 1-6.6 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 SystemUnknown state space system, F
xk+1=F (xk , ωk)
Measured EEG DataRepresented by the function, H
zk=H (xk , ωk)
Epilepsy PredictionRepresented by the function, G
yk+1=G (xk , ωk)
?
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 tij 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