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### Seizure prediction by non-linear time series analysis of brain electrical activity

Epilepsy Prediction fromEEG and ECOG Data

Nathan Intrator

Computer Science

Tel-Aviv University

cs.tau.ac.il/~nin

Collaborators

TAU Hospital: Talma Hendler, Itzhak Fried, Miri Noifeld,

TAU: Eshel Ben Jacob, Ilana Podipsky, Andrey Zhdanov

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Outline

- The Epilepsy Problem, Clinical Terms, and need for prediction
- Sensing, eeg, ecog, depth electrodes
- Animal models
- Wavelets
- Eshel
- Vagus nerve
- Heart/EEG, HRV, HS
- Complex Network Theory bocaletti
- Da Silva / Cerotti, Correlation,
- My contribution – level sets

boaz@eng.tau.ac.il

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

IlanaPodlipsky

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Epilepsy

- Epilepsy
- Synchronous firing of neurons which create high amplitude electrical discharges; this ‘storm’ inhibits other neural signals from getting through and disables function areas of the brain
- Statistics
- Everyone's brain has the ability to produce a seizure under the right conditions
- 1 in 20 will have an epileptic seizure at some time in their life
- Treatment
- Once diagnosed with epilepsy, people are generally given anti-epileptic medication. With the appropriate treatment, up to 70% of people could be seizure free.
- Characteristics / symptoms
- Seizures (40 different types)
- ‘Aura’, a sensory hallucination, often precludes a seizure
- EEG
- Recording of neural activity of targeted neurons / neural regions in brain
- Outputs brainwaves with associated rhythms and frequencies

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Types of Epilepsy

- Partial Seizures (Most Common) Video
- Simple partialynchronousfiring of neurons which create high amplitude electrical discharges; this ‘storm’ inhibits other neural Complex partial
- Statistics
- Everyone's brain has the ability to produce a seizure under the right conditions
- 1 in 20 will have an epileptic seizure at some time in their life
- Absence
- Once diagnosed with epilepsy, people are generally given anti-epileptic medication. With the appropriate treatment, up to 70% of people could be seizure free.
- Characteristics / symptoms
- Seizures (40 different types)
- ‘Aura’, a sensory hallucination, often precludes a seizure
- EEG
- Recording of neural activity of targeted neurons / neural regions in brain
- Outputs brainwaves with associated rhythms and frequencies

Epilepsy.com

Epilepsy

- Epilepsy
- Synchronous firing of neurons which create high amplitude electrical discharges; this ‘storm’ inhibits other neural signals from getting through and disables function areas of the brain
- Statistics
- Everyone's brain has the ability to produce a seizure under the right conditions
- 1 in 20 will have an epileptic seizure at some time in their life
- Treatment
- Once diagnosed with epilepsy, people are generally given anti-epileptic medication. With the appropriate treatment, up to 70% of people could be seizure free.
- Characteristics / symptoms
- Seizures (40 different types)
- ‘Aura’, a sensory hallucination, often precludes a seizure
- EEG
- Recording of neural activity of targeted neurons / neural regions in brain
- Outputs brainwaves with associated rhythms and frequencies

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Examples of Seizure Morphologies

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Complex Network Theory

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

- A lead is attached to the left vagus nerve in the lower part of the neck.
- It delivers mild electrical stimulations on demand
- Deep brain stimulation
- targets the thalamus (which relays pain, temperature, and touch sensations to the brain).

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Results of HRV Prediction

Humans

Rats

Successful forecasting

Tachycardia period

success rate

86%

|∆RRI| Vs. RRI

forecasting times

1.5-11 min.

Successful forecasting

Bradycardia period

success rate

82%

|∆RRI| Vs. RRI

forecasting times

2.5-9 min.

Fyodor Dostoyevsky(1821-1881)

- Most known epileptic novelist
- Gave vivid accounts of apparent temporal lobe seizures in his novel

“The Idiot”

- Describes an aura he used to get before the onset of a seizure

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Vagus Nerve Stimulation

- Longest nerve in the body;
- sweat, blood pressure, and heart activity (heart rate)

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

TheVagus Nerve

- Longest nerve in the body;
- Originates in the Brainstem
- Goes all the way to the stomach, passing through essential organs (Vocal cords, heart, lungs, intestines)
- Also controls sweat, blood pressure, and heart activity (e.g., heart rate)

Yaari & Beck, 202; Lopes da Silva et al., 2003;

TheVagus Nerve (cont)

- Modulates the SYMPATHETIC and PARASYMPATHETIC system
- Goes all the way to the stomach, passing through essential organs (Vocal cords, heart, lungs, intestines)
- Also controls sweat, blood pressure, and heart activity (heart rate)

Yaari & Beck, 202; Lopes da Silva et al., 2003;

Theproblem

- ~30% of epileptics left untreated and victim of

violent seizures

- Injuries resulting from epilepsy is most often

caused by convulsive seizures

- If a ‘lead-time’ could be provided by a seizure

detection system, physical injury would be greatly reduced and quality of life increased

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Distinctive Features of Epilepsy

- The epileptogenic process is characterized by abnormal synchronous burst discharges in neuronal cell assemblies recordable during and in between seizures (Matsumoto & Ajmone‐Marsan 1964a, Matsumoto & AjmoneMarsan 1964b; Babb et al. 1987).
- The transition to a seizure is caused by an increasing spatial and temporal non-linear summation of the activity of discharging neurons (Calvin 1971; Calvin et al. 1973).
- Due to the typically unpredictable occurrence of seizures it remains difficult to investigate the rules governing the initiation of seizure activity in humans.

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Brain as a Dynamic System

- A dynamical system consists of
- State
- Dynamics
- State – the information necessary at any time instant to describe the future evolution of a system
- Dynamics – defines how the state evolves over time

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Attractors and Dimensions

- Attractor
- Set of states towards which the system evolves – Characterizes the long term behavior of the system
- Dimension of a system
- Describes the amount of information required to specify a point on the attractor - the long term behavior of a system
- More complex behavior – more information is required to describe this behavior – higher dimension of the system

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Brain as a Dynamic System

- The application of the theory of non-linear dynamics offers information about the dynamics of the neuronal networks.
- Several authors have shown that EEG/ECoG signals exhibit chaotic behavior (Basar,1990; Frank et al,1990; Pijn et al,1991).
- The correlation dimension D2(Grassberger and Procaccia1983), provides good information about EEG complexity and chaotic behavior. (Mayer-Kress and Layne (1987) )

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Dynamics of Epileptic EEG

- The spatio-temporal dynamics of the epileptogenic focus is characterized by temporary transitions from high-to low-dimensional system states (dimension reductions)(Lehnertz & Elger 1995,1997).
- These dimension reductions allow the lateralization and possibly localization of the epileptogenic focus

(Lehnertz & Elger 1995,1997).

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Seizure prediction by non-linear time series analysis of brain electrical activityChristian E. Elger, Klaus Lehnertz (1998)

- Do prolonged and pronounced transitions from high - to low - dimensional system states characterize a pre-seizure phase?
- The identification of this phase would enable new diagnostic and therapeutic possibilities in the field of epileptology.

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Methods

- Electrocorticograms (ECoG) and stereoelectroencephalograms (SEEG) of 16 patients
- 68 EEG epochs were analyzed.
- Fifty‐two data sets of state 1; mean duration: 19.5 ± 6.9 min; range: 6–40 min; minimum distance to any seizure: 24 h.
- 16 data sets of state 2; mean duration before the electrographic seizure onset: 15.1 ± 5.8 min; range: 10–30 min; seizure onset was defined as earliest signs of ictalECoG/SEEG patterns).

Seizure prediction by non‐linear time series analysis of brain electrical activity Christian E. Elger, Klaus Lehnertz (1998)

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Methods

- A moving window dimension analysis was applied:
- Data sets were segmented into half-overlapping digitally low-pass filtered consecutive epochs of 30 s duration.
- Calculation of the modified correlation integral - the mean probability that the states at two different times are close.
- Estimate of the correlation dimension D2for each epoch.

Seizure prediction by non‐linear time series analysis of brain electrical activity Christian E. Elger, Klaus Lehnertz (1998)

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Calculation of correlation dimension

- Digital low-pass filtering (cut-off frequency 40 Hz)
- Construction of m-dimensional vectors Xm(i) (i = 1, N; m = 1,. . . , 30) from the initial ECoG samples v(i) (i = 1, N) of a given electrode using the method of delays (Takens, 1981):

Seizure prediction by non‐linear time series analysis of brain electrical activity Christian E. Elger, Klaus Lehnertz (1998)

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Correlation Integral

- For a stepwise decreasing radius r of a hypersphere centered at each vector Xm(i) for increasing m the correlation integral Cm(r) was calculated as(Grassberger and Procaccia, 1983):
- Counts the number of pairs of points with distance less then r.
- For small r: Cm(r) ≈ rD2
- D2 = slope of (in a linear region)

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Calculation of Correlation Dimension

- The correlation dimension D2is obtained by:

D2=slope of

for decreasing r in a linear region

- Alternatively:
- If no linear region

is found D2 = 10

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

ResultsSeizure prediction by non‐linear time series analysis of brain electrical activity Christian E. Elger, Klaus Lehnertz (1998)

- For each selected electrode of the ECoG sets, a time profile of the estimated D2, values was constructed.
- The seizure (S) exhibits lowest dimension values.

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

ResultsSeizure prediction by non‐linear time series analysis of brain electrical activity Christian E. Elger, Klaus Lehnertz (1998)

- For both states maximum dimension reductions were always found inside the epileptogenic focus regardless of spike activity.
- During state 2, maximum dimension reductions were always observed in time windows immediately preceding seizures.
- In state 1:
- Dimension reductions with a mean of 1.0; range 0.5-2.5.
- Mean duration of 5.25min; range 1.00–10.75 min.
- In state 2:
- Dimension reduction mean 2.0; range: 1.0–3.5.
- Mean duration 11.50 min; range: 4.25–25.00 min.
- Highly significant differences between maximum state 1 and pre-seizure state dimension reductions (Dr: Z = – 3.41, P = 0.0006;Tr: Z = – 3.52, P = 0.0004).

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

DiscussionSeizure prediction by non‐linear time series analysis of brain electrical activity Christian E. Elger, Klaus Lehnertz (1998)

- A reduced dimensionality of brain activity, as soon as it is of sufficient size and duration, precisely defines states which proceed to a seizure.
- I was demonstrated that the features of the pre-seizure state differ clearly from the one found during seizure.
- Pronounced dimension reductions of pre-seizure electrical brain activity are restricted to the area of the epileptogenic focus, they can reflect increasing degree of synchronicity of pathologically discharging neurons.

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Discussion

- Correlation Dimension measure as presented here is subjective.
- Highly sensitive to noise.
- Subject specific.

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Introduction cont.

the brain-heart axis

Vagus Nerve

The existence of

pre- ictal phase

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Introduction cont.

This study

Forecasting seizures

Partial complex – humans

Generalized - rats

Novel method for HRV analysis

Ph.D. D.H.Kerem

Ph.D. A.B.Geva

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Known Methods

Spectral analysis of the time series of R-R intervals

non-linear dynamics

shortcoming -

inability to account for non-stationary states and transients

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Known Methods cont.

Time-varying power spectral density estimation

Attractors and correlation dimensions

Karhunen-Love transform-based signal analysis method

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Fuzzy clustering approach

comet or torpedo-shaped

unsupervised method advantage

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Chosen method

EEG-contained information of HRV.

(GEVA and KEREM, 1998)

an unsupervised method designed to deal with merging and overlapping states

ability to spot and classify

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Data resources

Humans

- Humans
- 21 patients records, archived records
- The recording machinery
- simultaneous EEG and video recording
- ECG channel
- visual inspection by an EEG expert
- The actual database

- Rats
- Hyperbaric-oxygen
- ECG and EEG filtering and recording
- Rats effects
- Time period analyzing
- Control rats Vs. research rats

Rats

- OUTPUT

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Method cont.

Choice of analysis parameters

|∆RRI| Vs. RRI

embedding dimension N

For this experiment –

Both features

N = 3

number of clusters

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Method cont.

Forecasting criteria

Appearance

Disappearance

Dominant

False negative - False positive

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Results

Humans

- Successful forecasting
- Tachycardia period
- success rate
- 86%
- |∆RRI| Vs. RRI
- forecasting times
- 1.5-11 min.

- Successful forecasting
- Bradycardia period
- success rate
- 82%
- |∆RRI| Vs. RRI
- forecasting times
- 2.5-9 min.

Rats

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Results cont.

Humans

- prediction failures
- false negative
- One case
- false positive
- Two cases
- Longer records

- prediction failures
- false negative
- none
- false positive
- Two cases
- Ignoring changes shown in control rats

Rats

Yaari & Beck, 2002; Lopes da Silva et al., 2003;

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