Epilepsy Prediction from - PowerPoint Presentation

Slide1

Epilepsy Prediction fromEEG and ECOG Data

Nathan IntratorComputer ScienceTel-Aviv Universitycs.tau.ac.il/~nin

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

Collaborators

TAU Hospital

: Talma Hendler, Itzhak Fried, Miri

Noifeld

,

TAU

: Eshel Ben Jacob, Ilana

Podipsky

, Andrey Zhdanov

Slide2

Outline

The Epilepsy Problem, Clinical Terms, and need for predictionSensing, eeg, ecog, depth electrodesAnimal modelsWaveletsEshel

Vagus nerveHeart/EEG, HRV, HS

Complex Network Theory

bocaletti

Da Silva / Cerotti, Correlation, My contribution – level sets

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

boaz@eng.tau.ac.il

Slide3

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

Ilana Podlipsky

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

Slide4

Epilepsy

EpilepsySynchronous firing of neurons which create high amplitude electrical discharges; this ‘storm’ inhibits other neural signals from getting through and disables function areas of the brainStatisticsEveryone's brain has the ability to produce a seizure under the right conditions1 in 20 will have an epileptic seizure at some time in their lifeTreatmentOnce 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

EEGRecording of neural activity of targeted neurons / neural regions in brainOutputs brainwaves with associated rhythms and frequencies

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

Slide5

Types of Epilepsy

Partial Seizures (Most Common) VideoSimple partialynchronous firing 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

AbsenceOnce diagnosed with epilepsy, people are generally given anti-epileptic medication. With the appropriate treatment, up to 70% of people could be seizure free.

Characteristics / symptomsSeizures (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

Slide6

Epilepsy

EpilepsySynchronous firing of neurons which create high amplitude electrical discharges; this ‘storm’ inhibits other neural signals from getting through and disables function areas of the brainStatisticsEveryone's brain has the ability to produce a seizure under the right conditions1 in 20 will have an epileptic seizure at some time in their lifeTreatment

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 seizureEEGRecording of neural activity of targeted neurons / neural regions in brainOutputs brainwaves with associated rhythms and frequencies

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

Slide7

Examples of Seizure

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

Slide8

Complex Network Theory

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

Slide9

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

Slide10

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

Prof Paul Gompers

Slide11

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

Vagus nerve stimulation (VNS) 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).

Slide12

Results of HRV Prediction

HumansRatsSuccessful forecasting

Tachycardia period

success rate

86%

|∆RRI| Vs. RRIforecasting times

1.5-11 min.

Successful forecasting

Bradycardia period

success rate

82%

|∆RRI| Vs. RRI

forecasting times

2.5-9 min.

Slide13

Fyodor Dostoyevsky(1821-1881)

Most known epileptic novelistGave vivid accounts of apparent temporal lobe seizures in his novel “The Idiot”Describes an aura he used to get before the onset of a seizureYaari & Beck, 2002; Lopes da Silva et al., 2003;

Slide14

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;

Slide15

The

Vagus NerveLongest 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;

Slide16

The

Vagus Nerve (cont)Modulates the SYMPATHETIC and PARASYMPATHETIC systemGoes 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;

Slide17

The problem

~30% of epileptics left untreated and victim ofviolent seizuresInjuries resulting from epilepsy is most oftencaused by convulsive seizuresIf a ‘lead-time’ could be provided by a seizuredetection system, physical injury would be greatly reduced and quality of life increasedYaari & Beck, 2002; Lopes da Silva et al., 2003;

Slide18

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 & Ajmone Marsan 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;

Slide19

Brain as a Dynamic System

A dynamical system consists of StateDynamicsState – the information necessary at any time instant to describe the future evolution of a systemDynamics – defines how the state evolves over timeYaari & Beck, 2002; Lopes da Silva et al., 2003;

Slide20

Attractors and Dimensions

AttractorSet of states towards which the system evolves – Characterizes the long term behavior of the systemDimension of a systemDescribes the amount of information required to specify a point on the attractor - the long term behavior of a systemMore complex behavior – more information is required to describe this behavior – higher dimension of the system

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

Slide21

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;

Slide22

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;

Slide23

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;

Slide24

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 ictal ECoG/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;

Slide25

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 D2 for 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;

Slide26

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;

Slide27

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: C

m

(r) ≈ rD2 D2

= slope of (in a linear region)

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

Slide28

Calculation of Correlation Dimension

The correlation dimension D2 is obtained by:D2=slope offor decreasing r in a linear region Alternatively:

If no linear region

is found D

2

= 10

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

Slide29

Results

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.

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;

Slide30

Results

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 (

D

r: Z = – 3.41, P = 0.0006;Tr: Z

= – 3.52, P = 0.0004).

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;

Slide31

Discussion

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. 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;

Slide32

Discussion

Correlation Dimension measure as presented here is subjective.Highly sensitive to noise.Subject specific.Yaari & Beck, 2002; Lopes da Silva et al., 2003;

Slide33

Introduction cont.

the brain-heart axisVagus Nerve

The existence of

pre- ictal phase

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

Slide34

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;

Slide35

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;

Slide36

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;

Slide37

Fuzzy clustering approach

comet or torpedo-shapedunsupervised method advantageYaari & Beck, 2002; Lopes da Silva et al., 2003;

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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;

Slide39

Data resources

HumansRatsHumans

21 patients records, archived records

The recording machinery

simultaneous EEG and video recording

ECG channelvisual 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

OUTPUT

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

Slide40

Method cont.

Choice of analysis parameters|∆RRI| Vs. RRIembedding dimension NFor this experiment –

Both features

N = 3

number of clusters

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

Slide41

Method cont.

Forecasting criteria Appearance Disappearance Dominant

False negative - False positive

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

Slide42

Results

HumansRatsSuccessful forecasting

Tachycardia period

success rate

86%

|∆RRI| Vs. RRIforecasting times

1.5-11 min.

Successful forecasting

Bradycardia period

success rate

82%

|∆RRI| Vs. RRI

forecasting times

2.5-9 min.

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

Slide43

Results cont.

HumansRatsprediction failures

false negative

One case

false positive

Two casesLonger records

prediction failures

false negative

none

false positive

Two cases

Ignoring changes shown in control rats

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