Download See review presentation

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;
The Epilepsy Problem, Clinical Terms, and need for
prediction
 Sensing, eeg, ecog, depth electrodes
 Animal models
 Wavelets
[email protected]
 Eshel
 Vagus nerve
 Heart/EEG, HRV, HS
 Complex Network Theory bocaletti
 Da Silva / Cerotti, Correlation,
 My contribution – level sets

Yaari & Beck, 2002; Lopes da Silva et al., 2003;
Ilana Podlipsky
Yaari & Beck, 2002; Lopes da Silva et al., 2003;

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 antiepileptic 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;

Partial Seizures (Most Common) Video
› Simple 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

Absence
› Once diagnosed with epilepsy, people are generally given antiepileptic 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
› 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 antiepileptic 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;
Yaari & Beck, 2002; Lopes da Silva et al., 2003;
Yaari & Beck, 2002; Lopes da Silva et al., 2003;
Yaari & Beck, 2002; Lopes da Silva et al., 2003;
Prof Paul Gompers
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).
Yaari & Beck, 2002; Lopes da Silva et al., 2003;
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.



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;
Longest nerve in the
body;
 sweat, blood pressure,
and heart activity
(heart rate)

Yaari & Beck, 2002; Lopes da Silva et al., 2003;




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;
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;
~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;

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;

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;

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;

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;

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;

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;
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;

A moving window dimension analysis was applied:
1.
Data sets were segmented into half-overlapping digitally
low-pass filtered consecutive epochs of 30 s duration.
2.
Calculation of the modified correlation integral - the
mean probability that the states at two different times are
close.
3.
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;

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;

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
log Cm (r )
log r
(in a linear region)
Yaari & Beck, 2002; Lopes da Silva et al., 2003;

The correlation dimension D2 is obtained by:
log Cm (r )
D2=slope of
log r
for decreasing r in a linear region

Alternatively:
d log Cm (r )
D2  lim
r 0
d log r

If no linear region
is found D2 = 10
Yaari & Beck, 2002; Lopes da Silva et al., 2003;
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;

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 preseizure state dimension reductions (Dr: 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;

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;

Correlation Dimension measure as presented
here is subjective.

Highly sensitive to noise.

Subject specific.
Yaari & Beck, 2002; Lopes da Silva et al., 2003;
the brain-heart
Vagus Nerve
axis
The
existence of
pre- ictal phase
Yaari & Beck, 2002; Lopes da Silva et al., 2003;
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;
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;

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;
 comet or torpedo-shaped
 unsupervised method advantage
Yaari & Beck, 2002; Lopes da Silva et al., 2003;
EEG-contained
information of
 (GEVA and KEREM, 1998)
an
HRV.
unsupervised method designed to deal
with merging and overlapping states
ability
to spot and classify
Yaari & Beck, 2002; Lopes da Silva et al., 2003;
Humans
Rats

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
Yaari & Beck, 2002; Lopes da Silva et al., 2003;
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;
Forecasting criteria
 Appearance
 Disappearance
 Dominant
 False negative - False positive

Yaari & Beck, 2002; Lopes da Silva et al., 2003;
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.
Yaari & Beck, 2002; Lopes da Silva et al., 2003;
Humans


Rats


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
Yaari & Beck, 2002; Lopes da Silva et al., 2003;