Traditional
treatments
do
not
work
on
33%
of
epileptic
patients.Warning
devices
employing
seizure
prediction
or
forecasting
algorithms
could
bring
patients
a
newfound
quality
life.
These
would
attempt
to
detect
the
preictal
period,
transitional
moment
between
regular
brain
activity
and
seizure,
warn
user.
Several
past
methodologies
have
been
developed,
triggering
an
alarm
when
detecting
but
few
clinically
applicable.
Recent
studies
suggested
paradigm
change
that
takes
probabilistic
approach
instead
crisp
one
prediction.
The
is
substituted
by
constant
risk
assessment
analysis.
To
best
our
knowledge,
no
direct
comparison
using
same
database
has
made.
This
paper
explores
capable
compares
them
with
ones.
Using
data
from
EPILEPSIAE
database,
we
developed
several
patient-specific
different
classifiers
(a
Logistic
Regression,
15
Support
Vector
Machines
ensemble,
Shallow
Neural
Networks
ensemble).
Results
show
increase
sensitivity
in
relative
up
146%
number
displaying
improvement
over
chance
200%.
results
suggest
may
be
more
suitable
for
warning
than
Almost
one-third
of
epileptic
patients
fail
to
achieve
seizure
control
through
anti-epileptic
drug
administration.
In
those
cases,
prediction
plays
a
significant
role
in
clinical
management
and
therapy.
Seizure
algorithms
aim
identify
the
preictal
period
that
Electroencephalogram
(EEG)
signals
can
capture.
However,
this
is
associated
with
substantial
heterogeneity.
The
present
work
proposes
patient-specific
using
post-processing
techniques
explore
existence
set
chronological
brain
events
precedes
seizures.
study
was
conducted
37
from
EPILEPSIAE
database.
designed
methodology
combines
univariate
linear
features
classifier
based
on
Support
Vector
Machines
(SVM)
two
handle
pre-seizure
temporality
an
easily
explainable
way,
employing
knowledge
network
theory.
Chronological
Firing
Power
approach,
we
considered
as
sequence
three
separated
time.
Cumulative
assumed
overlapping
events.
These
methodologies
were
compared
approach
typical
machine
learning
pipeline.
Our
results
showed
may
improve
performance.
This
new
strategy
performed
above
chance
for
62%
patients,
whereas
Control
only
validated
49%
its
models.
Traditional
treatments
do
not
work
on
33%
of
epileptic
patients.Warning
devices
employing
seizure
prediction
or
forecasting
algorithms
could
bring
patients
a
newfound
quality
life.
These
would
attempt
to
detect
the
preictal
period,
transitional
moment
between
regular
brain
activity
and
seizure,
warn
user.
Several
past
methodologies
have
been
developed,
triggering
an
alarm
when
detecting
but
few
clinically
applicable.
Recent
studies
suggested
paradigm
change
that
takes
probabilistic
approach
instead
crisp
one
prediction.
The
is
substituted
by
constant
risk
assessment
analysis.
To
best
our
knowledge,
no
direct
comparison
using
same
database
has
made.
This
paper
explores
capable
compares
them
with
ones.
Using
data
from
EPILEPSIAE
database,
we
developed
several
patient-specific
different
classifiers
(a
Logistic
Regression,
15
Support
Vector
Machines
ensemble,
Shallow
Neural
Networks
ensemble).
Results
show
increase
sensitivity
in
relative
up
146%
number
displaying
improvement
over
chance
200%.
results
suggest
may
be
more
suitable
for
warning
than