Advanced Machine Learning for classification of EEG traits as Parkinson’s biomarker
2014
We
aim
to
develop
non-‐invasive,
low-‐cost
preclinical
markers
for
synucleinopathies
(Parkinson’s Disease
-‐
PD
or
Dementia
with
Lewy
Bodies
-‐
DLB)
with
impact
on
neuroprotection.
In
a
first
stage we
have
applied
Machine
Learning
techniques
to
the
analysis
of
the
spontaneous,
waking
EEG
of REM
Sleep
Behavior
Disorder
(RBD)
patients.
We
know
that
patients
with
RBD
may
evolve
towards PD
and
other
synucleinopathies
(i.e.
DLB)
providing
a
useful
guide
in
the
search
for
biomarkers. EEG
has
proven
to
be
sensitive
to
brain
alterations
related
to
both
RBD
and
PD
patients,
and
it
is known
that
patients
with
either
RBD
or
de
novo
PD
without
obvious
cognitive
alterations
show similar
EEG/MEG
alterations
(mainly
slowing
of
the
EEG)
while
awake. In
the
work
described
herein
we
achieve
the
classification
of
patients
according
to
their
ultimate diagnosis,
differentiating
among
different
patient
groups.
We
have
analyzed
EEG
data
from
a study
of
RBD
patients
at
the
Center
for
Advanced
Research
in
Sleep
Medicine
of
Montreal.
The EEG
was
recorded
from
8
patients
who
years
later
evolved
to
PD,
8
patients
who
later
evolved
to LBD,
10
patients
with
RBD
who
did
not
convert,
and
17
healthy
controls.
It
is
worth
mentioning that
80%
of
the
PD
and
DLB
patients
developed
disease
at
a
follow-‐up
of
8
years. Support
Vector
Machines
were
applied
to
different
feature
types.
Absolute
Band
Power
features extracted
each
4
seconds
outperform
the
other
ones.
After
non-‐parametric
feature
selection
of the
5
most
discriminative
channel-‐band
combinations,
classification
was
applied.
A
procedure
for performance
estimation
close
to
operational
conditions,
which
is
denoted
as
leave-‐pair-‐subjects-‐ out,
has
been
employed
for
evaluation
of
the
implemented
system.
Excellent
performance
levels, AUC
93.75-‐99%,
were
obtained
for
all
realized
group
comparisons. In
conclusion,
we
believe
that
these
results
support
the
idea
that
classification
of
EEG
shows
great potential
as
a
preclinical
biomarker.
Our
results
confirm
the
existent
literature,
where
EEG
slowing characterizes
RBD
and
PD/DLB
patients.
Furthermore
we
have
attained
this
characterization
at
the individual
subject
level
for
the
first
time
by
employing
machine
learning.
Given
that
EEG
was acquired
8
years
before
disease
development
in
a
majority
of
cases,
we
believe
that
the
utilization of
the
developed
system
for
conversion
prognosis
of
synucleinopathies
(PD
or
DLB)
will
have
real impact
on
both
early
treatment
and
drug
development.
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