Candidate:
Alaina
Mahalanabis
Title:
Generative
Adversarial
Networks
for
ECG
generation,
translation,
imputation
and
denoising
Date:
July
21,
2022
Time:
16:00
Place:
online
Supervisor(s):
Ganesh,
Vijay
Abstract:
Artificial
Intelligence
is
increasingly
being
used
in
medical
applications.
One
challenge
present
in
AI
in
medicine
is
not
having
high
quality
datasets
available
for
training
machine
learning
models.
In
this
work,
I
explore
two
different
methods
of
generating
high
quality
medical
data.
In
the
first
approach,
I
used
a
cycleGANs
as
novel
method
for
ECG
translation,
imputation
and
denoising.
In
the
second
method,
I
present
a
novel
algorithm
for
generating
high
quality
ECG
data
that
uses
a
machine
learning
framework
called
Generative
Adversarial
Networks
and
explanation
AI
systems.
Through
empirical
evaluation,
I
show
that
both
methods
improve
over
state-of-the-art
methods
in
their
respective
applications.
This
thesis
demonstrates
that
machine
learning
methods
can
be
used
to
address
the
data
scarcity
problem
in
the
medical
setting.
Thursday, July 21, 2022 4:00 pm
-
4:00 pm
EDT (GMT -04:00)