A systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogram
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Date
2022-07-07
Journal Title
Journal ISSN
Volume Title
Publisher
SPRINGER
Abstract
The success of deep learning over the traditional machine learning techniques in handling artiicial intelligence application
tasks such as image processing, computer vision, object detection, speech recognition, medical imaging and so on, has
made deep learning the buzz word that dominates Artiicial Intelligence applications. From the last decade, the applications of deep
learning in physiological
signals such as
electrocardiogram
(ECG) have attracted
a good number of research.
However, previous surveys have
not
been
able
to
provide
a
systematic
comprehensive
review
including
biometric
ECG
based
systems
of
the
applications
of
deep
learning
in
ECG
with
respect
to
domain
of
applications.
To
address this
gap,
we conducted
a systematic
literature
review on the
applications of deep learning
in ECG including
biometric
ECG based
systems.
The
study analyzed
systematically, 150
primary
studies
with
evidence
of
the
application
of deep
learning
in
ECG.
The
study
shows
that
the
applications
of
deep
learning
in
ECG
have
been
applied
in
diferent
domains.
We
presented
a
new taxonomy of the domains
of application
of the deep
learning
in ECG.
The
paper also presented
discussions on
biometric
ECG based
systems and
meta-data
analysis
of the
studies
based
on the
domain,
area,
task,
deep
learning
models,
dataset
sources and preprocessing methods. Challenges
and potential
research opportunities
were highlighted
to enable
novel
research.
We
believe
that
this
study
will
be
useful
to
both
new
researchers
and
expert
researchers
who
are
seeking
to add knowledge to the already
existing
body of knowledge in ECG signal processing using deep learning
algorithm.
Description
Keywords
ywords Biometric Electrocardiogram System · Machine learning · Deep learning · Electrocardiogram · Driving
Citation
Journal of Ambient Intelligence and Humanized Computing