There
have
been
numerous
efforts
in
the
field
of
electronics
with
the
aim
of
merging
the
areas
of
healthcare
and
technology
in
the
form
of
low
power,
more
efficient
hardware.
However
one
area
of
development
that
can
aid
in
the
bridge
of
healthcare
and
emerging
technology
is
in
Information
and
Communication
Technology
(ICT).
Here,
databasing
and
analysis
systems
can
help
bridge
the
wealth
of
information
available
(blood
tests,
genetic
information,
neural
data)
into
a
common
framework
of
analysis.
Also,
ICT
systems
can
integrate
real-time
processing
from
emerging
technological
solutions,
such
as
developed
low-power
electronics.
This
work
is
based
on
this
idea,
merging
technological
solutions
in
the
form
of
ICT
with
the
need
in
healthcare
to
identify
normality
in
a
patients’
health
profile.
In
this
work
we
develop
this
idea
and
explain
the
concept
more
thoroughly.
We
then
go
on
to
explore
two
applications
under
development.
The
first
is
a
system
designed
around
monitoring
neural
activity
and
identifying,
through
a
processing
algorithm,
what
is
normal
activity,
such
that
we
can
identify
anomalies,
or
abnormalities
in
the
signal.
We
explore
Epilespy
with
seizure
detection
and
prediction
as
an
application
case
study
to
show
the
potential
of
this
method.
The
motivation
being
that
current
methods
of
prediction
have
proven
to
be
unsuccessful.
We
show
that
using
our
algorithm
we
can
achieve
significant
success
in
seizure
prediction
and
detection,
above
and
beyond
current
methods.
The
second
application
explores
the
link
between
genetic
information
and
standard
tests
(blood,
urine
etc...)
and
how
they
link
in
together
to
define
a
personalised
benchmark.
We
show
how
this
could
work
and
the
steps
that
have
been
made
towards
developing
such
a
database.