3 research outputs found
Linear and Nonlinear Measures and Seizure Anticipation in Temporal Lobe Epilepsy
In a recent paper, we showed that the value of a nonlinear quantity computed from scalp electrode data was correlated with the time to a seizure in patients with temporal lobe epilepsy. In this paper we study the relationship between the linear and nonlinear content and analyses of the scalp data. We do this in two ways. First, using surrogate data methods, we show that there is important nonlinear structure in the scalp electrode data to which our methods are sensitive. Second, we study the behavior of some simple linear metrics on the same set of scalp data to see whether the nonlinear metrics contain additional information not carried by the linear measures. We find that, while the nonlinear measures are correlated with time to seizure, the linear measures are not, over the time scales we have defined. The linear and nonlinear measures are themselves apparently linearly correlated, but that correlation can be ascribed to the influence of a small set of outliers, associated with muscle artifact. A remaining, more subtle relation between the variance of the values of a nonlinear measure and the expectation value of a linear measure persists. Implications of our observations are discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46310/1/10827_2004_Article_5252207.pd
Neural Anomalies Monitoring: Applications to Epileptic Seizure Detection and Prediction
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
Hilbert-Huang Transform: biosignal analysis and practical implementation
Any system, however trivial, is subjected to data analysis on the signals it produces. Over the last 50 years the influx of new techniques and expansions of older ones have allowed a number of new applications, in a variety of fields, to be analysed and to some degree understood.
One of the industries that is benefiting from this growth is the medical field and has been further progressed with the growth of interdisciplinary collaboration. From a signal processing perspective, the challenge comes from the complex and sometimes chaotic nature of the signals that we measure from the body, such as those from the brain and to some degree the heart.
In this work we will make a contribution to dealing with such systems, in the form of a recent time-frequency data analysis method, the Hilbert-Huang Transform (HHT), and extensions to it.
This thesis presents an analysis of the state of the art in seizure and heart arrhythmia detection and prediction methods.
We then present a novel real-time implementation of the algorithm both in software and hardware and the motivations for doing so. First, we present our software implementation, encompassing realtime
capabilities and identifying elements that need to be considered for practical use. We then translated this software into hardware to aid real-time implementation and integration.
With these implementations in place we apply the HHT method to the topic of epilepsy (seizures)
and additionally make contributions to heart arrhythmias and neonate brain dynamics. We use the HHT and some additional algorithms to quantify features associated with each application for detection and prediction. We also quantify significance of activity in such a way as to merge prediction and detection into one framework. Finally, we assess the real-time capabilities of our
methods for practical use as a biosignal analysis tool