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Ambulatory
oesophageal manometry
My latest
research has involved investigating and improving techniques to
analyse ambulatory oesophageal manometry. Ambulatory manometry
was first introduced in the mid eighties but has gradually gone
out of fashion. Initially there was great enthusiasm for this
technique. Standard laboratory based oesophageal manometry had
been and still is problematic. With just a small window of
observations how do the results relate to day to day swallowing
activity and symtoms? The
results from laboratory studies are still problematic and the
relationship between observations and diseases is by far from
clear. Terms and concepts are frequently introduced and there
is much confusion about what value the results can be given.
How should we interpret the results?
Typical computer
analysis is not trusted for 24 hour ambulatory recordings (see
page 5 for a simple model of reflux and page 6 for a review
http://www.medeng.net/volume_9_4.pdf
). This is largely because the waves are complex and the
algorithms used are linear only detecting previously well
defined patterns. Small signals and ‘noise’ can
disrupt the analysis. Importantly anything not predefined is
not captured by the basic peak detection algorithms.
The data mining
approach
In our approach
a simple data mining technique is used. This described in the
following papers presentation.
http://www.medeng.net/IEE%20MASP.pdf
and http://www.medeng.net/York%202007.pdf
In summary all
possible events of interest are collected and then clustered
using a Kohonen self organising feature map. These clusters can
then be explored by the user. In addition post cluster
classification can be carried out and results compared between
subjects or groups of subjects
Examples
The following
examples are copyright © K.R. Haylett 2008 and can only
published or used with the consent of the author.
Example
1: This
shows an example of analysing a 24 hour recording and the
clusters created. It
also shows
post cluster classification into primary, secondary and
non-peristaltic waveform classes.
Example
2:
This shows a
comparison between a series of controls and patients with
Barrett’s oesophagus using post cluster classification.
The
technique is MATLAB based and can be
carried out online via the internet. For further information
please contact me.
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