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 6 at http://www.giphysiology.org/NewWave/volume_9_4.pdf  ). 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.