The classification of oesophageal 24 h pH measurements using a Kohonen self-organizing feature map

Analysis of 24 h oesophageal pH studies can be problematic with many patients asymptomatic during the investigation, despite observations of reflux. The aim of this study was to carry out a cluster analysis of ambulatory pH studies to determine any underlying patterns and classes within the data. The results of 900 24 h pH studies were investigated using the Kohonen self-organizing feature map (SOFM), a neural network that can be used to identify clusters within multidimensional data. The clinical features were presented to the network and the main classes identified. The SOFM-based analysis showed that patients clinically assessed as having symptomatic reflux during the study could be described by four major classifications. The results also showed that the probability of identifying a correlation between symptoms and reflux during an investigation varies from 0.49 to 0.78 for the classes identified. The developed network may be a useful tool in the classification of pH data. The cluster-based technique may offer an alternative to standard statistical techniques for high-dimensional gastrointestinal data and form the basis of an expert system for the automated analysis of pH data.