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.
Haylett
K R
KR
Medical Engineering, Manchester Royal Infirmary, Oxford Road, Manchester M13 9WL, UK. k.haylett@computer.org
Vales
P
P
McCloy
R F
RF
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004947:Esophagus / Q000737:chemistry* / Q000503:physiopathology*; D005764:Gastroesophageal Reflux / Q000175:diagnosis* / Q000503:physiopathology; D005766:Gastrointestinal Contents / Q000737:chemistry*; D006801:Humans; D006863:Hydrogen-Ion Concentration; D018670:Monitoring, Ambulatory / Q000379:methods; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D013223:Statistics as Topic