NEURAL-NETWORK FOR AUTOMATIC-ANALYSIS OF MOTILITY DATA

Continuous recording of intraluminal pressures for extended periods of time is currently regarded as a valuable method for detection of esop hageal motor abnormalities. A subsequent automatic analysis of the res ulting motility data relies on strict mathematical criteria for recogn ition of pressure events. Due to great variation in events, this metho d often fails to detect biologically relevant pressure variations. We have tried to develop a new concept for recognition of pressure events based on a neural network. Pressures were recorded for over 23 hours in 29 normal volunteers by means of a portable data recording system. A number of pressure events and non-events were selected from 9 record ings and used for training the network. The performance of the trained network was then verified on recordings from the remaining 20 volunte ers. The accuracy and sensitivity of the two systems were comparable. However, the neural network recognized pressure peaks clearly generate d by muscular activity that had escaped detection by the conventional program. In conclusion, we believe that neurocomputing has potential a dvantages for automatic analysis of gastrointestinal motility data.