Prediction of onset of respiratory disorder in neonates

Premature extremely sick babies are currently monitored by skilled medical staff using numerous dedicated non-invasive sensors and associated monitoring equipment. This paper describes a method of “fusing” a number of the physiological signals and, by examining them simultaneously and continuously in time, produces an early warning for the onset of respiratory disorder (RD). The method uses a multilayer perceptron neural network to produce probabilities that the patient is going to suffer from RD at some point within the next thirty minutes. Initial results from this classification system are shown and suggestions for further work are given