Forecasting Fetal Heartbeats with Neural Networks 1

The given task is to forecast the intervals between the heartbeats recorded from a fetus. The six tested neural network models combine input windows, hidden layer feedback, and self-recurrent unit feedback in different ways. The two networks combining an input window and hidden layer feedback performed best. One of them has additional self-recurrent feedback loops around the units in the state layer, which enable the system to deal with time-warped patterns. It turns out to be reasonable to combine several techniques for processing the temporal aspects inherent to the input sequence. 1 The Task Using the cardiotocogram (the CTG) is common for routine fetal monitoring. The CTG consists of fetal heartbeat and uterine contraction signals. At the site under investigation, such signals have been recorded and stored for further analysis. Usually, the heart rate is pre-processed before it is analyzed. In this study, though, each single heartbeat interval is recorded for obtaining greater precision. The overall aim is the development of an intelligent alarm system which can be employed as a tool for decision support. The rst step when processing the