Categorization of fetal heart rate patterns using neural networks

Digitized data from CTG (cardio-tocography) measurements (fetal heart rate and uterine contractions) have been used for categorization of typical heart rate patterns before and during delivery. Short time series of CTG data, about 7 minutes duration, have been used in the categorization process. In the first part of the study selected CTG data corresponding to 10 typical cases were used for purely autoassociative unsupervised training of a self-organizing map neural network (SOM). The network may then be used for objective categorization of CTG patterns through the map coordinates produced by the network. The SOM coordinates have then been compared In the second part of the study, a hybrid neural network consisting of a SOM network and a backpropagation network was trained with data corresponding to a number of basic heart rate pattern as described by 8 manually scaled indices. Test data (different than the training data) were then used to check the performance of the network. The present study shows that the categorization process, in which neural networks were used, can be reliable and agree well with the manual categorization. Since the categorization by neural networks is very fast and does not involve human efforts, it may be useful in patient monitoring