Categorization of fetal heart rate patterns using neural networks.

Digitized data from CTG (cardiotocography) 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 min 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 auto associative 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 were then compared. In the second part of the study, a hybrid neural network consisting of a SOM network and a Back-Propagation network (BP) was trained with data corresponding to a number of basic heart rate patterns as described by eight manually selected 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.

Prediction of blood glucose levels in diabetic patients using a hybrid AI technique.

One of the problems in the management of the diabetic patient is to balance the dose of insulin without exactly knowing how the patient's blood glucose concentration will respond. Being able to predict the blood glucose level would simplify the management. This paper describes an attempt to predict blood glucose levels using a hybrid AI technique combining the principal component method and neural networks. With this approach, no complicated models or algorithms need be considered. The results obtained from this fairly simple model show a correlation coefficient of 0.76 between the observed and the predicted values during the first 15 days of prediction. By using this technique, all the factors affecting this patient's blood glucose level are considered, since they are integrated in the data collected during this time period. It must be emphasized that the present method results in an individual model, valid for that particular patient under a limited period of time. However, the method itself has general validity, since the blood glucose variations over time have similar properties in any diabetic patient.