SUITABILITY OF ARTIFICIAL NEURAL NETWORKS FOR FEATURE-EXTRACTION FROMCARDIOTOCOGRAM DURING LABOR

Fetal condition during labour is inferred from a continuous display of fetal heart rate and uterine contractions called the cardiotocogram ( CTG). The CTG requires a considerable expertise for correct interpreta tion, which is not always available. We are developing an intelligent system to support clinical decision-making during labour. The system's performance depends on its ability to classify features from the CTG similarly to experts. Artificial neural networks (NNs) can be taught b y experts for such tasks, and so may be particularly suitable. We foun d NNs suitable for feature extraction when the problem was reduced to small well defined tasks, and numerical algorithms were used to pre-pr ocess the raw data before application to the NNs. A NN with optimised dimensions was used in this way to classify the magnitude of decelerat ions, a feature clinicians find particularly difficult. The NN was com pared with the algorithm used in a commercial antenatal monitor and s ix reviewers which included two CTG experts. The experts were consiste nt (89.7% and 97.0%) and agreed well with each other (81.0%), whereas the non-experts were less consistent and agreed less well The NN agree d well with the experts (75.0% and 81.9%) but the algorithm agreed poo rly (56.5% and 68.9%). It was found that the algorithm's performance c ould be improved (72.1% and 76.7%) when modified to use additional inf ormation. Our earlier attempts to fully classify the raw CTG using a s ingle NN were unsuccessful because of the large number of data pattern s. A simplified approach to classify the magnitude and timing of decel erations was also unsuitable when contraction data was Of poor quality or absent. We have adopted a hybrid approach for our intelligent syst em for reliability and improved performance. CTG features are extracte d and classified by combined numerical algorithms and small NNs. These features, together with patient information, are then processed by an expert system which allows interaction with clinicians.