A neuro-fuzzy approach to the classification of fetal cardiotocograms

Cardiotocography (CTG) is a primary biophysical method of fetal monitoring. The assessment of the printed CTG traces is based on the visual analysis of patterns describing the variability of fetal heart rate signal. The correct interpretation of traces from a bedside monitor is rather difficult even for experienced clinicians, so computer-aided fetal monitoring systems have become very popular. At present effective techniques enabling automated conclusion generation based on cardiotocograms are still being searched. The presented work describes an application the Artificial Neural Network Based on Logical Interpretation of fuzzy if-then Rules (ANBLIR) to classification of the fetal state as being normal or abnormal. A set of quantitative parameters describing fetal cardiotocograms is the system input. To evaluate the quality of the classification we proposed the overall validity index as a function of various prognostic indices. The obtained results confirm the usability of the ANBLIR neuro-fuzzy system for records classification within computer-aided fetal surveillance systems.