Evaluation of prolonged fetal monitoring with normal and pathologic outcome probabilities determined by artificial neural network

Objective: The purpose of this study was to objectively evaluate prolonged fetal heart rate (FHR) monitoring, which has been difficult to do with conventional cardiotocogram (CTG).

Methods: FHR was analyzed by an artificial neural network computer that calculates probabilities of normal, pathologic and suspicious outcome. Earlier normal and pathologic outcome probabilities (OPs) recorded during 15-min intervals are averaged every 5 min. Initially, two curves (the averaged normal and pathologic OPs) are compared. Furthermore, a single curve traced for each difference between the averaged normal and pathologic OPs and its value are studied. Our FHR probability data are of 9 cases reported in a previous paper on neural network FHR analysis.

Results: In the 4 cases of normal neonatal condition, the trends of the averaged curves and the last averaged values were higher for normal OP than for pathologic OP, and the final values of the difference were >0. On the other hand, in the 5 cases of neonatal depression, the trend of the two curves and the final values were lower for normal than for pathologic OP; and the final difference values of averaged probabilities were <0. For prolonged monitoring, the single parameter is more useful than the comparison of the two curves.

Conclusion: A useful single parameter is obtained for the accurate and objective evaluation of prolonged FHR monitoring. The present method is promising for prospective studies using the combined system of experts and neural computers.