Neural Network Computer Analysis of Fetal Heart Rate.

Abstract

> Objective: A nonsubjective evaluation of intrapartum fetal heart rate (FHR) with a neural network (NNW) computer system and its clinical application. Methods: Eight simple FHR data were input into the NNW computer after 16-step normalizations. The computer was composed of 40 units in the input layer, 30 in intermediate layer, and 3 in the output layer, and the probabilities to be normal, suspicious, and pathological were obtained at the output. Before use, the computer was trained 10,000 times by 50-min teacher FHR data of 20 cases with known outcomes. The trained NNW computer was tested by FHRs of another 29 cases. The outcome probabilities in 15 min were calculated every 5 min in another 10 cases, and the bar graphs of the probabilities were displayed in sequence in the trendgrams. Results: The trained NNW computer was 100% accurate in the internal check; in the external check 86% of the results were evaluated correctly with the cardiotocogram, Apgar score, and umbilical arterial pH of the 29 test cases. The FHR scores of our conventional computer FHR analysis were higher in the suspicious and pathological groups than the normal group, and the fetal distress index was high in the pathological group. The trendgrams were simply accurate in typically normal or abnormal cases, transitory abnormal probabilities were shown in intermediate cases, and mixed suspicious and pathological probabilities suggested pathological outcome. Conclusions: The outcome probabilities and their trendgrams in the NNW FHR analysis are promising in objective decision making in the intrapartum stage.