Describes the development of a neural network-based diagnostic system for fetal heart rate monitoring.
HYFM-III extends nonlinear methods for fetal monitoring; leaves open prospective validation before clinical use.
In this paper, we construct data-base for fetal heart rate (FHR) data and develop the FHR Monitering system to diagnose fetus, HYFM-III. For diagnostic system, a few statistical decision making mechanism are adopted, such as approximate entropy, neural networks, and logistic discrimination. Since FHR data is very chaotic, we mostly adopt nonlinear statistical methods. On the basis of this system, we expect to develop expert system for early detection of abnormal fetus.
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Cha et al. (2006) studied this question.
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