Intelligent fetal monitoring and decision support in the management of labour

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A Fuzzy System for Fetal Heart Rate Assessment

Abstract. The clinical interpretation of fetal heart rate traces is a difficult task that has led to the development computerised assessment systems. These systems are limited by their inability to represent uncertainty. This paper describes the first stage in the development of a fu2zy expert system for fetal heart rate assessment. A preliminary evaluation study comparing the initial fuzzy system with three clinicians and an existing crisp expert system is presented. The fuzzy system improved on the crisp system and achieved the highest overall performance. The use of fuzzy systems for analysis of fetal heart rate traces and similar time varying signals is shown to have potential benefit.

The Development of a Fuzzy Expert System for the Analysis of Umbilical Cord Blood

An assessment of neonatal outcome may be obtained from analysis of blood in the umbilical cord of an infant immediately after delivery. This can provide information on the health of the newborn infant and guide requirements for neonatal care, but there are problems with the technique. Samples frequently contain errors in one or more of the important parameters, preventing accurate interpretation and many clinical staff lack the expert knowledge required to interpret results. The development and validation of an expert system to overcome these difficulties is described. The initial development utilised conventional `crisp' logic within the rule base and this system was evaluated to commercial release. This expert system validates the raw data, provides an interpretation of the results for clinicians and archives all the results, including the quality control and calibration data, for permanent storage. Subsequent development went on to incorporate fuzzy logic into part of the expert system knowledge base, but tests of this preliminary fuzzy system showed that it performed worse than the original crisp expert system. A tuning algorithm was then employed to modify the fuzzy model and this process resulted in improved performance to a level comparable to clinicians and superior to the crisp system. Finally, the entire knowledge base was converted to utilise fuzzy logic and this `integrated' fuzzy expert system was validated against international expert opinion