Advanced Fuzzy Rule Set-Based Approach for Fetal Heart Rate Detection and Classification

Fetal heart rate measurement is important in evaluation of fetal well-being and the visible sign of compromise or dysfunction. Congenital heart diseases rank among the main causes of infant mortality and can be identified by means of fetal heart rate monitoring. An effective and reliable method of recognizing and categorizing fetal heart rate is hence important for correct diagnosis and early intervention. The directed research utilizes an improved fuzzy rule set to boost fetal heart rate detection and classification precision. This technique incorporates fuzzy algorithms to adapt to the non-linearity of the fetal cardiotocographic signal as compared to precise methodologies. Some of the difficulties that arise when using FHR analysis include handling of maternal heart rates, power line interference and other biomass signals which tend to complicate the fetal signal. With fuzzy rule sets, this work shall be able to generate better classification between fetal and maternal components for accurate extraction and differentiation of FHR patterns. Moreover, this approach is able to account for uncertainties and temporal changes of the signal and could potentially provide a more robust solution for clinical applications of fetal monitoring and early detection of congenital heart diseases.