ENHANCING THE ACCURACY OF CARDIOTOCOGRAM ANALYSIS USING FUZZY LOGIC SYSTEM
Cardeiotocogram based monitoring needs a reliable fuzzy logic system to reduce the incidence of unnecessary medical intervention and fetal injury during child labour, due to a high degree of uncertainty and imprecision. Presently, electronic fetal monitoring is based almost entirely on the Cardiotocogram (CTG), which is a continuous display of the fetal heart rate (FHR) pattern together with the contraction of the womb. Despite the widespread use of the Cardiotocogram, it has limited significant importance in fetal outcome as the Cardiotocogram alone does not always provide all the information required to improve the outcome of child labour. The Cardiotocogram machine and fuzzy system with five patients were used in the analysis to determine the accuracy of fetal heart rate (FHR). Seventy-nine (79) rules were coded into the system to drive the fuzzy inference system. The aggregation and implication of these rules are explained using surface plots to determine the rule that can fire during optimization process. The results show that the Cardiotocogram machine gave an accuracy of ±0.039%, ±0.043%, ±0.047%,±0.048% ±0.053%, while the fuzzy logic system gave an accuracy of ±0.0147%, ±0.0373, ±0.0373%, ±0.0373% and ±0.0152%, respectively. The comparison of these results confirmed that the fuzzy logic system has provided significant method of enhancing the Cardiotocogram analysis with a higher degree of accuracy between 0.6% – 3.8% and makes the system less sensitive to noise or error. With the results obtained, it is evident that the fuzzy logic system can be used to improve the efficiency of the clinician in making accurate diagnosis.