Adaptive Neuro-Fuzzy Inference System for antepartum antenatal care using phonocardiography

This work discusses development of a model based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for evaluation of fetal health status using phonocardiography. The model integrates adaptable fuzzy inputs with a modular neural network to deal with the imprecision and uncertainty in the interpretation of the FHR data from phonocardiographic signals. A zero-order Takagi-Sugeno model is chosen for designing ANFIS architecture. The diagnostic parameters e.g., Baseline FHR, Baseline Variability, Acceleration and Deceleration of the FHR are derived from the fPCG signals for training and testing of the model. The elicited fuzzy rules derived from clinical guidelines and other resources are implemented into the ANFIS expert model. The performance of the ANFIS model is evaluated in terms of sensitivity and overall accuracy. The results have indicated that the ANFIS can be implemented effectively and provides high accuracy for antepartum antenatal care through phonocardiography.