Fetal ECG Signal Extraction Using RBF, BP, and ANFIS Artificial Neural Networks

This article presents MATLAB source code implementations for fetal electrocardiogram (ECG) signal extraction using artificial neural networks including RBF (Radial Basis Function), BP (Backpropagation), and ANFIS (Adaptive Neuro-Fuzzy Inference System) approaches. Fetal ECG signal extraction represents a significant research domain that enables medical professionals and researchers to analyze and diagnose fetal health conditions. The implementation covers detailed explanations of each method's fundamental principles and practical implementation steps. The RBF network implementation utilizes Gaussian basis functions for nonlinear mapping, while the BP network employs gradient descent optimization with backpropagation for weight updates. The ANFIS approach combines neural network learning capabilities with fuzzy logic inference systems, implemented through hybrid learning algorithms that combine least-squares and backpropagation methods. Key MATLAB functions implemented include network initialization, training data preprocessing, feature extraction routines, and signal reconstruction algorithms. The code incorporates signal filtering techniques, noise reduction methods, and performance evaluation metrics to ensure accurate fetal ECG separation from maternal signals. The discussion extends to practical applications of fetal ECG signal extraction in clinical settings and outlines future research directions in the field. Through this comprehensive exploration, readers will gain enhanced understanding of fetal ECG extraction methodologies and techniques, providing valuable references for related research and practical applications in biomedical signal processing.