A New Method for Foetal Electrocardiogram Extraction Using Adaptive Nero-Fuzzy Interference System Trained With PSO Algorithm

Abstract—A New Method for Foetal Electrocardiogram Extraction Using Adaptive Nero-Fuzzy Interference System Trained With PSO Algorithm. This paper presents a new method for extracting the Foetal Electrocardiogram (FECG) signal form the two ECG signals recorded at the thoracic and abdominal areas of the mother’s skin. The thoracic ECG is assumed to be completely maternal ECG (MECG) while the abdominal ECG is assumed to be a combination of mother’s and foetus’s ECG signals and random noise. The maternal component of the in the abdominal ECG is a nonlinearly transformed version of the MECG. The method uses Adaptive Nero-Fuzzy Inference System (ANFIS) structure for identifying the nonlinear transformation. We have used Particle Swarm Optimization (PSO) as a new tool for training the ANFIS structure. By identifying the nonlinear transformation, < Final Year Projects > we have extracted FECG by subtracting the aligned version of the MECG signal from the abdominal ECG (AECG) signal. We validate our new method on both real and synthetic ECG signals. For synthetic signals, we have used a subjective criterion in addition to objective criteria. The results of extracting the FECG are promising comparing with conventional methods.