The fetal electrocardiogram (FECG) signal reflects the electrical activity of the fetal heart. It contains information about the health status of the fetus and therefore, an early diagnosis of any cardiac defects before delivery increases the effectiveness of the appropriate treatment. The proposed approach extracts the FECG from two ECG signals recorded at the thoracic and abdominal areas of the mother’s skin, with the help of a hybrid soft computing technique called Adaptive Neuro-Fuzzy Inference System (ANFIS). The thoracic ECG is assumed to be almost completely maternal (MECG) while the abdominal ECG is considered to be composite as it contains both the mother’s and the fetus’ ECG signals. The principle used for the elimination of artifacts is ANC. The results demonstrate the effectiveness of the proposed technique in extracting the FECG component from abdominal signals of very low maternal to fetal signal-to-noise ratios. Finally, a pure FECG is obtained with higher SNR. Also, we apply one of the swarm intelligent branches, namely particle swarm optimization (PSO) to furthermore tune the ANFIS parameters and to extract the pure FECG signal with higher SNR and lower error rate.

Adaptive neuro-fuzzy inference system fetal ECG maternal ECG Particle swarm optimization