Fetal Electrocardiogram Recognition Using Multilayer Perceptron Neural Network

Fetal Electrocardiography (FECG) signal contains valuable and meaningful information that would help doctors to make decisions during pregnancy and labor. It is also an important indicator of the fetal status. However, extracting FECG from non-invasive sensors is not easy since the FECG signal is weak compared to the Maternal ECG (MECG) signal. In conventional signal processing methods, it requires an adaptive filter with the MECG signal and the mixture of Electrocardiography (ECG) signal to reveal the FECG signal. This procedure requires significant computation power and multiple sensors applied on the pregnant women. As machine learning algorithms become more and more popular, applying neural network to signal processing is widely adapted in all types of applications. This paper presents a method based on neural network to recognize the FECG signal from the abdominal ECG signal acquired by non-invasive sensors. Training and evaluation procedure are achieved in TensorFlow on a heterogeneous platform. This algorithm can precisely identify both MECG and FECG signal from the maternal abdominal ECG signal.