Journal Home > Just Accepted

This paper deals with detecting fetal electrocardiogram FECG signals from single-channel abdominal lead. It is based on the Convolutional Neural Network (CNN) combined with advanced mathematical methods, such as independent component analysis (ICA), Singular Value Decomposition (SVD), and a dimension-reduction technique like Nonnegative Matrix Factorization (NMF). Due to the highly disproportionate frequency of the fetus’s heart rate compared to the mother’s, the time-scale representation clearly distinguishes the fetal electrical activity in terms of energy. Furthermore, we can disentangle the various components of fetal ECG, which serve as inputs to the CNN model to optimize the actual FECG signal, denoted by FECGr, which is recovered using the SVD-ICA process. The findings demonstrate the efficiency of this innovative approach, which may be deployed in real-time.

Publication history
Copyright
Rights and permissions

Publication history

Received: 28 August 2022
Accepted: 27 September 2022
Available online: 08 December 2022

Copyright

© The author(s) 2023

Rights and permissions

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

Return