SCTD-ICA: A ICA-Based Approach for Fetal ECG Extraction from Single Channel Abdominal ECG

The fetal electrocardiogram (FECG) signal provides valuable insights into the fetal cardiac status during pregnancy. As the maternal abdominal ECG (AECG) signal is influenced by various sources, including the maternal ECG (MECG) signal, it becomes challenging to separate the FECG signal from the AECG effectively. In this paper, we introduce a pipeline centered on Independent Component Analysis (ICA) for the extraction of FECG from a single-channel abdominal recording, termed "Single Channel Time Delay ICA (SCTD-ICA)". A notable limitation of ICA is its requirement for multi-channel data. To overcome this constraint, the proposed pipeline uses the time delay method to map single-channel data into multidimensional data. Subsequently, the multidimensional data is employed as input for the ICA algorithm. Ultimately, the analysis of the power spectrum leads to the automated identification of the fetal component. The effectiveness of the proposed pipeline is assessed on two real datasets: Abdominal and Direct Fetal Electrocardiogram Database (ADFECGDB) and Set-A of 2013 PhysioNet/Computing in Cardiology Challenge Database (PCDB). F1 metrics for fetal QRS detection in ADFECGDB and PCDB are obtained at 96.14% and 95.76%, respectively. In comparison to state-of-the-art methods, the proposed pipeline demonstrates comparable performance with approaches based on deep learning. As a result, the proposed single-channel pipeline is appropriate for continuous maternal and fetal health monitoring.