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 AEC...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics Vol. PP; pp. 1 - 11
Main Authors: Asadi, Amirreza, Ghaffari, Aboozar
Format: Journal Article
Language:English
Published: United States IEEE 16.09.2025
Subjects:
Delay effects
Electrocardiography
Encoding
Fetal ECG extraction
IIR filters
Independent component analysis
Low-pass filters
Matrix decomposition
Non-negative sparse coding
Pipelines
Principal component analysis
R-peak
Single-channel extraction
Sparse matrices
Time delay method
ISSN:2168-2194, 2168-2208, 2168-2208
Online Access:Get full text
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Summary: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.
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ISSN:2168-2194
2168-2208
2168-2208
DOI:10.1109/JBHI.2025.3609696