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A Review of Fetal ECG Signal Processing Issues and Promising Directions

Open access
Sameni·2010·The Open Pacing, Electrophysiology & Therapy Journal

Abstract

The field of electrocardiography has been in existence for over a century, yet despite significant advances in adult clinical electrocardiography, signal processing techniques and fast digital processors, the analysis of fetal ECGs is still in its infancy. This is, partly due to a lack of availability of gold standard databases, partly due to the relatively low signal-to-noise ratio of the fetal ECG compared to the maternal ECG (caused by the various media between the fetal heart and the measuring electrodes, and the fact that the fetal heart is simply smaller), and in part, due to the less complete clinical knowledge concerning fetal cardiac function and development. In this paper we review a range of promising recording and signal processing techniques for fetal ECG analysis that have been developed over the last forty years, and discuss both their shortcomings and advantages. Before doing so, however, we review fetal cardiac development, and the etiology of the fetal ECG. A selection of relevant models for the fetal/maternal ECG mixture is also discussed. In light of current understanding of the fetal ECG, we then attempt to justify recommendations for promising future directions in signal processing, and database creation.

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Metadata

Authors
Sameni
Venue
The Open Pacing, Electrophysiology & Therapy Journal
Published
2010
Volume
3
Pages
4-20
ISSN
1876-536X (ISSN-L)
OpenAlex
W2010755794
Cited by
410
References
3
FWCI
9.40
Type
journal article
Language
en
Open access
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CoChat. (2026). Citation check: A Review of Fetal ECG Signal Processing Issues and Promising Directions. https://app.cochat.ai/papers/10.2174/1876536x01003010004
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Sameni. (2010). A Review of Fetal ECG Signal Processing Issues and Promising Directions. The Open Pacing, Electrophysiology & Therapy Journal. https://doi.org/10.2174/1876536x01003010004

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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