Literature DB >> 31478873

A Review of Signal Processing Techniques for Non-Invasive Fetal Electrocardiography.

Radana Kahankova, Radek Martinek, Rene Jaros, Khosrow Behbehani, Adam Matonia, Michal Jezewski, Joachim A Behar.   

Abstract

Fetal electrocardiography (fECG) is a promising alternative to cardiotocography continuous fetal monitoring. Robust extraction of the fetal signal from the abdominal mixture of maternal and fetal electrocardiograms presents the greatest challenge to effective fECG monitoring. This is mainly due to the low amplitude of the fetal versus maternal electrocardiogram and to the non-stationarity of the recorded signals. In this review, we highlight key developments in advanced signal processing algorithms for non-invasive fECG extraction and the available open access resources (databases and source code). In particular, we highlight the advantages and limitations of these algorithms as well as key parameters that must be set to ensure their optimal performance. Improving or combining the current or developing new advanced signal processing methods may enable morphological analysis of the fetal electrocardiogram, which today is only possible using the invasive scalp electrocardiography method.

Entities:  

Year:  2019        PMID: 31478873     DOI: 10.1109/RBME.2019.2938061

Source DB:  PubMed          Journal:  IEEE Rev Biomed Eng        ISSN: 1937-3333


  7 in total

1.  A Deep Learning Approach for the Assessment of Signal Quality of Non-Invasive Foetal Electrocardiography.

Authors:  Gert Mertes; Yuan Long; Zhangdaihong Liu; Yuhui Li; Yang Yang; David A Clifton
Journal:  Sensors (Basel)       Date:  2022-04-26       Impact factor: 3.847

2.  An Efficient and Robust Deep Learning Method with 1-D Octave Convolution to Extract Fetal Electrocardiogram.

Authors:  Khuong Vo; Tai Le; Amir M Rahmani; Nikil Dutt; Hung Cao
Journal:  Sensors (Basel)       Date:  2020-07-04       Impact factor: 3.576

3.  A non-invasive multimodal foetal ECG-Doppler dataset for antenatal cardiology research.

Authors:  Eleonora Sulas; Monica Urru; Roberto Tumbarello; Luigi Raffo; Reza Sameni; Danilo Pani
Journal:  Sci Data       Date:  2021-01-26       Impact factor: 6.444

Review 4.  Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review.

Authors:  Umer Saeed; Syed Yaseen Shah; Jawad Ahmad; Muhammad Ali Imran; Qammer H Abbasi; Syed Aziz Shah
Journal:  J Pharm Anal       Date:  2022-01-04

5.  Optimization of adaptive filter control parameters for non-invasive fetal electrocardiogram extraction.

Authors:  Radana Kahankova; Martina Mikolasova; Radek Martinek
Journal:  PLoS One       Date:  2022-04-11       Impact factor: 3.240

Review 6.  A review of fetal cardiac monitoring, with a focus on low- and middle-income countries.

Authors:  Camilo E Valderrama; Nasim Ketabi; Faezeh Marzbanrad; Peter Rohloff; Gari D Clifford
Journal:  Physiol Meas       Date:  2020-12-18       Impact factor: 2.688

7.  A novel algorithm based on ensemble empirical mode decomposition for non-invasive fetal ECG extraction.

Authors:  Katerina Barnova; Radek Martinek; Rene Jaros; Radana Kahankova; Adam Matonia; Michal Jezewski; Robert Czabanski; Krzysztof Horoba; Janusz Jezewski
Journal:  PLoS One       Date:  2021-08-13       Impact factor: 3.240

  7 in total

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