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Mathematical Biosciences and Engineering

2021, Volume 18, Issue 4: 4919-4942. doi: 10.3934/mbe.2021250
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Fetal ECG signal processing and identification of hypoxic pregnancy conditions in-utero

  • 1.
    Department of Computer Science and Engineering, Volodymyr Dahl East Ukrainian National University, 43 Donetska Street, Severodonetsk 93400, Ukraine
  • 2.
    Faculty of Electrical and Computer Engineering, Cracow University of Technology, Warszawska 24 Street, Cracow 31155, Poland
  • 3.
    School of Engineering, Computing and Mathematics, Oxford Brookes University, Wheatley Campus, Oxford, OX33 1HX, UK
  • 4.
    Department of Informatics, Kazimierz Pulaski University of Technology and Humanities in Radom, Radom 26600, Poland
  • 5.
    Research Institute for Intelligent Computer Systems, West Ukrainian National University, Ternopil 46009, Ukraine
  • 6.
    Department of Computer Engineering and Programming, National Technical University “Kharkiv Polytechnic Institute,” 2 Kyrpychova Street, Kharkiv 61002, Ukraine
  • 7.
    School of Medicine, V. N. Karazin Kharkiv National University, 4 Svobody Square, Kharkiv 61002, Ukraine
  • Received: 07 February 2021 Accepted: 12 May 2021 Published: 04 June 2021

  • The fetal heart rate (fHR) variability and fetal electrocardiogram (fECG) are considered the most important sources of information about fetal wellbeing. Non-invasive fetal monitoring and analysis of fECG are paramount for clinical trials. They enable examining the fetal health status and detecting the heart rate changes associated with insufficient oxygenation to cut the likelihood of hypoxic fetal injury. Despite the fact that significant advances have been achieved in electrocardiography and adult ECG signal processing, the analysis of fECG is still in its infancy. Due to accurate fetal morphology extraction techniques have not been properly developed, many areas require particular attention on the way of fully understanding the changes in variability in the fetus and implementation of the non-invasive techniques suitable for remote home care which is increasingly in demand for high-risk pregnancy monitoring. In this paper, we introduce an integrated approach for fECG signal extraction and processing based on various methods for fetal welfare investigation and hypoxia risk estimation. To the best of our knowledge, this is the first attempt to introduce the auto-generated risk scoring in fECG to achieve early warning on fetus' safety and provide the physician with additional information about the possible fetal complications. The proposed method includes the following stages: fECG extraction, fHR and fetal heart rate variability (fHRV) calculation, hypoxia index (HI) evaluation and risk estimation. The extracted signals were examined by assessing Signal to Noise Ratio (SNR) and mean square error (MSE) values. The results obtained demonstrated great potential, but more profound research and validation, as well as a consistent clinical study, are needed before implementation into the hospital and at-home monitoring.

    Citation: Tetiana Biloborodova, Lukasz Scislo, Inna Skarga-Bandurova, Anatoliy Sachenko, Agnieszka Molga, Oksana Povoroznyuk, Yelyzaveta Yevsieieva. Fetal ECG signal processing and identification of hypoxic pregnancy conditions in-utero[J]. Mathematical Biosciences and Engineering, 2021, 18(4): 4919-4942. doi: 10.3934/mbe.2021250

    Related Papers:

  • The fetal heart rate (fHR) variability and fetal electrocardiogram (fECG) are considered the most important sources of information about fetal wellbeing. Non-invasive fetal monitoring and analysis of fECG are paramount for clinical trials. They enable examining the fetal health status and detecting the heart rate changes associated with insufficient oxygenation to cut the likelihood of hypoxic fetal injury. Despite the fact that significant advances have been achieved in electrocardiography and adult ECG signal processing, the analysis of fECG is still in its infancy. Due to accurate fetal morphology extraction techniques have not been properly developed, many areas require particular attention on the way of fully understanding the changes in variability in the fetus and implementation of the non-invasive techniques suitable for remote home care which is increasingly in demand for high-risk pregnancy monitoring. In this paper, we introduce an integrated approach for fECG signal extraction and processing based on various methods for fetal welfare investigation and hypoxia risk estimation. To the best of our knowledge, this is the first attempt to introduce the auto-generated risk scoring in fECG to achieve early warning on fetus' safety and provide the physician with additional information about the possible fetal complications. The proposed method includes the following stages: fECG extraction, fHR and fetal heart rate variability (fHRV) calculation, hypoxia index (HI) evaluation and risk estimation. The extracted signals were examined by assessing Signal to Noise Ratio (SNR) and mean square error (MSE) values. The results obtained demonstrated great potential, but more profound research and validation, as well as a consistent clinical study, are needed before implementation into the hospital and at-home monitoring.


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