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MSEU-Net: A Multi-Scale Deep Learning Framework for Precise FHR Baseline Determination

Published: 02 July 2024 Publication History

Abstract

In Fetal Heart Rate (FHR) analysis for intrauterine growth restriction (IUGR), accurate baseline determination is essential for effective monitoring and intervention. MSEU-Net, a multi-scale deep learning framework, significantly advances this effort by offering enhanced accuracy in baseline calculations, leveraging convolution blocks and a unique multi-scale extraction module. This innovative approach promises to improve prenatal care by enabling more accurate assessments of fetal well-being.

References

[1]
Samuel Boudet, Agathe Houzé de l'Aulnoit, Romain Demailly, et al. 2019. Fetal heart rate baseline computation with a weighted median filter. Computers in Biology and Medicine 114 (2019), 103468.
[2]
Agathe Houzé de l'Aulnoit, Samuel Boudet, Romain Demailly, et al. 2019. Automated fetal heart rate analysis for baseline determination and acceleration/deceleration detection: A comparison of 11 methods versus expert consensus. Biomedical Signal Processing and Control 49 (2019), 113--123.
[3]
Yan-Ju Jia, Xu Chen, Hong-Yan Cui, et al. 2021. Physiological CTG interpretation: the significance of baseline fetal heart rate changes after the onset of decelerations and associated perinatal outcomes. The Journal of Maternal-Fetal & Neonatal Medicine 34, 14 (2021), 2349--2354.
[4]
Yu Lu, Xi Zhang, Liwen Jing, et al. 2020. Estimation of the foetal heart rate baseline based on singular spectrum analysis and empirical mode decomposition. Future Generation Computer Systems 112 (2020), 126--135.

Cited By

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  • (2024)AI-driven paradigm shift in computerized cardiotocography analysis: A systematic review and promising directionsNeurocomputing10.1016/j.neucom.2024.128446607(128446)Online publication date: Nov-2024

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  1. MSEU-Net: A Multi-Scale Deep Learning Framework for Precise FHR Baseline Determination

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      cover image ACM Conferences
      CF '24: Proceedings of the 21st ACM International Conference on Computing Frontiers
      May 2024
      345 pages
      ISBN:9798400705977
      DOI:10.1145/3649153
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      New York, NY, United States

      Publication History

      Published: 02 July 2024

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      Author Tags

      1. FHR
      2. IUGR
      3. baseline
      4. deep learning

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      • Refereed limited

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      • Department of Education of Guangdong Province, China

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      CF '24 Paper Acceptance Rate 33 of 105 submissions, 31%;
      Overall Acceptance Rate 273 of 785 submissions, 35%

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      Cited By

      View all
      • (2024)AI-driven paradigm shift in computerized cardiotocography analysis: A systematic review and promising directionsNeurocomputing10.1016/j.neucom.2024.128446607(128446)Online publication date: Nov-2024

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