Automatic semantic segmentation of EHG recordings by deep learning: An approach to a screening tool for use in clinical practice.

Journal: Computer methods and programs in biomedicine
Published Date: Jul 5, 2024

Abstract

BACKGROUND AND OBJECTIVE: Preterm delivery is an important factor in the disease burden of the newborn and infants worldwide. Electrohysterography (EHG) has become a promising technique for predicting this condition, thanks to its high degree of sensitivity. Despite the technological progress made in predicting preterm labor, its use in clinical practice is still limited, one of the main barriers being the lack of tools for automatic signal processing without expert supervision, i.e. automatic screening of motion and respiratory artifacts in EHG records. Our main objective was thus to design and validate an automatic system of segmenting and screening the physiological segments of uterine origin in EHG records for robust characterization of uterine myoelectric activity, predicting preterm labor and help to promote the transferability of the EHG technique to clinical practice.

Authors

  • Félix Nieto-Del-Amor
    Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València (Ci2B), Valencia 46022, Spain.
  • Yiyao Ye-Lin
    Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València (Ci2B), Valencia 46022, Spain; BJUT-UPV Joint Research Laboratory in Biomedical Engineering, China.
  • Rogelio Monfort-Ortiz
    Servicio de Obstetricia, H.U.P. La Fe, Valencia 46026, Spain.
  • Vicente Jose Diago-Almela
    Servicio de Obstetricia, H.U.P. La Fe, Valencia 46026, Spain.
  • Fernando Modrego-Pardo
    Servicio de Obstetricia, H.U.P. La Fe, Valencia 46026, Spain.
  • Jose L Martinez-de-Juan
    Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València (Ci2B), Valencia 46022, Spain; BJUT-UPV Joint Research Laboratory in Biomedical Engineering, China.
  • Dongmei Hao
    College of Life Science and Bioengineering, Beijing University of Technology, Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing, 100024, China.
  • Gema Prats-Boluda
    Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València (Ci2B), Valencia 46022, Spain; BJUT-UPV Joint Research Laboratory in Biomedical Engineering, China. Electronic address: gprats@ci2b.upv.es.

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