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

Fecha de publicación: 01/09/2024 Fecha Ahead of Print: 05/07/2024

Autores de IIS La Fe

• Isauro Rogelio Monfort Ortiz

  Autor

• Vicente Jose Diago Almela

  Autor

• Gemma Prats Boluda

  Autor

Participantes ajenos a IIS La Fe

• Nieto-del-Amor, Felix
• Ye-Lin, Yiyao
• Modrego-Pardo, Fernando
• Martinez-de-Juan, Jose L.
• Hao, Dongmei

Grupos

• Medicina Reproductiva

  

  Investigador Principal

  José Morales Roselló

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. METHODS: For this, we combined 300 EHG recordings from the TPEHG DS database and 69 EHG recordings from our own database (Ci2B-La Fe) of women with singleton gestations. This dataset was used to train and evaluate U-Net, U-Net++, and U-Net 3+ for semantic segmentation of the physiological and artifacted segments of EHG signals. The model's predictions were then fine-tuned by post-processing. RESULTS: U-Net 3+ outperformed the other models, achieving an area under the ROC curve of 91.4 % and an average precision of 96.4 % in detecting physiological activity. Thresholds from 0.6 to 0.8 achieved precision from 93.7 % to 97.4 % and specificity from 81.7 % to 94.5 %, detecting high-quality physiological segments while maintaining a trade-off between recall and specificity. Post-processing improved the model's adaptability by fine-tuning both the physiological and corrupted segments, ensuring accurate artifact detection while maintaining physiological segment integrity in EHG signals. CONCLUSIONS: As automatic segmentation proved to be as effective as double-blind manual segmentation in predicting preterm labor, this automatic segmentation tool fills a crucial gap in the existing preterm delivery prediction system workflow by eliminating the need for double-blind segmentation by experts and facilitates the practical clinical use of EHG. This work potentially contributes to the early detection of authentic preterm labor women and will allow clinicians to design individual patient strategies for maternal health surveillance systems and predict adverse pregnancy outcomes.

Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.

Keywords

• Deep learning; Electrohysterography; Preterm delivery prediction; Semantic segmentation; Signal processing; Uterine myoelectric activity