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Pre-term Birth Prediction at Home: Signal Filtering Influence on the Good Prediction Rate

  • Alessandro GalassiEmail author
  • Charles Muszynski
  • Vincent Zalc
  • Dan Istrate
  • Catherine Marque
Conference paper
  • 31 Downloads
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 11)

Abstract

In this paper, we propose an automatic prediction system allowing to predict high probability of birth within 1–2 weeks from the EHG measurement [1, 2, 3]. Despite continuous clinical routine improvements, the preterm rate remains steady. With the aim of avoiding long hospitalization for pregnant women we propose an embedded system which acquires and processes EHG signals. We have already proposed a detection and recognition system for intrauterine contractions using directly the EHG signal obtained from a matrix of 16 electrodes. Since the measurements must be made at home, the decrease of computation power is an important constraint. In this work, we compare the results of the preterm birth prediction algorithm using a filtering step and with only the raw signals. The filtering step is applied directly on the raw signals or only on the automatically detected contractions to reduce computation time. We have applied in this, different filtering methods as denoising step to analyse their influence on the global classification performances. Two types of filtering are evaluated separately or combined: Canonical Correlation Analysis (CCA) and Empirical Mode Decomposition (EMD). The EMD decomposes a signal into a collection of oscillatory modes, called IMFs, which represent fast to slow oscillations in the signal. The CCA is a Blind Source Separation (BSS) method which assumes that the observed multichannel signals reflect a linear combination of several sources which are associated to underlying physiological processes, artefacts, and noise. The global classification results are compared between filtered and not filtered signals.

Notes

Acknowledgments

This work was founded by the Safepregnancy@home Eurostar project (E!10608) and also by BMBI UMR7338 laboratory. We would like to thank Jean Baptiste Tylcz for his help.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Alessandro Galassi
    • 1
    • 2
    Email author
  • Charles Muszynski
    • 3
  • Vincent Zalc
    • 1
  • Dan Istrate
    • 1
  • Catherine Marque
    • 1
  1. 1.Université de Technologie de Compiègne, Laboratoire BMBI UMR 7338CompiègneFrance
  2. 2.Faculty of Ingegneria Civile e IndustrialeLa Sapienza, University of RomeRomeItaly
  3. 3.Service de gynécologie et obstétriqueCHU Amiens-PicardieSalouelFrance

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