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Multivariate Time–Frequency Analysis of Electrohysterogram for Classification of Term and Preterm Labor

  • Jiwoo You
  • Youngjoo Kim
  • Woojoon Seok
  • Seungmin Lee
  • Donggyu Sim
  • Kwang Suk Park
  • Cheolsoo ParkEmail author
Original Article

Abstract

Non-invasive electrohysterogram (EHG) could be a promising technique for the preterm birth prediction, which could enable us to diagnose the preterm birth before the labor and reduces the infant mortality and morbidity. Previous studies on the preterm birth prediction with EHG have conducted comprehensive researches on various signal features and classification algorithms, but most of them adopted prefilters based on the linear transforms using fixed basis function, although they are suboptimal for the nonlinearity and nonstationarity of the EHG signal. In this paper, multivariate empirical mode decomposition (MEMD) is applied to decompose the electrical activity signal measured on the uterus. After the decomposition, features are calculated for the corresponding oscillations to the uterine contraction. To investigate the performance of the features, three-channel EHG signals of 254 patients (224 term, 30 preterm) are chosen among 300 patients from Physionet term-preterm electrohysterogram (TPEHG) database to extract features from the EHG signals and classify the features using machine learning algorithms. Classification results shows that the proposed method with MEMD achieved 94.66% correctly classified rate (CCR) and 0.987 area under the curve (AUC), which outperformed those with IIR filter implying MEMD provides a new prospect to improve the current preterm birth prediction approach.

Keywords

Preterm birth detection EHG Uterine EMG Machine learning classification NA-MEMD 

Notes

Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2017R1A5A1015596), the Ministry of Education (NRF-2017R1D1A1B03031485) and the Research Grant of Kwangwoon University in 2019.

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Copyright information

© The Korean Institute of Electrical Engineers 2019

Authors and Affiliations

  • Jiwoo You
    • 1
    • 2
  • Youngjoo Kim
    • 1
    • 2
  • Woojoon Seok
    • 1
  • Seungmin Lee
    • 3
  • Donggyu Sim
    • 1
  • Kwang Suk Park
    • 4
  • Cheolsoo Park
    • 1
    Email author
  1. 1.Department of Computer EngineeringKwangwoon UniversitySeoulKorea
  2. 2.Faculty of Science and EngineeringRijksuniversiteit GroningenGroningenThe Netherlands
  3. 3.School of Electrical Engineering, College of Creative EngineeringKookmin UniversitySeoulKorea
  4. 4.Department of Biomedical Engineering, College of MedicineSeoul National UniversitySeoulKorea

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