Identification of Uterine Contractions by An Ensemble of Gaussian Processes

Liu Yang; Cassandra Heiselman; J. Gerald Quirk; Petar M. Djuric

ICASSP 2021accepted0 citations

Identification of Uterine Contractions by An Ensemble of Gaussian Processes

Liu Yang, Cassandra Heiselman, J. Gerald Quirk, Petar M. Djuric

Abstract

Identifying uterine contractions with the aid of machine learning methods is necessary vis-á-vis their use in combination with fetal heart rates and other clinical data for the assessment of a fetus wellbeing. In this paper, we study contraction identification by processing noisy signals due to uterine activities. We propose a complete four-step method where we address the imbalanced classification problem with an ensemble Gaussian process classifier, where the Gaussian process latent variable model is used as a decision-maker. The results of both simulation and real data show promising performance compared to existing methods.

BibTeX

@inproceedings{icassp2021_identificationof,
  title = {Identification of Uterine Contractions by An Ensemble of Gaussian Processes},
  author = {Liu Yang and Cassandra Heiselman and J. Gerald Quirk and Petar M. Djuric},
  booktitle = {ICASSP 2021},
  year = {2021}
}