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Identification of Uterine Contractions by An Ensemble of Gaussian Processes

Citation Author(s):
Cassandra Heiselman, J. Gerald Quirk, Petar M. Djurić
Submitted by:
Liu Yang
Last updated:
25 June 2021 - 4:29pm
Document Type:
Presentation Slides
Document Year:
2021
Event:
Presenters Name:
Liu Yang
Paper Code:
BIO-2.5

Abstract 

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.

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Dataset Files

UC identification.pdf

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