Detection of preterm birth in electrohysterogram signals based on wavelet transform and stacked sparse autoencoder-Reference-Cited by-同舟云学术

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Detection of preterm birth in electrohysterogram signals based on wavelet transform and stacked sparse autoencoder

Published:2019-04-16 Issue:4 Volume:14 Page:e0214712
ISSN:1932-6203
Container-title:PLOS ONE
language:en
Short-container-title:PLoS ONE

Author:

Chen Lili,Hao Yaru,Hu Xue^ORCID

Funder

the Foundation and Frontier Research Project of Chongqing

the Science and Technology Research Project of Chongqing Municipal Education Committee

the Technology Innovation Project of Social Undertakings and Livelihood Security of Chongqing

the Postgraduate Science and Technology Innovation Project of Chongqing

Publisher

Public Library of Science (PLoS)

Subject

Multidisciplinary

Reference34 articles.

1. Born Too Soon: The Global Action Report on Preterm Birth;C Howson,2012

2. Identification of Human Term and Preterm Labor using Artificial Neural Networks on Uterine Electromyography Data;WL Maner;Ann Biomed Eng,2007

3. Transvaginal sonographic measurement of cervical length to predict preterm birth in asymptomatic women at increased risk: a systematic review;JMG Crane;Ultrasound in Obstetrics & Gynecology,2008

4. Maternal serum progesterone-induced blocking factor (PIBF) in the prediction of preterm birth;I Hudić;J Reprod Immunol,2015

5. Physiology and electrical activity of uterine contractions;RE Garfield;Semin Cell Dev Biol,2007

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1. Automatic semantic segmentation of EHG recordings by deep learning: An approach to a screening tool for use in clinical practice;Computer Methods and Programs in Biomedicine;2024-09

2. Preterm birth risk stratification through longitudinal heart rate and HRV monitoring in daily life;Scientific Reports;2024-08-27

3. An open dataset with electrohysterogram records of pregnancies ending in induced and cesarean section delivery;Scientific Data;2023-10-02

4. An automatic classification approach for preterm delivery detection based on deep learning;Biomedical Signal Processing and Control;2023-07

5. Enhancing classification of preterm-term birth using continuous wavelet transform and entropy-based methods of electrohysterogram signals;Frontiers in Endocrinology;2023-01-10

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