@article{678591efc65843aa8541ae07b1edd199,
title = "Automated labour detection framework to monitor pregnant women with a high risk of premature labour using machine learning and deep learning",
author = "Hisham Allahem and Srinivas Sampalli",
note = "Funding Information: The first author gratefully acknowledges the support of Jouf University, Saudi Arabia for funding his Ph.D. programme, which made the study reported in this paper possible. The authors also thank Dr. Jaume Manero, from Universitat Polit{\`e}cnica de Catalunya, Spain; Dr. Ebtehal Hussein Aljumaai, Obstetric and Gynaecology Registrar, Abha Maternity Children{\textquoteright}s Hospital, Saudi Arabia; Dr. Mohamed Elsheikh, obstetrician and foetal medicine consultant, National Guard Hospital, MRCOG, SSCOG, DIP Prenatal Genetics, Riyadh, Saudi Arabia; and Darshana Upadhyay, MCS, Faculty of Computer Science, Dalhousie University, Canada, for their help and support. Funding Information: The first author gratefully acknowledges the support of Jouf University, Saudi Arabia for funding his Ph.D. programme, which made the study reported in this paper possible. The authors also thank Dr. Jaume Manero, from Universitat Polit{\`e}cnica de Catalunya, Spain; Dr. Ebtehal Hussein Aljumaai, Obstetric and Gynaecology Registrar, Abha Maternity Children's Hospital, Saudi Arabia; Dr. Mohamed Elsheikh, obstetrician and foetal medicine consultant, National Guard Hospital, MRCOG, SSCOG, DIP Prenatal Genetics, Riyadh, Saudi Arabia; and Darshana Upadhyay, MCS, Faculty of Computer Science, Dalhousie University, Canada, for their help and support. Publisher Copyright: {\textcopyright} 2021 The Authors",
year = "2022",
month = jan,
doi = "10.1016/j.imu.2021.100771",
language = "English",
volume = "28",
journal = "Informatics in Medicine Unlocked",
issn = "2352-9148",
publisher = "Elsevier Ltd.",
}