Automated uterine contractions pattern detection framework to monitor pregnant women with a high risk of premature labour

• Main Authors: Hisham Allahem, Srinivas Sampalli
• Format: Article
• Language: English
• Published: Elsevier 2020-01-01
• Series: Informatics in Medicine Unlocked
• Subjects: Wireless body sensor networks; Pregnancy; Uterine contractions; Labour pattern; Premature birth; Electrohysterography
• Online Access: http://www.sciencedirect.com/science/article/pii/S2352914820305542

More than one in ten babies are born prematurely worldwide, resulting in nearly one million deaths each year. Furthermore, surviving babies face lifelong health-related disabilities, such as difficulties in learning or hearing and vision loss. Monitoring uterine contractions can evaluate the health and progress of the pregnancy. This monitoring can help in determining if the pregnant woman is in labour, thus assisting them to go to the hospital, which will help in reducing premature birth issues. In this paper, we aim to mitigate the consequences of premature birth for both the pregnant woman and the fetus by proposing a safe, simple, home-comfortable, low-cost, and reliable monitoring framework. The system uses a non-invasive method to monitor the uterine electrohysterography (EHG) contractions using a wireless body sensor (WBS) and a smartphone. The smartphone will analyze uterine EHG contractions readings, and if they contain a premature labour pattern, a warning notification will be triggered. A proof-of-concept prototype of the smartphone application was designed and tested for reliability, performance and power consumption using three uterine contractions databases. The results show that the application was able to meet the framework objectives in detecting the labour pattern.