Abstract Machine learning technologies and translation of artificial intelligence tools to
enhance the patient experience are changing obstetric and maternity care. An increasing …

The existing non-invasive automated preterm birth prediction methods rely on the use of
uterine electrohysterogram (EHG) records coming from spontaneous preterm and term …

Uterine contractions are routinely monitored by tocodynamometer (TOCO) at late stage of
pregnancy to predict the onset of labor. However, TOCO reveals no information on the …

The recent advancements in sensor, big data, and artificial intelligence (AI) have introduced
digital transformation in the manufacturing industry. Machine maintenance has been one of …

Preterm birth is the leading cause of perinatal morbidity and mortality. In clinical practice, the
information of uterine contraction is an important reference for preterm delivery. The …

Timely preterm labor prediction plays an important role for increasing the chance of neonate
survival, the mother's mental health, and reducing financial burdens imposed on the family …

Abstract Background and Objective: Preterm delivery is an important factor in the disease
burden of the newborn and infants worldwide. Electrohysterography (EHG) has become a …

We aimed to develop, train, and validate machine learning models for predicting preterm
birth (< 37 weeks' gestation) in singleton pregnancies at different gestational intervals …

Electrohysterography has been shown to provide relevant information on preventing preterm
labor. Recent studies have confirmed the feasibility of using the vectormyometriogram …

Recently, tocography (TOCO) and electrohysterogram (EHG) signals are real-time and non-
invasive technology that has been applied to detect preterm delivery. This paper proposes a …