If you don't remember your password, you can reset it by entering your email address and clicking the Reset Password button. You will then receive an email that contains a secure link for resetting your password
If the address matches a valid account an email will be sent to __email__ with instructions for resetting your password
Detection and management of complications such as preterm birth, could be improved by early labour detection. In our previous work, we showed that specific patterns in physiological data such as electrohysterography (EHG) and heart rate (HR) could be used to build predictive statistical models able to detect labour. In this work we highlight how physiological data can be discriminative of early term labour recordings when acquired both in laboratory and free-living conditions.
Accelerometry, EHG and HR data were collected under supervised laboratory conditions on 84 pregnant women using a wearable sensor designed to be attached to the abdomen using an adhesive patch as well as in free-living on 120 pregnant women. We extracted time and frequency domain features from EHG and HR data, as stronger, sinusoidal pattern arise on both data streams in correspondence of uterine contractions during labour. Features were used as input to a statistical model previously developed using recordings collected during labour at term and pregnancy as training set, to recognize labour and non-labour recordings. The statistical model was applied to preterm and early term recordings to assess the model's discriminative ability under those circumstances.
We report labour estimation probability for different conditions. Results showed that the probability of being in labour for recordings collected during the last 24 hours of pregnancy, when considering preterm or early term recordings, was consistently higher than the probability estimated for recordings collected outside of the last 24 hours. In particular, for recordings collected in supervised laboratory conditions, the mean probability of being in labour was 98% for actual preterm and early term recordings (combined dataset), while it was 0.1% for pregnancy recordings collected up to 24 hours before delivery. For free-living data, the mean probability of being in labour was 56% for actual early term recordings, while it was 27% for pregnancy recordings collected up to 24 hours before delivery.
Our labour detection models demonstrated the ability to discriminate between early term and preterm labour recordings and non-labour recordings, using a combination of EHG and HR data. Our findings seem to indicate that the physiology of labour is similar for preterm and early term recordings, with respect to term recordings, as EHG and HR patterns were found discriminative of the conditions.