N-Beats as an EHG Signal Forecasting Method for Labour Prediction in Full Term Pregnancy

Abstract

The early prediction of onset labour is critical for avoiding the risk of death due to pregnancy delay. Low-income countries often struggle to deliver timely service to pregnant women due to a lack of infrastructure and healthcare facilities, resulting in pregnancy complications and, eventually, death. In this regard, several artificial-intelligence-based methods have been proposed based on the detection of contractions using electrohysterogram (EHG) signals. However, the forecasting of pregnancy contractions based on real-time EHG signals is a challenging task. This study proposes a novel model based on neural basis expansion analysis for interpretable time series (N-BEATS) which predicts labour based on EHG forecasting and contraction classification over a given time horizon. The publicly available TPEHG database of Physiobank was exploited in order to train and test the model, where signals from full-term pregnant women and signals recorded after 26 weeks of gestation were collected. For these signals, the 30 most commonly used classification parameters in the literature were calculated, and principal component analysis (PCA) was utilized to select the 15 most representative parameters (all the domains combined). The results show that neural basis expansion analysis for interpretable time series (N-BEATS) forecasting can forecast EHG signals through training after few iterations. Similarly, the forecasting signal’s duration is determined by the length of the recordings. We then deployed XG-Boost, which achieved the classification accuracy of 99 percent, outperforming the state-of-the-art approaches using a number of classification features greater than or equal to 15.

A deep learning hierarchical approach to road traffic forecasting

“…The authors reported that N‐BEATS outperformed the M4 forecast competition winner by 3%. Recently, the architecture has gained a number of desirable properties, including being interpretable, applicable without modification to a wide array of target domains (Jossou et al, 2022; Puszkarski et al, 2021; Sbrana et al, 2020; Siddardha et al, 2021), and fast to train.…”

An open dataset with electrohysterogram records of pregnancies ending in induced and cesarean section delivery

“…In 2021, an important study 26 revealed that over-sampling applied after data partitioning, i.e., partition-synthesis over-sampling approach, needs to be applied to achieve realistic classification performance, and realistic preterm birth prediction in the case of imbalanced sets. Recently, many interesting studies related to preterm birth prediction using the TPEHG DB were published using traditional feature engineering 27 – 33 and deep learning 34 – 37 approaches. A nice review of the literature dealing with the use of EHG records for the task of predicting premature birth and for understanding the underlying physiological processes during pregnancy can be found in 38 .…”