Combining Electrohysterography and Heart Rate Data to Detect Labour

Marco Altini1, Elisa Rossetti2, Michael Johannes Rooijakkers, Julien Penders2, Dorien Lanssens3, Lars Grieten, Wilfried Gyselaers3

  • 1Bloom Technologies, USA - ACTLab, University of Passau, DE
  • 2Bloom Technologies
  • 3ZoL

Details

09:05 - 09:55 | Fri 17 Feb | Ballroom D | FrRAF.21

Session: Rapid Fire Session 03: Sensor Informatics II

Abstract

In this paper we propose a method combining electrohysterography (EHG) and heart rate (HR) data to detect labour. Given specific changes in physiological data such as EHG and HR highlighted from previous literature in correspondence of uterine contractions, we sought to create a model able to classify between labour and non-labour recordings based on EHG and maternal HR data. In particular, we collected 37 recordings (19 labour and 18 non-labour) from pregnant women at different stages of pregnancy using a wearable sensor designed to be attached to the abdomen using an adhesive patch. We extracted time and frequency domain features from EHG and HR data, as stronger, sinusoidal patterns arise on both data streams in correspondence with uterine contractions during labour. The accuracy of the proposed model in classifying labour and non-labour recordings was evaluated using leave one out cross validation. We analyzed results including as predictors; gestational age (GA) only, as reference lower bound (68% accuracy), EHG features only (71% accuracy), HR features only (71% accuracy) and combined EHG and HR data, resulting in 82% accuracy. Inclusion of GA as additional predictor further increased detection accuracy to 79%, 82% and 87% for EHG, HR and combined EHG and HR respectively. Our labour detection model demonstrated a high accuracy in classifying labour and non-labour recordings using EHG and HR data collected using a single wearable device.