11/2019 – 05/2020
More than 10% of all births in the world are premature, i.e. they occur before the end of 37 weeks of gestation . Preterm birth complications are the leading cause of death among children under 5 years . According to the WHO many survivors face a lifetime of handicap, including learning disabilities, visual and hearing problems . Most preterm deliveries happen spontaneously due to an early onset of labor caused by either multiple pregnancies or chronic conditions, but often no specific cause can be identified . Even though the total number of incidences slightly decreased over the last years, the main decrease was among late preterm deliveries .
To successfully treat and prevent premature birth detecting labor as soon as possible is important . Labor is the physiological process of delivering a baby. It is defined as an increase in duration and regularity of uterine contractions resulting in effacement and dilation of the cervix . The intrauterine pressure catheter (IUPc) is the gold standard for monitoring uterine activity. The main problem of the IUPc is its invasiveness, which requires a rupture of the membranes and can therefore only be used during delivery, at the risk of damaging maternal or fetal tissues and infection . The non-invasive tocodynamometer (TOCO) is standard in everyday clinical practice but less reliable due to the indirect way of measurement. Recently, uterine electromyography (EMG) has shown to be a more accurate way of monitoring uterine contractility compared to the TOCO . Nevertheless, the TOCO is still the de facto standard in clinical practice. None of these medical devices currently distinguishes between actual labor contractions and pregnancy contractions or performs decision making, which is only done in academic studies so far.
Academic studies differ mostly in the type and number of electrodes and in the applied features and methods. Very promising results have been achieved, e.g. using artificial neural networks processing on uterine EMG burst features as an automatic categorization . However, the promising results found in literature are limited to data collected in controlled laboratory conditions and using a single recording per subject, and they haven’t been shown to extend to free living conditions. Lack of technology suitable for high-quality recordings in free-living situations has limited research in this field, but recent innovations in the field of wearable technology could allow overcoming previous limitations. The Bloomlife sensor is a wearable device consisting of an electronic sensor module and a disposable adhesive patch with 3 sensing electrodes, which measures electrophysiological signals detected from the pregnant women’s abdomen. Its ease of use and convenience make it an ideal tool to continuously acquire data throughout pregnancy and labor in a natural environment.
The goal of this master thesis is therefore to develop a labor detection approach using machine learning techniques and a subset of data acquired with the Bloomlife sensor. The data used in this project was collected in free-living conditions by more than 10,000 women (Bloomlife customers) during the last weeks of their pregnancy. In addition, characteristics that evolve during pregnancy and might indicate the preparation of the maternal body for delivery are to be investigated.
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