Non-Invasive Fetal Monitoring using Non-Contact Electrodes and Recurrent Neural Networks
Examensarbete för masterexamen
The fetal well-being is routinely monitored using a cardiotocograph (CTG), a combination of a Doppler sensor used to measure the fetal heart beats and a pressure sensor to measure uterine muscle contractions. However, the equipment is expensive and there is a recurrent issue where the CTG confuses the maternal heart rate for the fetal heart rate, leading to ambiguities that have resulted in adverese fetal outcomes on multiple occasions. Non-invasive recordings of the fetal heart activity on the maternal abdomen could constitute a viable alternative to Doppler ultrasound recording. However, the potential sensed by an abdominal electrode is the combination of many sources, making it an onerous task to extract the components stemming from the fetal heart beats. The most difficult subsignal to circumvent is the contribution from the maternal heart. This thesis explores the viability of using non-contact electrodes to reliably measure the fetal electrocardiogram, fECG, and an electrode is successfully designed for this purpose. This electrode could be implemented in an array embedded in a piece of clothing for a practical implementation of many uncorrelated electrodes, which normally facilitates the signal separation process and improves its accuracy. Separately, using the Non-Invasive Fetal Electrocardiogram (NI-fECG) database, source separation and fetal heart rate extraction algorithms are evaluated, developed, and improved upon. It is shown that using said algorithms in combination with a novel fetal heart rate detector, fetal heart beats can be detected in a reliable manner on the evaluated data.