02 / 2021 – 08 / 2021
Nearly one third of all birth defects are congenital heart conditions. The global prevalence at birth is estimated at almost 1.8 cases per 100 live births. In countries with a high Socio-Demographic Index (SDI), congenital heart defects are among the top five causes of infant mortality, and among the top eight causes in all countries. Therefore, an early evaluation of the fetal heart is essential for identifying fetuses at risk and provide early care [3, 7]. In current clinical practice, the cardiac health status of the fetus is examined using cardiotocography (CTG). This technology uses an ultrasound transducer attached to the maternal abdomen to measure fetal heart rate. Simultaneously, uterine activity is captured by a pressure-sensitive transducer. However, this technology has certain limitations.On one hand, current devices can only estimate the time-averaged fetal heart rate due to the usage of window functions. On the other hand, CTG must be used by medical professionals since an accurate placement of the ultrasound sensor is essential to obtain a high signal quality [2, 5].
In contrast, non-invasive fetal electrocardiography (NI-fECG) is a measurement technique that has the potential to address these challenges. This method records the electrophysiological signal on the skin surface of the maternal abdomen using multiple electrodes. The measured signal is composed of the fetal ECG, the maternal ECG and other interfering signals such as muscular or motion artifacts. Signal processing methods can then be used to extract the fetal heart rate from the signal. In this way it is possible to achieve a beat-to-beat measurement of the fetal heart rate, which provides additional diagnostic information about heart rate variability [4, 6]. Furthermore, it is more feasible to integrate NI-fECG measurement technology into wearable devices to enable a home-monitoring outside of the clinical environment . This facilitates pregnant women’s access to prenatal care, especially in rural areas, as no professional staff is needed to operate the device . One reason why NI-fECG is not yet established in clinical practice is the lack of a large database with uniformly recorded signals on which new algorithms can be tested and compared. Recently, however, several open-source datasets have been published that include recordings with different electrode placements and fetal R-peak annotations from medical experts for reference . Multiple signal processing algorithms have already been developed for fetal heart rate extraction from NI-fECG. They can be divided into algorithms that consider only abdominal electrodes and algorithms that require an additional electrode on the mother’s chest to record maternal ECG. When the maternal ECG is available, it is assumed that components of the maternal ECG are present in both chest and abdominal electrodes. Thus, the maternal ECG can be removed from the abdominal signals using adaptive techniques, leaving the fetal ECG. If only abdominal electrodes are available, the maternalECGis extracted from the abdominal signals using either a template in the time domain, or blind source separation techniques that operate in the spatial domain. Literature suggests that a combination of different algorithms, so-called hybrid methods, may provide the best results .
However, to date, no objective comparison between existing fetal heart rate extraction algorithms has been conducted. Therefore, there is a need to benchmark the existing algorithms on the various publicly available datasets. The aim of this thesis is to develop a new hybrid algorithm for fetal heart rate extraction from NI-fECG records, which should especially focus on signals measured with wearable devices. For this purpose, the existing algorithms are compared and their strengths and weaknesses are identified in order to achieve a suitable combination of the methods. The resulting approach will be tested on recordings from existing open-access fECG databases as well as on an additional dataset that will be acquired within a clinical study using a wearable device. In a final step, the developed algorithm is benchmarked against state-of-the-art approaches.
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