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Power-MF: robust fetal QRS detection from non-invasive fetal electrocardiogram recordings. Physiological measurement Perinatal asphyxia poses a significant risk to neonatal health, necessitating accurate fetal heart rate monitoring for effective detection and management. The current gold standard, cardiotocography, has inherent limitations, highlighting the need for alternative approaches. The emerging technology of non-invasive fetal electrocardiography shows promise as a new sensing technology for fetal cardiac activity, offering potential advancements in the detection and management of perinatal asphyxia. Although algorithms for fetal QRS detection have been developed in the past, only a few of them demonstrate accurate performance in the presence of noise and artifacts.In this work, we propose, a new algorithm for fetal QRS detection combining power spectral density and matched filter techniques. We benchmarkagainst three open-source algorithms on two recently published datasets (Abdominal and Direct Fetal ECG Database: ADFECG, subsets B1 Pregnancy and B2 Labour; Non-invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research: NInFEA).Our results show thatoutperforms state-of-the-art algorithms on ADFECG (B1 Pregnancy: 99.5% ± 0.5% F1-score, B2 Labour: 98.0% ± 3.0% F1-score) and on NInFEA in three of six electrode configurations by being more robust against noise.Through this work, we contribute to improving the accuracy and reliability of fetal cardiac monitoring, an essential step toward early detection of perinatal asphyxia with the long-term goal of reducing costs and making prenatal care more accessible. 10.1088/1361-6579/ad4952
Fetal electrocardiogram (fECG) gated MRI. Paley Martyn N J,Morris Janet E,Jarvis Debbie,Griffiths Paul D Sensors (Basel, Switzerland) We have developed a Magnetic Resonance Imaging (MRI)-compatible system to enable gating of a scanner to the heartbeat of a foetus for cardiac, umbilical cord flow and other possible imaging applications. We performed radiofrequency safety testing prior to a fetal electrocardiogram (fECG) gated imaging study in pregnant volunteers (n = 3). A compact monitoring device with advanced software capable of reliably detecting both the maternal electrocardiogram (mECG) and fECG simultaneously was modified by the manufacturer (Monica Healthcare, Nottingham, UK) to provide an external TTL trigger signal from the detected fECG which could be used to trigger a standard 1.5 T MR (GE Healthcare, Milwaukee, WI, USA) gating system with suitable attenuation. The MR scanner was tested by triggering rapidly during image acquisition at a typical fetal heart rate (123 beats per minute) using a simulated fECG waveform fed into the gating system. Gated MR images were also acquired from volunteers who were attending for a repeat fetal Central Nervous System (CNS) examination using an additional rapid cardiac imaging sequence triggered from the measured fECG. No adverse safety effects were encountered. This is the first time fECG gating has been used with MRI and opens up a range of new possibilities to study a developing foetus. 10.3390/s130911271
The standardized 12-lead fetal electrocardiogram of the healthy fetus in mid-pregnancy: A cross-sectional study. Lempersz Carlijn,van Laar Judith O,Clur Sally-Ann B,Verdurmen Kim M,Warmerdam Guy J,van der Post Joris,Blom Nico A,Delhaas Tammo,Oei S Guid,Vullings Rik PloS one INTRODUCTION:The examination of the fetal heart in mid-pregnancy is by ultrasound examination. The quality of the examination is highly dependent on the skill of the sonographer, fetal position and maternal body mass index. An additional tool that is less dependent on human experience and interpretation is desirable. The fetal electrocardiogram (ECG) could fulfill this purpose. We aimed to show the feasibility of recording a standardized fetal ECG in mid-pregnancy and explored its possibility to detect congenital heart disease (CHD). MATERIALS AND METHODS:Women older than 18 years of age with an uneventful pregnancy, carrying a healthy singleton fetus with a gestational age between 18 and 24 weeks were included. A fetal ECG was performed via electrodes on the maternal abdomen. After removal of interferences, a vectorcardiogram was constructed. Based on the ultrasound assessment of the fetal orientation, the vectorcardiogram was rotated to standardize for fetal orientation and converted into a 12-lead ECG. Median ECG waveforms for each lead were calculated. RESULTS:328 fetal ECGs were recorded. 281 were available for analysis. The calculated median ECG waveform showed the electrical heart axis oriented to the right and inferiorly i.e. a negative QRS deflection in lead I and a positive deflection in lead aVF. The two CHD cases show ECG abnormalities when compared to the mean ECG of the healthy cohort. DISCUSSION:We have presented a method for estimating a standardized 12-lead fetal ECG. In mid-pregnancy, the median electrical heart axis is right inferiorly oriented in healthy fetuses. Future research should focus on fetuses with congenital heart disease. 10.1371/journal.pone.0232606
Detection of maternal and fetal stress from the electrocardiogram with self-supervised representation learning. Scientific reports In the pregnant mother and her fetus, chronic prenatal stress results in entrainment of the fetal heartbeat by the maternal heartbeat, quantified by the fetal stress index (FSI). Deep learning (DL) is capable of pattern detection in complex medical data with high accuracy in noisy real-life environments, but little is known about DL's utility in non-invasive biometric monitoring during pregnancy. A recently established self-supervised learning (SSL) approach to DL provides emotional recognition from electrocardiogram (ECG). We hypothesized that SSL will identify chronically stressed mother-fetus dyads from the raw maternal abdominal electrocardiograms (aECG), containing fetal and maternal ECG. Chronically stressed mothers and controls matched at enrolment at 32 weeks of gestation were studied. We validated the chronic stress exposure by psychological inventory, maternal hair cortisol and FSI. We tested two variants of SSL architecture, one trained on the generic ECG features for emotional recognition obtained from public datasets and another transfer-learned on a subset of our data. Our DL models accurately detect the chronic stress exposure group (AUROC = 0.982 ± 0.002), the individual psychological stress score (R2 = 0.943 ± 0.009) and FSI at 34 weeks of gestation (R2 = 0.946 ± 0.013), as well as the maternal hair cortisol at birth reflecting chronic stress exposure (0.931 ± 0.006). The best performance was achieved with the DL model trained on the public dataset and using maternal ECG alone. The present DL approach provides a novel source of physiological insights into complex multi-modal relationships between different regulatory systems exposed to chronic stress. The final DL model can be deployed in low-cost regular ECG biosensors as a simple, ubiquitous early stress detection and monitoring tool during pregnancy. This discovery should enable early behavioral interventions. 10.1038/s41598-021-03376-8