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BACKGROUND: Electronic foetal monitoring (EFM), a method to monitor foetal intrauterine conditions and foetal reserve capacity, is the most extensively used intrauterine monitoring technology in obstetrics. OBJECTIVE: This study aims to compare the Thoth wearable foetal electrocardiogram (foetal ECG [FECG]) monitoring system with a traditional Doppler foetal heart monitoring system before labour to investigate their respective values in clinical application. METHODS: A total of 393 pregnant women admitted to our hospital between 2020 and 2022 participated in this study. They were recruited using the convenience sampling method. We employed a paired design to assess the confusion rate, trend overlap, and foetal heart rate/ECG monitoring consistency, whereas a completely randomised design was used to measure pregnancy outcome indicators. The participants were divided into two groups using a random number table: the Thoth group (n= 196) and the traditional Doppler group (n= 197). Each group was monitored using the corresponding system. RESULTS: The Thoth monitor demonstrated a lower confusion rate compared with the traditional Doppler monitor (0.25% vs 2.04%; χ2= 5.508, P= 0.019). The trend overlap in foetal heart rates was consistently 100%, with 91.2% of readings showing a consistency rate of ⩾ 95%. Additionally, the Thoth monitor recorded a higher cumulative interruption time in the foetal heart rate curve (12.13 ± 2.22 vs 21.02 ± 2.34; t= 18.471, P< 0.001) and more abnormal ECGs (21.21 ± 4.32 vs 18.21 ± 2.91; t= 7.582, P< 0.001) than the traditional Doppler system. CONCLUSION: The Thoth wearable FECG monitor offers several advantages over the traditional Doppler foetal heart monitoring system. These include a reduced confusion rate, more accurate data collection, a lower rate of clinical misjudgement, reduced workload for medical staff, and enhanced comfort during vaginal delivery. The rates of emergency caesarean sections and neonatal asphyxia in the Thoth group were marginally lower than those in the Doppler group, which may be attributed to issues such as ECG disconnection or interference from the maternal heart rate.

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Non-invasive foetal electrocardiography (NI-FECG) has become an important prenatal monitoring method in the hospital. However, due to its susceptibility to non-stationary noise sources and lack of robust extraction methods, the capture of high-quality NI-FECG remains a challenge. Recording waveforms of sufficient quality for clinical use typically requires human visual inspection of each recording. A Signal Quality Index (SQI) can help to automate this task but, contrary to adult ECG, work on SQIs for NI-FECG is sparse. In this paper, a multi-channel signal quality classifier for NI-FECG waveforms is presented. The model can be used during the capture of NI-FECG to assist technicians to record high-quality waveforms, which is currently a labour-intensive task. A Convolutional Neural Network (CNN) is trained to distinguish between NI-FECG segments of high and low quality. NI-FECG recordings with one maternal channel and three abdominal channels were collected from 100 subjects during a routine hospital screening (102.6 min of data). The model achieves an average 10-fold cross-validated AUC of 0.95 ± 0.02. The results show that the model can reliably assess the FECG signal quality on our dataset. The proposed model can improve the automated capture and analysis of NI-FECG as well as reduce technician labour time.

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BACKGROUND AND OBJECTIVE: The detection of a clean and undistorted foetal electrocardiogram (fECG) from non-invasive abdominal recordings is an open research issue. Several physiological and instrumental noise sources hamper this process, even after that powerful fECG extraction algorithms have been used. Wavelet denoising is widely used for the improvement of the SNR in biomedical signal processing. This work aims to systematically assess conventional and unconventional wavelet denoising approaches for the post-processing of fECG signals by providing evidence of their effectiveness in improving fECG SNR while preserving the morphology of the signal of interest. METHODS: The stationary wavelet transform (SWT) and the stationary wavelet packet transform (SWPT) were considered, due to their different granularity in the sub-band decomposition of the signal. Three thresholds from the literature, either conventional (Minimax and Universal) and unconventional, were selected. To this aim, the unconventional one was adapted for the first time to SWPT by trying different approaches. The decomposition depth was studied in relation to the characteristics of the fECG signal. Synthetic and real datasets, publicly available for benchmarking and research, were used for quantitative analysis in terms of noise reduction, foetal QRS detection performance and preservation of fECG morphology. RESULTS: The adoption of wavelet denoising approaches generally improved the SNR. Interestingly, the SWT methods outperformed the SWPT ones in morphology preservation (p<0.04) and SNR (p<0.0003), despite their coarser granularity in the sub-band analysis. Remarkably, the Han et al. threshold, adopted for the first time for fECG processing, provided the best quality improvement (p<0.003). CONCLUSIONS: The findings of our systematic analysis suggest that particular care must be taken when selecting and using wavelet denoising for non-invasive fECG signal post-processing. In particular, despite the general noise reduction capability, signal morphology can be significantly altered on the basis of the parameterization of the wavelet methods. Remarkably, the adoption of a finer sub-band decomposition provided by the wavelet packet was not able to improve the quality of the processing.

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BACKGROUND: Maturity of the autonomic nervous system (ANS) is of paramount importance for fetal adaptation to extrauterine life and for early neurological development. Markers of ANS maturity, such as electrophysiological heart rate parameters, are of interest as tools to determine prenatal fetal maturity. The available technology, fetal magnetocardiography is expensive and not suitable for clinical use. Detection of fetal electrocardiographic signals using traditional ECG leads on the maternal abdomen may be brought to the bedside, but is technically challenging. Our group has recently developed an innovative system consisting of a standard ECG with external leads applied on the maternal abdomen coupled with a software that extracts the fetal heart signal from the maternal noise. OBJECTIVE: To validate the use of this innovative non-invasive system to detect fetal ECG (fECG) and its ability to detect changes in electrophysiological fetal cardiac parameters associated with ANS maturation. STUDY DESIGN: we recruited 50 pregnant women between 24 and 41 weeks and they received non-invasive recording of fECG. RESULTS: fECG was measurable at all gestational ages. Fetal heart rate variability (RR interval) and other associated parameters, such as low and high frequency increased with gestational age, particularly up to the 31st week. CONCLUSIONS: This study shows that non-invasive fECG is feasible throughout a broad range of gestational ages and allows detecting electrophysiological parameters of the fetal heart that may be used a surrogate of ANS maturity. Technological implementation of this system and its further exploitation may generate new tool to estimate fetal maturity.

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BACKGROUND AND OBJECTIVE: This paper proposes a simple yet effective method for the extraction of foetal ECG from abdominal ECG which is necessary due to similar spatial and temporal content of mother and foetal ECG. METHODS: The proposed algorithm for extraction of foetal ECG (fECG) from abdominal signal uses single channel. Pre-processing of abdominal ECG (abdECG) has been done to eliminate noise and condition the signal. The maternal ECG R-peaks have been detected based on thresholding, first order Gaussian differentiation and zero cross detection on pre-processed signal. Having identified R-peaks and pre-processed signal as base, using Maximum Likelihood Estimation, one beat including QRS complex morphology of maternal ECG (mECG) has been constructed. Extraction of maternal ECG from abdECG is done based on the constructed beat, R-peak locations and its corresponding QRS complex of abdECG. Extracted mECG has been cancelled from abdECG. This results in foetal ECG with residual noise. The noise has been reduced by Polynomial Approximation and Total Variation (PATV) to improve SNR. This approach ensures no loss of partially or completely overlapped fECG signals due to mECG removal. The algorithm is tested on three database namely daISy (DBI), Physiobank challenge 2013 (DBII) and abdominal and direct foetal ECG database (adfecgdb) of Physiobank (DBIII). RESULTS: The algorithm detected no false positives or false negatives with certain channel for DBI, DBII and DBIII which shows that the proposed algorithm can achieve good performance. Overall accuracy and sensitivity of the system is 98.53% and 100% for DBI. Best accuracy and sensitivity of 97.77% and 98.63% are obtained for DBII. Best accuracy of 92.41% and sensitivity of 93.8% are obtained for DBIII. Correlation coefficient between actual foetal heart rate (fHR) and estimated fHR of 0.66 for DBII and 0.59 for DBIII is obtained. The method has obtained overall F1 score of 99.25% for DBI, 96.04% for DBII and 94.25% for DBIII. It has obtained a best MSE of fHR and overall MSE of R-R interval which is 10.8bpm2 and 2.2 ms for DBII, 12bpm2 and 2.14 ms for DBIII. CONCLUSION: The results for different public databases show that the proposed method is capable of providing good results. The foetal QRS, R-peaks and R-R intervals have also been obtained in this method. Thus, it gives a significant contribution in the required area of research.

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We construct the components for a family of computational models of the electrophysiology of the human foetal heart from 60 days gestational age (DGA) to full term. This requires both cell excitation models that reconstruct the myocyte action potentials, and datasets of cardiac geometry and architecture. Fast low-angle shot and diffusion tensor magnetic resonance imaging (DT-MRI) of foetal hearts provides cardiac geometry with voxel resolution of approximately 100 µm. DT-MRI measures the relative diffusion of protons and provides a measure of the average intravoxel myocyte orientation, and the orientation of any higher order orthotropic organization of the tissue. Such orthotropic organization in the adult mammalian heart has been identified with myocardial sheets and cleavage planes between them. During gestation, the architecture of the human ventricular wall changes from being irregular and isotropic at 100 DGA to an anisotropic and orthotropic architecture by 140 DGA, when it has the smooth, approximately 120° transmural change in myocyte orientation that is characteristic of the adult mammalian ventricle. The DT obtained from DT-MRI provides the conductivity tensor that determines the spread of potential within computational models of cardiac tissue electrophysiology. The foetal electrocardiogram (fECG) can be recorded from approximately 60 DGA, and RR, PR and QT intervals between the P, R, Q and T waves of the fECG can be extracted by averaging from approximately 90 DGA. The RR intervals provide a measure of the pacemaker rate, the QT intervals an index of ventricular action potential duration, and its rate-dependence, and so these intervals constrain and inform models of cell electrophysiology. The parameters of models of adult human sinostrial node and ventricular cells that are based on adult cell electrophysiology and tissue molecular mapping have been modified to construct preliminary models of foetal cell electrophysiology, which reproduce these intervals from fECG recordings. The PR and QR intervals provide an index of conduction times, and hence propagation velocities (approx. 1-10 cm s(-1), increasing during gestation) and so inform models of tissue electrophysiology. Although the developing foetal heart is small and the cells are weakly coupled, it can support potentially lethal re-entrant arrhythmia.