Fetal Electrocardiogram Research Articles

Study on the clinical value of the wearable foetal electrocardiogram monitoring system.

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. 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. 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. 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. 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.

A Non-Invasive Fetal QRS Complex Detection Method Based on a Multi-Feature Fusion Neural Network

Fetal heart monitoring, as a crucial part of fetal monitoring, can accurately reflect the fetus’s health status in a timely manner. To address the issues of high computational cost, inability to observe fetal heart morphology, and insufficient accuracy associated with the traditional method of calculating the fetal heart rate using a four-channel maternal electrocardiogram (ECG), a method for extracting fetal QRS complexes from a single-channel non-invasive fetal ECG based on a multi-feature fusion neural network is proposed. Firstly, a signal entropy data quality detection algorithm based on the blind source separation method is designed to select maternal ECG signals that meet the quality requirements from all channel ECG data, followed by data preprocessing operations such as denoising and normalization on the signals. After being segmented by the sliding window method, the maternal ECG signals are calculated as data in four modes: time domain, frequency domain, time–frequency domain, and data eigenvalues. Finally, the deep neural network using three multi-feature fusion strategies—feature-level fusion, decision-level fusion, and model-level fusion—achieves the effect of quickly identifying fetal QRS complexes. Among the proposed networks, the one with the best performance has an accuracy of 95.85% and sensitivity of 97%.

Data-driven optimal shrinkage of singular values under high-dimensional noise with separable covariance structure with application

We develop a data-driven optimal shrinkage algorithm, named extended OptShrink (eOptShrink), for matrix denoising with high-dimensional noise and a separable covariance structure. This noise is colored and dependent across samples. The algorithm leverages the asymptotic behavior of singular values and vectors of the noisy data's random matrix. Our theory includes the sticking property of non-outlier singular values, delocalization of weak signal singular vectors, and the spectral behavior of outlier singular values and vectors. We introduce three estimators: a novel rank estimator, an estimator for the spectral distribution of the pure noise matrix, and the optimal shrinker eOptShrink. Notably, eOptShrink does not require estimating the noise's separable covariance structure. We provide a theoretical guarantee for these estimators with a convergence rate. Through numerical simulations and comparisons with state-of-the-art optimal shrinkage algorithms, we demonstrate eOptShrink's application in extracting maternal and fetal electrocardiograms from single-channel trans-abdominal maternal electrocardiograms.

Integrating Contrastive Learning and Cycle Generative Adversarial Networks for Non-invasive Fetal ECG Extraction.

Fetal electrocardiogram (FECG) contains crucial information about the fetus during pregnancy, making the extraction of FECG signal essential for monitoring fetal health. However, extracting FECG signal from abdominal electrocardiogram (AECG) poses several challenges: (1) FECG signal is often contaminated by noise, and (2) FECG signal is frequently overshadowed by high-amplitude maternal electrocardiogram (MECG). To address these issues and enhance the accuracy of signal extraction, this paper proposes an improved Cycle Generative Adversarial Networks (CycleGAN) with integrated contrastive learning for FECG signal extraction. The model introduces a dual-attention mechanism in the generator of the generative adversarial network, incorporating amulti-head self-attention(MSA) module and achannel-wise self-attention (CSA) module to enhance the quality of generated signals. Additionally, a contrastive triplet loss is integrated into the CycleGAN loss function, optimizing training to increase the similarity between the extracted FECG signal and the scalp fetal electrocardiogram. The proposed method is evaluated using the ADFECG dataset and the PCDB dataset both from the Physionet. In terms of signal extraction quality, Mean Squared Error is reduced to 0.036, Mean Absolute Error (MAE) to 0.009, and Pearson Correlation Coefficient reaches 0.924. When validating the model performance, Structural Similarity Index achieves 95.54%, Peak Signal-to-Noise Ratio (PSNR) reaches 38.87dB, and R-squared (R2) attains 95.12%. Furthermore, the positive predictive value (PPV), sensitivity (SEN) and F1-score for QRS wave cluster detection on the ADFECG dataset also reached 99.56%, 99.43% and 99.50%, respectively. On the PCDB dataset, the positive predictive value (PPV), sensitivity (SEN) and F1-score for QRS wave cluster detection also reached 98.24%, 98.60% and 98.42%, respectively. All of them are higher than other methods. Therefore, the proposed model has important applications in effective monitoring of fetal health during pregnancy.

Surveillance fœtale : limites actuelles et nouvelles pistes basées sur l’analyse du système nerveux autonome fœtal

ObjectiveCurrently, fetal monitoring during labor is based on visual analysis of the fetal heart rate (FHR). This test is imperfect, with high intra- and inter-observer variability and a moderate to poor prediction of the occurrence of neonatal acidosis or anoxic-ischaemic encephalopathy. In situations where there is an intermediate risk of acidosis, it is possible to use second-line tests such as blood scalp sampling (with pH or lactate measurement) or ST segment analysis of the fetal ECG. However, these invasive tests have many limitations and their place is debated. Some authors suggest a more physiological approach to FHR assessment. The main actor in maintaining fetal homeostasis is the autonomic nervous system (ANS). Its activity can be assessed by analysing heart rate variability (HRV). The aim is to assess whether HRV can be used to identify situations at risk of acidosis. Materials and methodsOur team has developed an index, the Fetal Stress Index, to measure HRV. To test it in a situation of acidosis, we used a pregnant ewe model. We also developed in parallel a human fetal ECG recording system. ResultsIn our experimental model, we have shown that this index reflects variations in the parasympathetic system and correlates with the onset of acidosis. As its use in clinical practice requires the acquisition of a beat-to-beat FHR signal, we have also developed an abdominal patch that allows highly accurate analysis of the fetal ECG. ConclusionThe future is therefore to validate the FSI as a marker of acidosis in a prospective cohort using the signal obtained from our patch. This could be a new tool for fetal monitoring during labor.

Retraction Note: Single-channel fetal ECG extraction method based on extended Kalman filtering with singular value decomposition algorithm

Retraction Note: Single-channel fetal ECG extraction method based on extended Kalman filtering with singular value decomposition algorithm

STUDY AND ANALYSIS OF FETAL HEART RATE MONITORING SYSTEM

Analysis of FHR variability, together with uterine muscle contractions and fetal movement activity, subject to cardiotocographic monitoring, currently play a fundamental role in fetal assessment. The strength of cardiotocography lies in that normal ranges of the results of the FHR signal analysis almost always (&gt;95%) confirm fetal wellbeing. Unfortunately, questionable or abnormal signal features may indicate both fetal distress and its absence. The classic fetal heart rate variability analysis consists in determining the FHR baseline and then in identifying bradycardia/tachycardia, acceleration/deceleration patterns as well as the type and amplitude of oscillations. A more advanced FHR analysis is aimed at evaluating instantaneous FHR variability, especially with beat-to-beat approach, which is very important in fetal condition assessment. The most common technique of recording the FHR signal is the Doppler ultrasound method, which makes it difficult to correctly estimate the instantaneous heart rate variability because the measurements are averaged. On the other hand, instantaneous heart rate variability plays a crucial role in the diagnosis of fetal condition [1]. The increased interest in monitoring the fetal cardiac bioelectrical activity is due to the fact that the analysis of the fetal electrocardiogram ensures high efficiency and accuracy of the heart rate measurement even on a beat-to-beat level. The first fetal electrocardiogram was recorded as early as in the 1960-ies by means of a special spiral electrode enabling direct signal registration from a fetal head. Due to the way the electrode is placed, its use is limited only to the advanced labour stage [1]..

Unsupervised denoising of the non-invasive fetal electrocardiogram with sparse domain Kalman filtering and vectorcardiographic loop alignment

Objective. Even though the electrocardiogram (ECG) has potential to be used as a monitoring or diagnostic tool for fetuses, the use of non-invasive fetal ECG is complicated by relatively high amounts of noise and fetal movement during the measurement. Moreover, machine learning-based solutions to this problem struggle with the lack of clean reference data, which is difficult to obtain. To solve these problems, this work aims to incorporate fetal rotation correction with ECG denoising into a single unsupervised end-to-end trainable method. Approach. This method uses the vectorcardiogram (VCG), a three-dimensional representation of the ECG, as an input and extends the previously introduced Kalman-LISTA method with a Kalman filter for the estimation of fetal rotation, applying denoising to the rotation-corrected VCG. Main results. The resulting method was shown to outperform denoising auto-encoders by more than 3 dB while achieving a rotation tracking error of less than 33∘. Furthermore, the method was shown to be robust to a difference in signal to noise ratio between electrocardiographic leads and different rotational velocities. Significance. This work presents a novel method for the denoising of non-invasive abdominal fetal ECG, which may be trained unsupervised and simultaneously incorporates fetal rotation correction. This method might prove clinically valuable due the denoised fetal ECG, but also due to the method’s objective measure for fetal rotation, which in turn might have potential for early detection of fetal complications.

Application of the electrocardiography method for the diagnosis of foaling in horses

Diagnosis of pregnancy is a responsible and integral procedure in the list of measures aimed at obtaining a healthy horse population. Pregnancy confirmation can be performed using different methods, with ultrasound examination (US) being the most widespread and universal. However, this method has some limitations and disadvantages. An alternative to the ultrasound method for diagnosing pregnancy and monitoring the fetus in the middle and late stages of pregnancy can be the electrocardiography (ECG) method. A description of this method for recording the biopotentials of the fetal heart is found in Soviet and modern foreign literature. However, this method of research is not reflected in modern Russian literature and in practice has been undeservedly forgotten. The purpose of this study is to update data on the described diagnostic method, as well as to assess the possibility and reliability of using the ECG method for diagnosing and monitoring pregnancy in horses.The experiment involved a Hanoverian mare whose pregnancy was confirmed by ultrasound. ECG registration was performed on days 121, 150, 184, 214, 245, 276, 304, 335 of pregnancy.As a result of the research, the following was established. The earliest period of pregnancy at which it was possible to detect a fetal ECG was 150 days. By the 11th month of pregnancy, there was a clear trend towards a decrease in the fetal heart rate and a significant increase in the voltage of the fetal teeth.This type of research is easily reproducible in the field, does not require expensive equipment, is characterized by a high level of objectivity of the data obtained and can be recommended for use in the daily practice of an equine veterinarian. As a research prospect, to identify general patterns and distinguish between states of conditional norm and pathology, it can be recommended to conduct further studies with a larger sample of animals.

Fetal electrocardiogram signal extraction based on multi-scale residual shrinkage U-Net

In the extraction of fetal electrocardiogram (ECG) signal, due to the unicity of the scale of the U-Net same-level convolution encoder, the size and shape difference of the ECG characteristic wave between mother and fetus are ignored, and the time information of ECG signals is not used in the threshold learning process of the encoder's residual shrinkage module. In this paper, a method of extracting fetal ECG signal based on multi-scale residual shrinkage U-Net model is proposed. First, the Inception and time domain attention were introduced into the residual shrinkage module to enhance the multi-scale feature extraction ability of the same level convolution encoder and the utilization of the time domain information of fetal ECG signal. In order to maintain more local details of ECG waveform, the maximum pooling in U-Net was replaced by Softpool. Finally, the decoder composed of the residual module and up-sampling gradually generated fetal ECG signals. In this paper, clinical ECG signals were used for experiments. The final results showed that compared with other fetal ECG extraction algorithms, the method proposed in this paper could extract clearer fetal ECG signals. The sensitivity, positive predictive value, and F1 scores in the 2013 competition data set reached 93.33%, 99.36%, and 96.09%, respectively, indicating that this method can effectively extract fetal ECG signals and has certain application values for perinatal fetal health monitoring.

Elicitation of fetal ECG from abdominal recordings using Blind Source Separation techniques and Robust Set Membership Affine Projection algorithm for signal quality enhancement

Background:The utilization of non-invasive techniques for fetal cardiac health surveillance is pivotal in evaluating fetal well-being throughout the gestational period. This process requires clean and interpretable fetal Electrocardiogram (fECG) signals. Method:The proposed work is the novel framework for the elicitation of fECG signals from abdominal ECG (aECG) recordings of the pregnant mother. The comprehensive approach encompasses pre-processing of the raw ECG signal, Blind Source Separation techniques (BSS), Decomposition techniques like Empirical Mode Decomposition (EMD), and its variants like Ensemble Empirical Mode Decomposition (EEMD), and Complete Ensemble Empirical Mode Decomposition with Additive Noise (CEEMDAN). The Robust Set Membership Affine Projection (RSMAP) Algorithm is deployed for the enhancement of the obtained fECG signal. Result:The results show significant improvements in the elicited fECG signal with a maximum Signal Noise Ratio (SNR) of 31.72 dB and correlation coefficient = 0.899, Maximum Heart Rate(MHR) obtained in the range of 108–142 bpm for all the records of abdominal ECG signals. The statistical test gave a p-value of 0.21 accepting the null hypothesis. The Abdominal and Direct Fetal Electrocardiogram Database (ABDFECGDB) from PhysioNet has been used for this analysis. Conclusion:The proposed framework demonstrates a robust and effective method for the elicitation and enhancement of fECG signals from the abdominal recordings.

Frequency Domain Template Subtraction Approach to Attenuate Maternal Electrocardiogram in Fetal Electrocardiogram.

We develop a frequency domain template subtraction approach to attenuate the maternal ECG in the abdominal ECG measured from pregnant women. The proposed approach was tested on five public fetal ECG datasets simultaneously measured with ECG from the fetal scalp. The method's performance was compared with the template subtraction approach in the time domain using the accuracy and association metrics. The accuracy was calculated by counting the number of fetal complexes in the processed data that coincided with the fetal complexes in the scalp fetal ECG. The association is quantified as the coherence between the processed data and the gold standard. The maximum coherence values calculated for each approach were compared using the paired t-test. Our results showed no difference in the accuracy between the frequency and time domain approach (p = 0.733). However, the association was higher between the frequency domain data and the gold standard compared to the template subtraction data and the gold standard (p = 0.049), indicating that the frequency domain approach yielded a signal that resembled that of the scalp ECG compared to the time domain approach.

Advancing non-invasive fetal health monitoring: A time–frequency approach to extracting fetal electrocardiogram signals

The fetal electrocardiogram (FECG) provides an important way for clinicians to assess fetal health and to monitor pregnancy conditions. Although FECG can be captured through abdominal electrocardiogram (AECG) recordings from pregnant women, it often receives interference from maternal cardiac activity and other external sources. Particularly, as the high-amplitude maternal electrocardiogram (MECG) typically obscures the waveform of the FECG, isolating FECG from the AECG signals is challenging. Nevertheless, the differences in frequency patterns between MECG and FECG suggest a potential way for reconstructing FECG by combining the frequency and amplitude information. To this end, we proposed a novel method for separating FECG signals from the AECG via the time–frequency representation. We designed an encoder–decoder architecture based hierarchical multiscale-aware network to capture features embedded in both the time and frequency domains. Equipped with a holistic attention-aware module (HAA), the model extracts and consolidates fetal heart activity-related features in the time–frequency domain. Further introducingan attention gate module (AG) to emphasize key features during encoder-to-decoder transmission, the model enhances the quality and efficiency of FECG extraction without any redundancy in high-quality feature mapping. The model was trained using a hybrid time-domain and frequency-domain loss function and validated on two public databases. Excellent performances, such as an average Pearson correlation coefficient of 0.85 for estimating the quality of FECG signals, were observed demonstrating its excellent performance and providing a promising non-invasive method for monitoring fetal health. These findings indicate that the proposed method effectively captures the amplitude differences between fetal and maternal heartbeats across various frequency components within the AECG, leading to the extraction of high-quality FECG signals.

Diagnosis and Management of Fetal Arrhythmias in the Current Era.

Diagnosis and management of fetal arrhythmias have changed over the past 40-50 years since propranolol was first used to treat fetal tachycardia in 1975 and when first attempts were made at in utero pacing for complete heart block in 1986. Ongoing clinical trials, including the FAST therapy trial for fetal tachycardia and the STOP-BLOQ trial for anti-Ro-mediated fetal heart block, are working to improve diagnosis and management of fetal arrhythmias for both mother and fetus. We are also learning more about how "silent arrhythmias", like long QT syndrome and other inherited channelopathies, may be identified by recognizing "subtle" abnormalities in fetal heart rate, and while echocardiography yet remains the primary tool for diagnosing fetal arrhythmias, research efforts continue to advance the clinical envelope for fetal electrocardiography and fetal magnetocardiography. Pharmacologic management of fetal arrhythmias remains one of the most successful achievements of fetal intervention. Patience, vigilance, and multidisciplinary collaboration are key to successful diagnosis and treatment.

Compression of electrocardiogram signals using compressive sensing technique based on curvelet transform toward medical applications

AbstractElectrocardiogram (ECG) signals can be monitored from many patients based on healthcare systems. To enhance these systems, the ECG signals should be collected and then stored in a cloud platform for later analysis. Hence, ECG signals can be utilized to diagnose heart diseases. However, the ECG signals require great internet capacity. So, compression techniques can be implemented to reduce a memory storage capacity for these signals. One of the potential compression techniques is the compressive sensing (CS). This paper proposes a CS technique to compress ECG signals. This technique is used to reduce sampling rates of the ECG signals to be less than the Nyquist rate. Moreover, a framework is suggested for the compression of maternal and fetal ECG signals. The compression of these signals is based on the curvelet transform (CT) to produce sparsity in ECG signals. The MIT-BIH database are utilized for testing the ECG signals. This database includes several ECG signals with various sampling rates, such as aberrant and normal signals. The proposed CS technique achieved a compression ratio (CR) of 15.7 with an accuracy of 98.2%. Also, a percentage root mean difference (PRD) is utilized to calculate the performance of the reconstructed ECG signals. The achieved value of the PRD is 2.0.

The Development and Implementation of Innovative Blind Source Separation Techniques for Real-Time Extraction and Analysis of Fetal and Maternal Electrocardiogram Signals.

This article presents an innovative approach to analyzing and extracting electrocardiogram (ECG) signals from the abdomen and thorax of pregnant women, with the primary goal of isolating fetal ECG (fECG) and maternal ECG (mECG) signals. To resolve the difficulties related to the low amplitude of the fECG, various noise sources during signal acquisition, and the overlapping of R waves, we developed a new method for extracting ECG signals using blind source separation techniques. This method is based on independent component analysis algorithms to detect and accurately extract fECG and mECG signals from abdomen and thorax data. To validate our approach, we carried out experiments using a real and reliable database for the evaluation of fECG extraction algorithms. Moreover, to demonstrate real-time applicability, we implemented our method in an embedded card linked to electronic modules that measure blood oxygen saturation (SpO2) and body temperature, as well as the transmission of data to a web server. This enables us to present all information related to the fetus and its mother in a mobile application to assist doctors in diagnosing the fetus's condition. Our results demonstrate the effectiveness of our approach in isolating fECG and mECG signals under difficult conditions and also calculating different heart rates (fBPM and mBPM), which offers promising prospects for improving fetal monitoring and maternal healthcare during pregnancy.

Enhancing Fetal Electrocardiogram Signal Extraction Accuracy through a CycleGAN Utilizing Combined CNN-BiLSTM Architecture.

The fetal electrocardiogram (FECG) records changes in the graph of fetal cardiac action potential during conduction, reflecting the developmental status of the fetus in utero and its physiological cardiac activity. Morphological alterations in the FECG can indicate intrauterine hypoxia, fetal distress, and neonatal asphyxia early on, enhancing maternal and fetal safety through prompt clinical intervention, thereby reducing neonatal morbidity and mortality. To reconstruct FECG signals with clear morphological information, this paper proposes a novel deep learning model, CBLS-CycleGAN. The model's generator combines spatial features extracted by the CNN with temporal features extracted by the BiLSTM network, thus ensuring that the reconstructed signals possess combined features with spatial and temporal dependencies. The model's discriminator utilizes PatchGAN, employing small segments of the signal as discriminative inputs to concentrate the training process on capturing signal details. Evaluating the model using two real FECG signal databases, namely "Abdominal and Direct Fetal ECG Database" and "Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeat Annotations", resulted in a mean MSE and MAE of 0.019 and 0.006, respectively. It detects the FQRS compound wave with a sensitivity, positive predictive value, and F1 of 99.51%, 99.57%, and 99.54%, respectively. This paper's model effectively preserves the morphological information of FECG signals, capturing not only the FQRS compound wave but also the fetal P-wave, T-wave, P-R interval, and ST segment information, providing clinicians with crucial diagnostic insights and a scientific foundation for developing rational treatment protocols.

LabVIEW-Enabled Synthetic Signal for Empowering Fetal-Maternal Healthcare

Biomedical signal processing has advanced to the point that tools and methods are now available to doctors to diagnose and track medical conditions connected to pregnancy. However, it is extremely difficult for researchers to look into novel procedures and approaches to uncover underlying pathological abnormalities associated with high-risk pregnancies due to the scarcity of high-quality medical databases of pregnant women. In this study, a LabVIEW software environment is used to precisely design a bio-physiological signal generator (BPSG) for use in feto-maternal health assessment applications. McSharry’s dynamical ECG model served as inspiration for the methods utilized to create the proposed time-domain mathematical model. The BPSG is capable of generating various realistic synthetic signals like respiration signal, pulse plethysmography (PPG) signal, phonocardiography (PCG) signal, maternal ECG (MECG) signal, fetal ECG (FECG) signal, abdominal ECG (AECG) signa,l and umbilical blood flow (UBF) velocimetry signals with corresponding Doppler indices. It is possible to create synthetic signals for both healthy and unhealthy conditions. Synthetic signal facilitates the testing and calibration of new diagnostic procedures, denoising algorithms, feature extraction processes, and instrumentation, all of which contribute to the prompt prediction of an overall health state of expectant mother.

Power-MF: robust fetal QRS detection from non-invasive fetal electrocardiogram recordings

Objective. 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. Approach. In this work, we propose Power-MF, a new algorithm for fetal QRS detection combining power spectral density and matched filter techniques. We benchmark Power-MF against 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). Main results. Our results show that Power-MF outperforms 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. Significance. 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.

A deep learning framework for noninvasive fetal ECG signal extraction.

Introduction: The availability of proactive techniques for health monitoring is essential to reducing fetal mortality and avoiding complications in fetal wellbeing. In harsh circumstances such as pandemics, earthquakes, and low-resource settings, the incompetence of many healthcare systems worldwide in providing essential services, especially for pregnant women, is critical. Being able to continuously monitor the fetus in hospitals and homes in a direct and fast manner is very important in such conditions. Methods: Monitoring the health of the baby can potentially be accomplished through the computation of vital bio-signal measures using a clear fetal electrocardiogram (ECG) signal. The aim of this study is to develop a framework to detect and identify the R-peaks of the fetal ECG directly from a 12 channel abdominal composite signal. Thus, signals were recorded noninvasively from 70 pregnant (healthy and with health conditions) women with no records of fetal abnormalities. The proposed model employs a recurrent neural network architecture to robustly detect the fetal ECG R-peaks. Results: To test the proposed framework, we performed both subject-dependent (5-fold cross-validation) and independent (leave-one-subject-out) tests. The proposed framework achieved average accuracy values of 94.2% and 88.8%, respectively. More specifically, the leave-one-subject-out test accuracy was 86.7% during the challenging period of vernix caseosa layer formation. Furthermore, we computed the fetal heart rate from the detected R-peaks, and the demonstrated results highlight the robustness of the proposed framework. Discussion: This work has the potential to cater to the critical industry of maternal and fetal healthcare as well as advance related applications.