Non-invasive fetal ECG analysis

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.

[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.

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 proposePower-MF, a new algorithm for fetal QRS detection combining power spectral density and matched filter techniques. We benchmarkPower-MFagainst 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 thatPower-MFoutperforms 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.

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.

A Nonlinear Functional Link Multilayer Perceptron Using Volterra Series as an Adaptive Noise Canceler for the Extraction of Fetal Electrocardiogram

Uninterrupted monitoring of fetal cardiac health is essential for the timely diagnosis of congenital diseases. The maternal Electrocardiogram (mECG), which has the most significant impact, always tampers with the signals collected from the pregnant woman's abdomen. So, an efficient nonlinear filtering network based on artificial neural network (ANN) is required to eliminate the maternal part from the abdominal Electrocardiogram (aECG) that is traveled from the thoracic of the mother to the abdomen following nonlinear dynamics. In this work, we have presented an adaptive noise canceler (ANC) using 3-layer perceptron architecture where the inputs are expanded by the functional link expansion using the second-order Volterra series, and the weights are updated using backpropagation. The adaptive filter approximates the nonlinear mapping between the thoracic Electrocardiogram (tECG) and the maternal component present in the aECG. Here the thoracic signal is the reference signal, and the abdominal signal is the desired signal to the adaptive filter. The proposed methodology uses the advantages of both multilayer perceptron (MLP) as well as functional link neural network (FLNN) in mapping the nonlinearity and effectively determining the fetal Electrocardiogram (fECG) from the aECG. For the detailed analysis, we have used the real Daisy database, the Non-invasive Fetal ECG database, and the fetal ECG synthetic database from Physionet. The results show that the nonlinear functional link MLP using the Volterra series gives a high-level performance compared to other classical adaptive filtering techniques, as all the evaluation metrics are above 90%.

Fetal monitoring technologies for the detection of intrapartum hypoxia - challenges and opportunities

Intrapartum fetal hypoxia is related to long-term morbidity and mortality of the fetus and the mother. Fetal surveillance is extremely important to minimize the adverse outcomes arising from fetal hypoxia during labour. Several methods have been used in current clinical practice to monitor fetal well-being. For instance, biophysical technologies including cardiotocography, ST-analysis adjunct to cardiotocography, and Doppler ultrasound are used for intrapartum fetal monitoring. However, these technologies result in a high false-positive rate and increased obstetric interventions during labour. Alternatively, biochemical-based technologies including fetal scalp blood sampling and fetal pulse oximetry are used to identify metabolic acidosis and oxygen deprivation resulting from fetal hypoxia. These technologies neither improve clinical outcomes nor reduce unnecessary interventions during labour. Also, there is a need to link the physiological changes during fetal hypoxia to fetal monitoring technologies. The objective of this article is to assess the clinical background of fetal hypoxia and to review existing monitoring technologies for the detection and monitoring of fetal hypoxia. A comprehensive review has been made to predict fetal hypoxia using computational and machine-learning algorithms. The detection of more specific biomarkers or new sensing technologies is also reviewed which may help in the enhancement of the reliability of continuous fetal monitoring and may result in the accurate detection of intrapartum fetal hypoxia.

A Novel Approach to Fetal ECG Extraction Using Temporal Convolutional Encoder-Decoder Network (TCED-Net)

To extract weak fetal ECG signals from the mixed ECG signal on the mother's abdominal wall, providing a basis for accurately estimating fetal heart rate and analyzing fetal ECG morphology. First, based on the relationship between the maternal chest ECG signal and the maternal ECG component in the abdominal signal, the temporal convolutional encoder-decoder network (TCED-Net) model is trained to fit the nonlinear transmission of the maternal ECG signal from the chest to the abdominal wall. Then, the maternal chest ECG signal is nonlinearly transformed to estimate the maternal ECG component in the abdominal mixed signal. Finally, the estimated maternal ECG component is subtracted from the abdominal mixed signal to obtain the fetal ECG component. The simulation results on the FECGSYN dataset show that the proposed approach achieves the best performance in F1 score, mean square error (MSE), and quality signal-to-noise ratio (qSNR) (98.94%, 0.18, and 8.30, respectively). On the NI-FECG dataset, although the fetal ECG component is small in energy in the mixed signal, this method can effectively suppress the maternal ECG component and thus extract a clearer and more accurate fetal ECG signal. Compared with existing algorithms, the proposed method can extract clearer fetal ECG signals, which has significant application value for effective fetal health monitoring during pregnancy.

Deep learning with fetal ECG recognition

Objective.Independent component analysis (ICA) is widely used in the extraction of fetal ECG (FECG). However, the amplitude, order, and positive or negative values of the ICA results are uncertain. The main objective is to present a novel approach to FECG recognition by using a deep learning strategy.Approach.A cross-domain consistent convolutional neural network (CDC-Net) is developed for the task of FECG recognition. The output of the ICA algorithm is used as input to the CDC-Net and the CDC-Net identifies which channel's signal is the target FECG.Main results.Signals from two databases are used to test the efficiency of the proposed method. The proposed deep learning method exhibits good performance on FECG recognition. Specifically, the Precision, Recall and F1-score of the proposed method on the ADFECGDB database are 91.69%, 91.37% and 91.52%, respectively. The Precision, Recall and F1-score of the proposed method on the Daisy database are 97.85%, 97.42% and 97.63%, respectively.Significance. This study is a proof of concept that the proposed method can automatically recognize the FECG signals in multi-channel ECG data. The development of FECG recognition technology contributes to automated FECG monitoring.

Evaluation of The Fetal Heart Rate Monitoring with The Non-Invasive Electrocardiography Signals

Fetal heart rate monitoring is a crucial element in determining the health of the fetus during pregnancy. In this paper, we evaluate the fetal heart rate (FHR) and maternal heart rate (MHR) between our non-invasive fetal monitoring system, Femom, developed by a Biorithm and the Huntleigh computerized cardiotocography (cCTG) together with the Sonicaid FetalCare3 software by comparing the accuracy, sensitivity, and reliability through using Bland-Altman analysis, Positive Percent Agreement (PPA) and Intraclass Correlation Coefficient (ICC) respectively. Femom device is a part of the Femom system which collects abdominal electrocardiogram (aECG) signals. Femom sever then processes the collected signals to generate FHR and MHR using novel algorithms. We collected data from 285 pregnant participants who were at least of 28 weeks of gestational age. FHR accuracy consists of mean bias and limit-of-agreement (LoA). The FHR bias is 0.05 beat per minute (BPM) and LoA is [-8.7 8.8] with 95% confidence interval (95% CI) measured using Bland Altman analysis. The PPA of 90.9% reflects FHR sensitivity. Reliability is measured with absolute ICC and consistency ICC. The absolute ICC is of 88% and consistency ICC of 94%. For MHR evaluation, accuracy is measured using Bland Altman analysis which provided a bias of 0.35 BPM and LoA of [-7 6.2] with 95% CI. The MHR sensitivity calculated using PPA is 98% while the MHR reliability is with the absolute value of 99% and consistency ICC of 99%.

Correlation of short-term variation derived from novel ambulatory fetal electrocardiography monitor with computerized cardiotocography

OBJECTIVES

To compare short-term variation (STV) outputs from a novel self-applied non-invasive fetal electrocardiography (NIFECG) device with those obtained on computerized cardiotocography (cCTG). Technological and algorithmic limitations and mitigation strategies were also evaluated.

METHODS

This was a prospective cohort study of women with a singleton pregnancy over 28 + 0 weeks' gestation attending a tertiary London hospital for cCTG assessment between June 2021 and June 2022. Women underwent concurrent monitoring with both NIFECG and cCTG for up to 1 h. Postprocessing of NIFECG data using various filtering methods produced NIFECG-STV (eSTV) values, which were compared with cCTG-STV (cSTV) outputs. Linear correlation, mean bias, precision and limits of agreement were assessed for STV derived by the different methods of computation and mathematical correction.

RESULTS

Overall, 306 concurrent NIFECG and cCTG traces were collected from 285 women. Fully filtered eSTV was correlated very strongly with cSTV (r = 0.911, P < 0.001), but generated results only in 142/306 (46.4%) 1-h traces owing to the removal of traces with lower-quality signals. Partial filtering generated more eSTV data (98.4%), but with a weak correlation with cSTV (r = 0.337, P < 0.001). The difference in STV between the monitors (eSTV - cSTV) increased with signal loss; in traces with > 60% signal loss, the values became highly discrepant. Removal of traces with > 60% signal loss resulted in a stronger correlation with cSTV, while still generating eSTV results for 65% of traces. Correcting these remaining eSTV values for signal loss using linear regression analysis further improved correlation with cSTV (r = 0.839, P < 0.001).

CONCLUSIONS

The discrepancy between STV computed by NIFECG and cCTG necessitates signal filtering, exclusion of poor-quality traces and eSTV correction. This study demonstrates that, with such correction, NIFECG is able to produce STV values that are strongly correlated with those of cCTG. This evidence base for NIFECG monitoring and interpretation is a promising step forward in the development of safe and effective at-home fetal heart-rate monitoring. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Phase-rectified signal averaging: correlation between two monitors and relationship with short-term variation of fetal heart rate

OBJECTIVES

To establish the correlation between phase-rectified signal averaging (PRSA) outputs obtained from a novel self-applicable non-invasive fetal electrocardiography (NIFECG) monitor with those from computerized cardiotocography (cCTG). A secondary objective was to evaluate the potential for remote assessment of fetal wellbeing by determining the relationship between PRSA and short-term variation (STV).

METHODS

This was a prospective observational study of women with a singleton pregnancy over 28 + 0 weeks' gestation attending a London teaching hospital for cCTG assessment. Participants underwent concurrent cCTG and NIFECG monitoring for up to 60 min. Averaged accelerative (AAC) and decelerative (ADC) capacities and STV were derived by postprocessing and filtration of signals, generating fully (F) and partially (P) filtered results. Linear correlation and accuracy and precision analysis were performed to assess the relationship between PRSA outputs from cCTG and NIFECG, using varying anchor thresholds, and their association with STV.

RESULTS

A total of 306 concurrent cCTG and NIFECG traces were collected from 285 women. F-filtered NIFECG PRSA (eAAC/eADC) results were generated from 65% of traces, whereas cCTG PRSA (cAAC/cADC) outputs were generated from all. Strong correlations were observed between cAAC and F-filtered eAAC (r = 0.879, P < 0.001) and between cADC and F-filtered eADC (r = 0.895, P < 0.001). NIFECG anchor detection decreased significantly with increasing signal loss, and NIFECG PRSA indices showed considerable deviation from those of cCTG when derived from traces in which fewer than 100 anchors were detected. Removing anchor filters from NIFECG traces to generate P-filtered PRSA outputs weakened the correlation (AAC: r = 0.505, P < 0.001; ADC: r = 0.560, P < 0.001). Lowering the anchor threshold to 100 increased the yield of eAAC and eADC outputs to approximately 74%, whilst maintaining strong correlation with cAAC (r = 0.839, P < 0.001) and cADC (r = 0.815, P < 0.001), respectively. Both cAAC and cADC showed a very strong linear relationship with cCTG STV (r = 0.928, P < 0.001 and r = 0.911, P < 0.001, respectively). Similar findings were observed with eAAC (r = 0.825, P < 0.001) and eADC (r = 0.827, P < 0.001).

CONCLUSIONS

PRSA appears to be a method of fetal assessment equivalent to STV, but, due to its innate ability to eliminate artifacts, it generates interpretable NIFECG traces with high accuracy at a higher rate. These findings raise the possibility of self-applied at-home or remote fetal heart-rate monitoring with automated reporting, thus enabling increased surveillance in high-risk women without impacting on service demand. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Effectiveness of ambulatory non-invasive fetal electrocardiography: impact of maternal and fetal characteristics

INTRODUCTION

Non-invasive fetal electrocardiography (NIFECG) has potential benefits over the computerized cardiotocography (cCTG) that may permit its development in remote fetal heart-rate monitoring. Our study aims to compare signal quality and heart-rate detection from a novel self-applicable NIFECG monitor against the cCTG, and evaluate the impact of maternal and fetal characteristics on both devices.

MATERIAL AND METHODS

This prospective observational study took place in a university hospital in London. Women with a singleton pregnancy from 28 + 0 weeks' gestation presenting for cCTG were eligible. Concurrent monitoring with both NIFECG and cCTG were performed for up to 60 minutes. Post-processing of NIFECG produced signal loss, computed in both 0.25 (E240)- and 3.75 (E16)-second epochs, and fetal heart-rate and maternal heart-rate values. cCTG signal loss was calculated in 3.75-second epochs. Accuracy and precision analysis of 0.25-second epochal fetal heart-rate and maternal heart-rate were compared between the two devices. Multiple regression analyses were performed to assess the impact of maternal and fetal characteristics on signal loss.

CLINICALTRIALS

gov Identifier: NCT04941534.

RESULTS

285 women underwent concurrent monitoring. For fetal heart-rate, mean bias, precision and 95% limits of agreement were 0.1 beats per minute (bpm), 4.5 bpm and -8.7 bpm to 8.8 bpm, respectively. For maternal heart-rate, these results were -0.4 bpm, 3.3 bpm and -7.0 to 6.2 bpm, respectively. Median NIFECG E240 and E16 signal loss was 32.0% (interquartile range [IQR] 6.5%-68.5%) and 17.3% (IQR 1.8%-49.0%), respectively. E16 cCTG signal loss was 1.0% (IQR 0.0%-3.0%). For NIFECG, gestational age was negatively associated with signal loss (beta = -2.91, 95% CI -3.69 to -2.12, P < 0.001). Increased body mass index, fetal movements and lower gestational age were all associated with cCTG signal loss (beta = 0.30, 95% CI 0.17-0.43, P < 0.001; beta = 0.03, 95% CI 0.01-0.05, P = 0.014; and beta = -0.28, 95% CI -0.51 to -0.05, P = 0.017, respectively).

CONCLUSIONS

Although NIFECG is complicated by higher signal loss, it does not appear to be influenced by increased body mass index or fetal movement. NIFECG signal loss varies according to method of computation, and standards of signal acceptability need to be defined according to the ability of the device to produce clinically reliable physiological indices. The high accuracy of heart-rate indices is promising for NIFECG usage in the remote setting.

Automatic signal quality assessment of raw trans-abdominal biopotential recordings for non-invasive fetal electrocardiography

Introduction: Wearable monitoring systems for non-invasive multi-channel fetal electrocardiography (fECG) can support fetal surveillance and diagnosis during pregnancy, thus enabling prompt treatment. In these embedded systems, power saving is the key to long-term monitoring. In this regard, the computational burden of signal processing methods implemented for the fECG extraction from the multi-channel trans-abdominal recordings plays a non-negligible role. In this work, a supervised machine-learning approach for the automatic selection of the most informative raw abdominal recordings in terms of fECG content, i.e., those potentially leading to good-quality, non-invasive fECG signals from a low number of channels, is presented and evaluated. Methods: For this purpose, several signal quality indexes from the scientific literature were adopted as features to train an ensemble tree classifier, which was asked to perform a binary classification between informative and non-informative abdominal channels. To reduce the dimensionality of the classification problem, and to improve the performance, a feature selection approach was also implemented for the identification of a subset of optimal features. 10336 5-s long signal segments derived from a real dataset of multi-channel trans-abdominal recordings acquired from 55 voluntary pregnant women between the 21st and the 27th week of gestation, with healthy fetuses, were adopted to train and test the classification approach in a stratified 10-time 10-fold cross-validation scheme. Abdominal recordings were firstly pre-processed and then labeled as informative or non-informative, according to the signal-to-noise ratio exhibited by the extracted fECG, thus producing a balanced dataset of bad and good quality abdominal channels. Results and Discussion: Classification performance revealed an accuracy above 86%, and more than 88% of those channels labeled as informative were correctly identified. Furthermore, by applying the proposed method to 50 annotated 24-channel recordings from the NInFEA dataset, a significant improvement was observed in fetal QRS detection when only the channels selected by the proposed approach were considered, compared with the use of all the available channels. As such, our findings support the hypothesis that performing a channel selection by looking directly at the raw abdominal signals, regardless of the fetal presentation, can produce a reliable measurement of fetal heart rate with a lower computational burden.

A signal quality assessment method for fetal QRS complexes detection

OBJECTIVE

Non-invasive fetal ECG (NI-FECG) provides a non-invasive method to monitor the health of the fetus. However, the NI-FECG is easily interfered by noise, which makes the signal quality decline, leading to the fetal heart rate (FHR) monitoring becoming a challenging task.

METHODS

In this work, an algorithm for dynamic evaluation of signal quality is proposed to improve the multi-channel FHR monitoring. The innovation of the method is to assess the signal quality in the process of multi-channel fetal QRS (FQRS) complexes detection. Specifically, the detected FQRS is used as quality unit. Each quality unit can be a true R peak (TR) or a false R peak (FR). It is the basic quality information in this work. The signal quality of each channel is estimated by estimating the correctness of the detection results. Further, the TRs of all channels can be fused to obtain more reliable fetal heart rate monitoring.

MAIN RESULTS

Analysis results demonstrate that the proposed algorithm is capable of selecting the good quality signal for FQRS detection achieving 97.40% PPV, 98.33% SE and 97.86% F₁.

SIGNIFICANCE

This work sheds light on the quality assessment of fetal monitoring signal.

Morphology extraction of fetal ECG using temporal CNN-based nonlinear adaptive noise cancelling

OBJECTIVES

Noninvasive fetal electrocardiography (FECG) offers many advantages over alternative fetal monitoring techniques in evaluating fetal health conditions. However, it is difficult to extract a clean FECG signal with morphological features from an abdominal ECG recorded at the maternal abdomen; the signal is usually contaminated by the maternal ECG and various noises. The aim of the work is to extract an FECG signal that preserves the morphological features from the mother's abdominal ECG recording, which allows for accurately estimating the fetal heart rate (FHR) and analyzing the waveforms of the fetal ECG.

METHODS

We propose a novel nonlinear adaptive noise cancelling framework (ANC) based on a temporal convolutional neural network (CNN) to effectively extract fetal ECG signals from mothers' abdominal ECG recordings. The proposed framework consists of a two-stage network, using the ANC architecture; one network is for the maternal ECG component elimination and the other is for the residual noise component removal of the extracted fetal ECG signal. Then, JADE (one of the blind source separation algorithms) is applied as a postprocessing step to produce a clean fetal ECG signal.

RESULTS

Synthetic ECG data (FECGSYNDB) and clinical ECG data (NIFECGDB, PCDB) are used to evaluate the extraction performance of the proposed framework. The statistical and visual results demonstrate that our method outperforms the other state-of-the-art algorithms in the literature. Specifically, on the FECGSYNDB, the mean squared error (MSE), signal-to-noise ratio (SNR), correlation coefficient (R) and F1-score of our method are 0.16, 7.94, 0.95 and 98.89%, respectively. The F1-score on the NIFECGDB reaches 98.62%. The value of the F1-score on the PCDB is 98.62%.

CONCLUSION

As opposed to the existing algorithms being restricted to fetal QRS complex detection, the proposed framework can preserve the morphological features of the extracted fetal ECG signal well, which could support medical diagnoses based on the morphology of the fetal ECG signal.

Nature inspired method for noninvasive fetal ECG extraction

This paper introduces a novel algorithm for effective and accurate extraction of non-invasive fetal electrocardiogram (NI-fECG). In NI-fECG based monitoring, the useful signal is measured along with other signals generated by the pregnant women's body, especially maternal electrocardiogram (mECG). These signals are more distinct in magnitude and overlap in time and frequency domains, making the fECG extraction extremely challenging. The proposed extraction method combines the Grey wolf algorithm (GWO) with sequential analysis (SA). This innovative combination, forming the GWO-SA method, optimises the parameters required to create a template that matches the mECG, which leads to an accurate elimination of the said signal from the input composite signal. The extraction system was tested on two databases consisting of real signals, namely, Labour and Pregnancy. The databases used to test the algorithms are available on a server at the generalist repositories (figshare) integrated with Matonia et al. (Sci Data 7(1):1-14, 2020). The results show that the proposed method extracts the fetal ECG signal with an outstanding efficacy. The efficacy of the results was evaluated based on accurate detection of the fQRS complexes. The parameters used to evaluate are as follows: accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and F1 score. Due to the stochastic nature of the GWO algorithm, ten individual runs were performed for each record in the two databases to assure stability as well as repeatability. Using these parameters, for the Labour dataset, we achieved an average ACC of 94.60%, F1 of 96.82%, SE of 97.49%, and PPV of 98.96%. For the Pregnancy database, we achieved an average ACC of 95.66%, F1 of 97.44%, SE of 98.07%, and PPV of 97.44%. The obtained results show that the fHR related parameters were determined accurately for most of the records, outperforming the other state-of-the-art approaches. The poorer quality of certain signals have caused deviation from the estimated fHR for certain records in the databases. The proposed algorithm is compared with certain well established algorithms, and has proven to be accurate in its fECG extractions.

Heuristic RNN-based Kalman filter for fetal electrocardiogram extraction

Fetal Electrocardiogram (FECG) analysis helps in diagnosis of fetal heart. Extracting FECG from composite abdominal signal that contains noises like maternal ECG (MECG), electrical interference etc is a topic of great research interest, and several approaches have been reported. The proposed method is Heuristic RNN-based Kalman Filter for Fetal Electrocardiogram Extraction (HRKFFEE) which is based on redundant noise and signal patterns in the residual signal of FECG and MECG. Two functional blocks are used in the proposed method. The first functional block is based on Heuristic RNN equipped with legacy Long Short-Term Memory (LSTM) for assembling a knowledgebase and the second functional block is RNN-based Kalman filter. Upon testing, the proposed method delivers better average values of accuracy, F Score, Precision and Specificity as 93.118%, 93.106%, 92.9495 % and 92.98% respectively.

Wearable Fetal ECG Monitoring System from Abdominal Electrocardiography Recording

Fetal electrocardiography (ECG) monitoring during pregnancy can provide crucial information for assessing the fetus’s health status and making timely decisions. This paper proposes a portable ECG monitoring system to record the abdominal ECG (AECG) of the pregnant woman, comprising both maternal ECG (MECG) and fetal ECG (FECG), which could be applied to fetal heart rate (FHR) monitoring at the home setting. The ECG monitoring system is based on data acquisition circuits, data transmission module, and signal analysis platform, which consists of low input-referred noise, high input impedance, and high resolution. The combination of the adaptive dual threshold (ADT) and the independent component analysis (ICA) algorithm is employed to extract the FECG from the AECG signals. To validate the performance of the proposed system, AECG is recorded and analyzed of pregnant women in three different postures (supine, seated, and standing). The result shows that the proposed system can record the AECG in different postures with good signal quality and high accuracy in fetal ECG and heart rate information. Sensitivity (Se), positive predictive accuracy (PPV), accuracy (ACC), and their harmonic mean (F1) are utilized as the metrics to evaluate the performance of the fetal QRS (fQRS) complexes extraction. The average Se, PPV, ACC, and F1 score are 99.62%, 97.90%, 97.40%, and 98.66% for the fQRS complexes extraction,, respectively. This paper shows the proposed system has a promising application in fetal health monitoring.

A Deep Learning Approach for the Assessment of Signal Quality of Non-Invasive Foetal Electrocardiography

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.

Optimization of adaptive filter control parameters for non-invasive fetal electrocardiogram extraction

This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters of different hybrid systems used for non-invasive fetal electrocardiogram (fECG) extraction. The tested hybrid systems consist of two different blocks, first for maternal component estimation and second, so-called adaptive block, for maternal component suppression by means of an adaptive algorithm (AA). Herein, we tested and optimized four different AAs: Adaptive Linear Neuron (ADALINE), Standard Least Mean Squares (LMS), Sign-Error LMS, Standard Recursive Least Squares (RLS), and Fast Transversal Filter (FTF). The main criterion for optimal parameter selection was the F1 parameter. We conducted experiments using real signals from publicly available databases and those acquired by our own measurements. Our optimization method enabled us to find the corresponding optimal settings for individual adaptive block of all tested hybrid systems which improves achieved results. These improvements in turn could lead to a more accurate fetal heart rate monitoring and detection of fetal hypoxia. Consequently, our approach could offer the potential to be used in clinical practice to find optimal adaptive filter settings for extracting high quality fetal ECG signals for further processing and analysis, opening new diagnostic possibilities of non-invasive fetal electrocardiography.

Measurement of the cardiac time intervals of the fetal ECG utilising a computerised algorithm: A retrospective observational study

Objective

Establish whether the reliable measurement of cardiac time intervals of the fetal ECG can be automated and to address whether this approach could be used to investigate large datasets.

Design

Retrospective observational study.

Setting

Teaching hospitals in London UK, Nottingham UK and New York USA.

Participants

Singleton pregnancies with no known fetal abnormality.

Methods

Archived fetal ECG's performed using the MonicaAN24 monitor. A single ECG (PQRST) complex was generated from 5000 signal-averaged beats and electrical cardiac time intervals measured in an automated way and manually.

Main Outcome measure

Validation of a newly developed algorithm to measure the cardiac time intervals of the fetal ECG.

Results

188/236 (79.7%) subjects with fECGs of suitable signal:noise ratio were included for analysis comparing manual with automated measurement. PR interval was measured in 173/188 (92%), QRS complex in 170/188 (90%) and QT interval in 123/188 (65.4%). PR interval was 107.6 (12.07) ms [mean(SD)] manual vs 109.11 (14.7) ms algorithm. QRS duration was 54.72(6.35) ms manual vs 58.34(5.73) ms algorithm. QT-interval was 268.93 (21.59) ms manual vs 261.63 (36.16) ms algorithm. QTc was 407.5(32.71) ms manual vs 396.4 (54.78) ms algorithm. The QRS-duration increased with gestational age in both manual and algorithm measurements.

Conclusion

Accurate measurement of fetal ECG cardiac time intervals can be automated with potential application to interpretation of larger datasets.

A Novel Cluster-Based Method for Single-channel Fetal Electrocardiogram Detection

Fetal electrocardiography (FECG) is a promising technology for non-invasive fetal monitoring. However, due to the low amplitude and non-stationary characteristics of the FECG signal, it is difficult to extract it from maternal abdominal signals. Moreover, most FECG extraction methods are based on multiple channels, which make it difficult to achieve fetal monitoring outside the clinic. This paper proposes an efficient cluster-based method for accurate FECG extraction and fetal QRS detection only using one channel signal. We designed min-max-min group as the basis for feature extraction. The extracted features are used to distinguish the different components of the abdominal signal, and finally extract the FECG signal. To verify the effectiveness of our algorithm, we conducted experiments on a public dataset and a dataset record from the Tongji Hospital. Experimental results show that our method can achieve an accuracy rate of more than 96% which is better than other algorithms.

Development of a Home-based Fetal Electrocardiogram (ECG) Monitoring System

We develop a novel wearable fetal electrocardiogram (fECG) monitoring system consisting of an abdominal patch that communicates with a smart device. The system has two main components: the fetal patch and the monitoring app. The fetal patch has electronics and recording electrodes fabricated on a hybrid flexible-rigid platform while the Android app is developed for a wide range of applications. The patch collects the abdominal ECG (aECG) signals that are sent to the smart device app via secure Bluetooth Low Energy (BLE) communication. The app software connects to a cloud server where processing and extraction algorithms are executed for real-time fECG extraction and fetal heartrate (fHR) calculation from the collected raw data. We have validated the algorithms and real-time data recordings on pregnant subjects yielding promising results. Our system has the potential to transform the currently used fetal monitoring system to an effective distanced and telematernity care.

Evaluation of Mesh and Sensor Resolution for Finite Element Modeling of Non-Invasive Fetal ECG Signals

Non-invasive fetal electrocardiography (NI-FECG) is an emerging tool with novel diagnostic potential for monitoring fetal wellbeing using electrical signals acquired from the maternal abdomen. However, variations in the geometric structure and conductivity of maternal-fetal tissues have been shown to affect the reliability of NI-FECG signals. Previous studies have utilized detailed finite element models to simulate these impacts, however this approach is computationally expensive. In this study, we investigate a range of mesh and sensor resolutions to determine an optimal trade-off between computational cost and modeling accuracy for simulating NI-FECG signals. Our results demonstrate that an optimal refinement of mesh resolution provides comparable accuracy to a detailed reference solution while requiring approximately 12 times less computation time and one-third of the memory usage. Furthermore, positioning simulated sensors at a 20 mm grid spacing provides a sufficient representation of abdominal surface potentials. These findings represent default parameters to be used in future simulations of NI-FECG signals. Code for the model utilized in this work is available under an open-source GPL license as part of the fecgsyn toolbox.Clinical Relevance- Simulating NI-FECG signals provides the opportunity to study the effects of sensor placement and maternal-fetal anatomic variations in a controlled setting. This work has relevance in determining default parameters for efficiently performing these simulations.

Fetal ECG extraction using short time Fourier transform and generative adversarial networks

Objective.Fetal ECG (FECG) plays an important role in fetal monitoring. However, the abdominal ECG (AECG) recorded at the maternal abdomen is affected by various noises, making the extraction of FECG a challenging task. The main objective is to present a novel approach to FECG extraction using short time Fourier transform (STFT) and generative adversarial networks (GAN).Methods.Firstly, the AECG signals are transformed from one-dimensional (1D) time domain to two-dimensional (2D) time-frequency domain by using the STFT. Secondly, the 2D-STFT coefficients of FECG are estimated by the GAN model in the time-frequency domain. Finally, after the inverse STFT, the FECG can be reconstructed in the time domain.Main results.Experimental results on two databases demonstrate the effectiveness of the proposed method. Specifically, the SE, PPV andF₁of the proposed method on PCDB are 92.37 ± 3.78%, 93.69 ± 3.96% and 93.02 ± 3.81%, respectively. And the SE, PPV andF₁on ADFECGDB are 90.32 ± 10.70%, 89.79 ± 9.26% and 90.05 ± 9.81%, respectively.Significance.Unlike the previous studies based on the elimination of maternal ECG in the 1D time domain, the novelty of the proposed method relies on extracting the FECG directly from the AECG in the 2D time-frequency domain. It sheds some light to the topic of FECG extraction.

Fetal ECG Extraction from Maternal ECG using Attention-based CycleGAN

A non-invasive fetal electrocardiogram (FECG) is used to monitor the electrical pulse of the fetal heart. Decomposing the FECG signal from the maternal ECG (MECG) is a blind source separation problem, which is hard due to the low amplitude of the FECG, the overlap of R waves, and the potential exposure to noise from different sources. Traditional decomposition techniques, such as adaptive filters, require tuning, alignment, or pre-configuration, such as modeling the noise or desired signal to map the MECG to the FECG. The high correlation between maternal and fetal ECG fragments decreases the performance of convolution layers. Therefore, the masking region of interest based on the attention mechanism was performed to improve the signal generators' precision. The sine activation function was also used to retain more details when converting two signal domains. Three available datasets from the Physionet, including the A&D FECG, NI-FECG, and NI-FECG challenge, and one synthetic dataset using FECGSYN toolbox, were used to evaluate the performance. The proposed method could map an abdominal MECG to a scalp FECG with an average of 98% R-Square [CI 95%: 97%, 99%] as the goodness of fit on the A&D FECG dataset. Moreover, it achieved 99.7 % F1-score [CI 95%: 97.8-99.9], 99.6% F1-score [CI 95%: 98.2%, 99.9%] and 99.3% F1-score [CI 95%: 95.3%, 99.9%] for fetal QRS detection on the A&D FECG, NI-FECG and NI-FECG challenge datasets, respectively. Also, the distortion was in the very good and good ranges. These results are comparable to the state-of-the-art results; thus, the proposed algorithm has the potential to be used for high-performance signal-to-signal conversion.

A novel algorithm based on ensemble empirical mode decomposition for non-invasive fetal ECG extraction

Non-invasive fetal electrocardiography appears to be one of the most promising fetal monitoring techniques during pregnancy and delivery nowadays. This method is based on recording electrical potentials produced by the fetal heart from the surface of the maternal abdomen. Unfortunately, in addition to the useful fetal electrocardiographic signal, there are other interference signals in the abdominal recording that need to be filtered. The biggest challenge in designing filtration methods is the suppression of the maternal electrocardiographic signal. This study focuses on the extraction of fetal electrocardiographic signal from abdominal recordings using a combination of independent component analysis, recursive least squares, and ensemble empirical mode decomposition. The method was tested on two databases, the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations and the PhysioNet Challenge 2013 database. The evaluation was performed by the assessment of the accuracy of fetal QRS complexes detection and the quality of fetal heart rate determination. The effectiveness of the method was measured by means of the statistical parameters as accuracy, sensitivity, positive predictive value, and F1-score. Using the proposed method, when testing on the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations database, accuracy higher than 80% was achieved for 11 out of 12 recordings with an average value of accuracy 92.75% [95% confidence interval: 91.19-93.88%], sensitivity 95.09% [95% confidence interval: 93.68-96.03%], positive predictive value 96.36% [95% confidence interval: 95.05-97.17%] and F1-score 95.69% [95% confidence interval: 94.83-96.35%]. When testing on the Physionet Challenge 2013 database, accuracy higher than 80% was achieved for 17 out of 25 recordings with an average value of accuracy 78.24% [95% confidence interval: 73.44-81.85%], sensitivity 81.79% [95% confidence interval: 76.59-85.43%], positive predictive value 87.16% [95% confidence interval: 81.95-90.35%] and F1-score 84.08% [95% confidence interval: 80.75-86.64%]. Moreover, the non-invasive ST segment analysis was carried out on the records from the Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeats Annotations database and achieved high accuracy in 7 from in total of 12 records (mean values μ < 0.1 and values of ±1.96σ < 0.1).

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach-Part I: Cardiac Signals

Advanced signal processing methods are one of the fastest developing scientific and technical areas of biomedical engineering with increasing usage in current clinical practice. This paper presents an extensive literature review of the methods for the digital signal processing of cardiac bioelectrical signals that are commonly applied in today's clinical practice. This work covers the definition of bioelectrical signals. It also covers to the extreme extent of classical and advanced approaches to the alleviation of noise contamination such as digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation and wavelet transform.

Detection of fetal arrhythmia by adaptive single channel electrocardiogram extraction

Fetal arrhythmia, the abnormal heartbeat of a fetus is broadly classified as tachy arrhythmia (too fast > 160 beats/min) and brady arrhythmia (too slow < 120 beats/min). Detection of this irregular heart beat rhythm of the fetus during pregnancy is still a challenging task for the clinicians. Heart rate detection through electrocardiography has always been accurate for identifying cardiac defect in humans. Adult ECG has achieved several developments in the modern medicine whereas noninvasive fetal ECG (FECG) continues to be a big challenge. Automatic detection of fetal heart rate is vital for monitoring the unborn infant during pregnancy. The non-invasive placement of electrodes over the abdomen region of pregnant women records the ECG signal of both mother and fetus. The arrhythmia affected FECG signals (n = 14) are processed from the physionet database. This raw ECG signal is preprocessed using a Savitzky-Golay filter and symlet wavelet transform to remove the basic noises. Adaptive recursive least square filter is preferably chosen for extracting the FECG, using mother's thorax ECG as a reference. An accurate PQRST wave-shape of the FECG is required for the proper diagnosis of fetal cardiac defects. Using a single channel abdominal ECG signal, the proposed work generates extracted fetal ECG and an automated visual display of fetal heart rate. The presence of arrhythmia and fetal distress can be analyzed through fetal heart rate display and abnormal conductivity of PQRST wave respectively. We have analyzed fetal arrhythmias through ECG extraction and the same was compared with the echocardiograph results given by pediatric cardiologist. This study helps to identify the fetal distress at early gestational age that helps the obstetricians to make quick decisions before or immediately after delivery.

A dilated inception CNN-LSTM network for fetal heart rate estimation

Objective. Fetal heart rate (HR) monitoring is routinely used during pregnancy and labor to assess fetal well-being. The noninvasive fetal electrocardiogram (ECG), obtained by electrodes on the maternal abdomen, is a promising alternative to standard fetal monitoring. Subtraction of the maternal ECG from the abdominal measurements results in fetal ECG signals, in which the fetal HR can be determined typically through R-peak detection. However, the low signal-to-noise ratio and the nonstationary nature of the fetal ECG make R-peak detection a challenging task.Approach. We propose an alternative approach that instead of performing R-peak detection employs deep learning to directly determine the fetal HR from the extracted fetal ECG signals. We introduce a combination of dilated inception convolutional neural networks (CNN) with long short-term memory networks to capture both short-term and long-term temporal dynamics of the fetal HR. The robustness of the method is reinforced by a separate CNN-based classifier that estimates the reliability of the outcome.Main results. Our method achieved a positive percent agreement (within 10% of the actual fetal HR value) of 97.3% on a dataset recorded during labor and 99.6% on set-A of the 2013 Physionet/Computing in Cardiology Challenge exceeding top-performing state-of-the-art algorithms from the literature.Significance. The proposed method can potentially improve the accuracy and robustness of fetal HR extraction in clinical practice.

A non-invasive multimodal foetal ECG-Doppler dataset for antenatal cardiology research

Non-invasive foetal electrocardiography (fECG) continues to be an open topic for research. The development of standard algorithms for the extraction of the fECG from the maternal electrophysiological interference is limited by the lack of publicly available reference datasets that could be used to benchmark different algorithms while providing a ground truth for foetal heart activity when an invasive scalp lead is unavailable. In this work, we present the Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research (NInFEA), the first open-access multimodal early-pregnancy dataset in the field that features simultaneous non-invasive electrophysiological recordings and foetal pulsed-wave Doppler (PWD). The dataset is mainly conceived for researchers working on fECG signal processing algorithms. The dataset includes 60 entries from 39 pregnant women, between the 21st and 27th week of gestation. Each dataset entry comprises 27 electrophysiological channels (2048 Hz, 22 bits), a maternal respiration signal, synchronised foetal trans-abdominal PWD and clinical annotations provided by expert clinicians during signal acquisition. MATLAB snippets for data processing are also provided.

A review of fetal cardiac monitoring, with a focus on low- and middle-income countries

There is limited evidence regarding the utility of fetal monitoring during pregnancy, particularly during labor and delivery. Developed countries rely on consensus 'best practices' of obstetrics and gynecology professional societies to guide their protocols and policies. Protocols are often driven by the desire to be as safe as possible and avoid litigation, regardless of the cost of downstream treatment. In high-resource settings, there may be a justification for this approach. In low-resource settings, in particular, interventions can be costly and lead to adverse outcomes in subsequent pregnancies. Therefore, it is essential to consider the evidence and cost of different fetal monitoring approaches, particularly in the context of treatment and care in low-to-middle income countries. This article reviews the standard methods used for fetal monitoring, with particular emphasis on fetal cardiac assessment, which is a reliable indicator of fetal well-being. An overview of fetal monitoring practices in low-to-middle income counties, including perinatal care access challenges, is also presented. Finally, an overview of how mobile technology may help reduce barriers to perinatal care access in low-resource settings is provided.

A Novel Tool for Non-Invasive Fetal Electrocardiography Research: the NInFEA Dataset

In this work, a novel open-source dataset for noninvasive fetal electrocardiography research is presented. It is composed of 60 high-quality electrophysiological recordings acquired between the 21st and the 27th weeks of gestation. For each acquisition, whose average duration is 30.5 s, 24 unipolar abdominal leads and three bipolar thoracic leads were included, along with a maternal respiration signal collected by a thoracic resistive belt. The chosen electrodes positioning map allows reproducing up to ten setups presented in the scientific literature. Each biopotential recording was acquired synchronously with the corresponding fetal cardiac pulsed-wave Doppler (PWD) signal, to provide complete information about the fetal cardiac cycle, both from the electrical and mechanical point of view.This is the first dataset allowing the non-invasive fetal ECG analysis even in early pregnancies with a ground truth about the fetal heart activity, given by the PWD signal. For this reason, it can be used to assess fetal ECG extraction algorithms requiring multiple channels, eventually including maternal references. This dataset is being released on Physionet by the end of June 2020 and will be continuously improved in the framework of the Non-Invasive Fetal ECG Analysis (NInFEA) project of the University of Cagliari (Italy).

Deep Convolutional Long Short-Term Memory Network for Fetal Heart Rate Extraction

Fetal electrocardiography is a valuable alternative to standard fetal monitoring. Suppression of the maternal electrocardiogram (ECG) in the abdominal measurements, results in fetal ECG signals, from which the fetal heart rate (HR) can be determined. This HR detection typically requires fetal R-peak detection, which is challenging, especially during low signal-to-noise ratio periods, caused for example by uterine activity. In this paper, we propose the combination of a convolutional neural network and a long short-term memory network that directly predicts the fetal HR from multichannel fetal ECG. The network is trained on a dataset, recorded during labor, while the performance of the method is evaluated both on a test dataset and on set-A of the 2013 Physionet /Computing in Cardiology Challenge. The algorithm achieved a positive percent agreement of 92.1% and 98.1% for the two datasets respectively, outperforming a top-performing state-of-the-art signal processing algorithm.

Wavelet denoising as a post-processing enhancement method for non-invasive foetal electrocardiography

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.

Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations

Monitoring fetal heart rate (FHR) variability plays a fundamental role in fetal state assessment. Reliable FHR signal can be obtained from an invasive direct fetal electrocardiogram (FECG), but this is limited to labour. Alternative abdominal (indirect) FECG signals can be recorded during pregnancy and labour. Quality, however, is much lower and the maternal heart and uterine contractions provide sources of interference. Here, we present ten twenty-minute pregnancy signals and 12 five-minute labour signals. Abdominal FECG and reference direct FECG were recorded simultaneously during labour. Reference pregnancy signal data came from an automated detector and were corrected by clinical experts. The resulting dataset exhibits a large variety of interferences and clinically significant FHR patterns. We thus provide the scientific community with access to bioelectrical fetal heart activity signals that may enable the development of new methods for FECG signals analysis, and may ultimately advance the use and accuracy of abdominal electrocardiography methods.

Investigation of Methods to Extract Fetal Electrocardiogram from the Mother's Abdominal Signal in Practical Scenarios

Monitoring of fetal electrocardiogram (fECG) would provide useful information about fetal wellbeing as well as any abnormal development during pregnancy. Recent advances in flexible electronics and wearable technologies have enabled compact devices to acquire personal physiological signals in the home setting, including those of expectant mothers. However, the high noise level in the daily life renders long-entrenched challenges to extract fECG from the combined fetal/maternal ECG signal recorded in the abdominal area of the mother. Thus, an efficient fECG extraction scheme is a dire need. In this work, we intensively explored various extraction algorithms, including template subtraction (TS), independent component analysis (ICA), and extended Kalman filter (EKF) using the data from the PhysioNet 2013 Challenge. Furthermore, the modified data with Gaussian and motion noise added, mimicking a practical scenario, were utilized to examine the performance of algorithms. Finally, we combined different algorithms together, yielding promising results, with the best performance in the F1 score of 92.61% achieved by an algorithm combining ICA and TS. With the data modified by adding different types of noise, the combination of ICA-TS-ICA showed the highest F1 score of 85.4%. It should be noted that these combined approaches required higher computational complexity, including execution time and allocated memory compared with other methods. Owing to comprehensive examination through various evaluation metrics in different extraction algorithms, this study provides insights into the implementation and operation of state-of-the-art fetal and maternal monitoring systems in the era of mobile health.

Model based estimation of QT intervals in non-invasive fetal ECG signals

The end timing of T waves in fetal electrocardiogram (fECG) is important for the evaluation of ST and QT intervals which are vital markers to assess cardiac repolarization patterns. Monitoring malignant fetal arrhythmias in utero is fundamental to care in congenital heart anomalies preventing perinatal death. Currently, reliable detection of end of T waves is possible only by using fetal scalp ECG (fsECG) and fetal magnetocardiography (fMCG). fMCG is expensive and less accessible and fsECG is an invasive technique available only during intrapartum period. Another safer and affordable alternative is the non-invasive fECG (nfECG) which can provide similar assessment provided by fsECG and fMECG but with less accuracy (not beat by beat). Detection of T waves using nfECG is challenging because of their low amplitudes and high noise. In this study, a novel model-based method that estimates the end of T waves in nfECG signals is proposed. The repolarization phase has been modeled as the discharging phase of a capacitor. To test the model, fECG signals were collected from 58 pregnant women (age: (34 ± 6) years old) bearing normal and abnormal fetuses with gestational age (GA) 20-41 weeks. QT and QTc intervals have been calculated to test the level of agreement between the model-based and reference values (fsECG and Doppler Ultrasound (DUS) signals) in normal subjects. The results of the test showed high agreement between model-based and reference values (difference < 5%), which implies that the proposed model could be an alternative method to detect the end of T waves in nfECG signals.

The Influence of Vectorcardiogram Orientation on the T/QRS Ratio Obtained Via Non-Invasive Fetal ECG

Non-invasive fetal electrocardiography (NI-FECG) is an emerging technology that demonstrates potential for providing novel physiological information compared to traditional ultrasound-based cardiotocography (CTG). However, few studies have investigated the reliability of signal features derived via this technique for diagnostic use. One feature of NI-FECG recordings proposed for the purpose of identifying fetal distress is the T/QRS ratio, which has been indicated to change in response to fetal hypoxia. As the T/QRS ratio measures characteristics of the heart's electrical activity in 3D space (represented as the vectorcardiogram), it is critical to understand how changes in the vectorcardiogram orientation may influence the reliability of this feature. To study this influence, this work simulates NI-FECG recordings using eight finite element models of the maternal-fetal anatomy and calculates the T/QRS ratio for a range of vector-cardiogram orientations and sensor positions. To quantify the potential for T/QRS ratio estimation error in real world data, these results are compared to those observed in a homogeneous volume conductor model, as assumed by many existing signal processing techniques. Our results demonstrate that the fetal vectorcardiogram orientation has a significant influence on the reliability of the T/QRS ratio obtained via NI-FECG. Varying the vectorcardiogram orientation through a range of -30 to +30 degrees along each coordinate axis results in the potential for the T/QRS ratio to be underestimated by up to 94% and overestimated by up to 240% if a homogeneous volume conductor model is assumed. Furthermore, we find that the sensor positioning on the maternal abdomen strongly affects the range of the T/QRS ratio estimation error. These results confirm that further study must be undertaken to determine the relationship between the physiological and signal processing domains before utilizing the T/QRS ratio obtained via NI-FECG for diagnostic purposes.

End-to-end trained encoder-decoder convolutional neural network for fetal electrocardiogram signal denoising

OBJECTIVE

Non-invasive fetal electrocardiography has the potential to provide vital information for evaluating the health status of the fetus. However, the low signal-to-noise ratio of the fetal electrocardiogram (ECG) impedes the applicability of the method in clinical practice. Quality improvement of the fetal ECG is of great importance for providing accurate information to enable support in medical decision-making. In this paper we propose the use of artificial intelligence for the task of one-channel fetal ECG enhancement as a post-processing step after maternal ECG suppression.

APPROACH

We propose a deep fully convolutional encoder-decoder framework, learning end-to-end mappings from noise-contaminated fetal ECGs to clean ones. Symmetric skip-layer connections are used between corresponding convolutional and transposed convolutional layers to help recover the signal details.

MAIN RESULTS

Experiments on synthetic data show an average improvement of 7.5 dB in the signal-to-noise ratio (SNR) for input SNRs in the range of  -15 to 15 dB. Application of the method with real signals and subsequent ECG interval analysis demonstrates a root mean square error of 9.9 and 14 ms for the PR and QT intervals, respectively, when compared with simultaneous scalp measurements. The proposed network can achieve substantial noise removal on both synthetic and real data. In cases of highly noise-contaminated signals some morphological features might be unreliably reconstructed.

SIGNIFICANCE

The presented method has the advantage of preserving individual variations in pulse shape and beat-to-beat intervals. Moreover, no prior knowledge on the power spectra of the noise or the pulse locations is required.

Single-lead noninvasive fetal ECG extraction by means of combining clustering and principal components analysis

Early detection of potential hazards in the fetal physiological state during pregnancy and childbirth is very important. Noninvasive fetal electrocardiogram (FECG) can be extracted from the maternal abdominal signal. However, due to the interference of maternal electrocardiogram and other noises, the task of extraction is challenging. This paper introduces a novel single-lead noninvasive fetal electrocardiogram extraction method based on the technique of clustering and PCA. The method is divided into four steps: (1) pre-preprocessing; (2) fetal QRS complexes and maternal QRS complexes detection based on k-means clustering algorithm with the feature of max-min pairs; (3) FQRS correction step is to improve the performance of step two; (4) template subtraction based on PCA is introduced to extract FECG waveform. To verify the performance of the proposed algorithm, two clinical open-access databases are used to check the performance of FQRS detection. As a result, the method proposed shows the average PPV of 95.35%, Se of 96.23%, and F1-measure of 95.78%. Furthermore, the robustness test is carried out on an artificial database which proves that the algorithm has certain robustness in various noise environments. Therefore, this method is feasible and reliable to detect fetal heart rate and extract FECG. Graphical abstract Early detection of potential hazards in the fetal physiological state during pregnancy and childbirth is very important. Noninvasive fetal electrocardiogram (FECG) can be extracted from maternal abdominal signal. However, due to the interference of maternal electrocardiogram and other noises, the task of extraction is challenging. This paper introduces a novel single-lead noninvasive fetal electrocardiogram extraction method based on the technique of clustering and PCA. The method is divided into four steps: (1) pre-preprocessing; (2) fetal QRS complexes and maternal QRS complexes detection based on k-means clustering algorithm with the feature of max-min pairs; (3) FQRS correction step is to improve the performance of step two; (4) template subtraction based on PCA is introduced to extract FECG waveform. To verify the performance of algorithm, two clinical open-access databases are used to check the performance of FQRS detection. As a result, the method proposed shows the average PPV of 95.35%, Se of 96.23%, and F1-measure of 95.78%. Furthermore, the robustness test is carried out on an artificial database which proves that the algorithm has certain robustness in various noise environments. Therefore, this method is feasible and reliable to detect fetal heart rate and extract FECG.

Recovery of the fetal electrocardiogram for morphological analysis from two trans-abdominal channels via optimal shrinkage

OBJECTIVE

We propose a novel algorithm to recover fetal electrocardiogram (ECG) for both the fetal heart rate analysis and morphological analysis of its waveform from two or three trans-abdominal maternal ECG channels.

APPROACH

We design an algorithm based on the optimal-shrinkage under the wave-shape manifold model. For the fetal heart rate analysis, the algorithm is evaluated on publicly available database, 2013 PhyioNet/Computing in Cardiology Challenge, set A (CinC2013). For the morphological analysis, we analyze CinC2013 and another publicly available database, non-invasive fetal ECG arrhythmia database (nifeadb), and propose to simulate semi-real databases by mixing the MIT-BIH normal sinus rhythm database and MITDB arrhythmia database.

MAIN RESULTS

For the fetal R peak detection, the proposed algorithm outperforms all algorithms under comparison. For the morphological analysis, the algorithm provides an encouraging result in recovery of the fetal ECG waveform, including PR, QT and ST intervals, even when the fetus has arrhythmia, both in real and simulated databases.

SIGNIFICANCE

To the best of our knowledge, this is the first work focusing on recovering the fetal ECG for morphological analysis from two or three channels with an algorithm potentially applicable for continuous fetal electrocardiographic monitoring, which creates the potential for long term monitoring purpose.

Fetal electrocardiography extraction with residual convolutional encoder-decoder networks

In the context of fetal monitoring, non-invasive fetal electrocardiography is an alternative approach to the traditional Doppler ultrasound technique. However, separating the fetal electrocardiography (FECG) component from the abdominal electrocardiography (AECG) remains a challenging task. This is mainly due to the interference from maternal electrocardiography, which has larger amplitude and overlaps with the FECG in both temporal and frequency domains. The main objective is to present a novel approach to FECG extraction by using a deep learning strategy from single-channel AECG recording. A residual convolutional encoder-decoder network (RCED-Net) is developed for this task of FECG extraction. The single-channel AECG recording is the input to the RCED-Net. And the RCED-Net extracts the feature of AECG and directly outputs the estimate of FECG component in the AECG recording. The AECG recordings from two different databases are collected to illustrate the efficiency of the proposed method. And the achieved results show that the proposed technique exhibits the best performance when compared to the existing methods in the literature. This work is a proof of concept that the proposed method could effectively extract the FECG component from AECG recordings. The focus on single-channel FECG extraction technique contributes to the commercial applications for long-term fetal monitoring.

QRStree: A prefix tree-based model to fetal QRS complexes detection

Non-invasive fetal electrocardiography (NI-FECG) plays an important role in fetal heart rate (FHR) measurement during the pregnancy. However, despite the large number of methods that have been proposed for adult ECG signal processing, the analysis of NI-FECG remains challenging and largely unexplored. In this study, we propose a prefix tree-based framework, called QRStree, for FHR measurement directly from the abdominal ECG (AECG). The procedure is composed of three stages: Firstly, a preprocessing stage is employed for noise elimination. Secondly, the proposed prefix tree-based method is used for fetal QRS complexes (FQRS) detection. Finally, a correction stage is applied for false positive and false negative correction. The novelty of the framework relies on using the range of FHR to establish the connections between the FQRS. The consecutive FQRS can be considered as strings composed of alphabet items, thus we can use the prefix tree to store them. A vertex of the tree contains an alphabet, thus a path of the tree gives a string. Such that, by storing the connections of the FQRS into the prefix tree structure, the problem of FQRS detection converts to a problem of optimal path selection. Specifically, after selecting the optimal path of the tree, the nodes in the optimal path are collected as detected FQRS. Since the prefix tree can cover every possible combination of the FQRS candidates, it has the potential to reduce the occurrence of miss detections. Results on two different databases show that the proposed method is effective in FHR measurement from single-channel AECG. The focus on single-channel FHR measurement facilitates the long-term monitoring for healthcare at home.

A Review of Signal Processing Techniques for Non-Invasive Fetal Electrocardiography

Fetal electrocardiography (fECG) is a promising alternative to cardiotocography continuous fetal monitoring. Robust extraction of the fetal signal from the abdominal mixture of maternal and fetal electrocardiograms presents the greatest challenge to effective fECG monitoring. This is mainly due to the low amplitude of the fetal versus maternal electrocardiogram and to the non-stationarity of the recorded signals. In this review, we highlight key developments in advanced signal processing algorithms for non-invasive fECG extraction and the available open access resources (databases and source code). In particular, we highlight the advantages and limitations of these algorithms as well as key parameters that must be set to ensure their optimal performance. Improving or combining the current or developing new advanced signal processing methods may enable morphological analysis of the fetal electrocardiogram, which today is only possible using the invasive scalp electrocardiography method.