Fetal Electrocardiogram Research Articles

New Features for the Detection of Fetal QRS Complexes in Non-Invasive Fetal Electrocardiograms.

Non-invasive fetal electrocardiography (NI-fECG) is a promising technique for continuous fetal heart rate (fHR) monitoring. However, the weak amplitude of the fetal electrocardiogram (fECG), and the presence of the dominant maternal ECG (mECG), makes it highly challenging to detect the fetal QRS (fQRS) complex, which is needed to obtain the fHR. This paper proposes a new method for automated fQRS detection from single-channel NI-fECG signals, without cancelling out the mECG. The proposed method leverages the different spectral behaviour exhibited by mECG and fECG signals. Fetal R-peaks are detected using a hybrid combination of k-means clustering with time and time-frequency features extracted from pre-processed NI-fECG recordings. The performance of our method is evaluated using real and synthetic signals from publicly available datasets, achieving a best of 96.3% sensitivity and 90.4% F1 score. The results obtained demonstrates the effectiveness of the proposed method for the detection of fQRS complexes with high sensitivity and low computational complexity.

FECG compressed sensing mode based on joint block sparsity

The development of medical Internet of Things technology has enabled the rise of telemedicine. For medical signals, real-time transmission, accuracy of signals and portability of equipment are required to be strict. The telemedicine system based on Compressed Sensing (CS) technology can avoid sampling a large number of redundant information, and reduce the complexity of data processing and computing time. However, the performance of existing Compressed Sensing algorithms in processing and reconstructing medical signals is poor. It is difficult to meet the strict requirements. In this paper, the sparse representation method which is most suitable for Fetal Electrocardiogram (FECG) signals is obtained by comparison experiment. And a Logistics-Tent Sine Bernoulli Measurement Matrix (LTSBM) construction algorithm is proposed for more convenient hardware applications. Based on the characteristics of FECG signals, a Joint Block Multiple Orthogonal Least Squares (JBMOLS) algorithm is proposed to improve the reconstruction performance in this paper. Combined with the above algorithms, the FECG compressed sensing mode is constituted, which is used to improve the compression, sampling and reconstruction performance of FECG signals. The experimental results show that, compared with traditional compressed sensing technology, the FECG compressed sensing mode improves the overall signal reconstruction performance, the running time is greatly reduced, and the practicability is stronger.

One-Dimensional W-NETR for Non-Invasive Single Channel Fetal ECG Extraction.

Fetal cardiac monitoring is very helpful in the early detection of the potential risk of fetal cardiac abnormalities, which enables prompt preventative care and ensures safe births. As a result, it is crucial to regularly check on the embryonic heart. Methods of non-invasively fetal ECG extraction from maternal abdominal ECG signal are thoroughly discussed. Although fetal signals are generally obscured by maternal ECG signals and noise, extracting a clean fetal ECG is a significant difficulty. The majority of techniques for fetal ECG extraction include many extraction steps. We describe a unique method for splitting a single-channel maternal abdominal ECG into maternal and fetus ECG employing two parallel U-nets with transformer encoding, which we refer to as W-NEt TRansformers (W-NETR). Due to its enhanced capacity to simulate remote interactions and capture global context, the suggested pipeline utilizes the self-attention mechanism of the transformer. We tested the proposed pipeline on synthetic and real datasets and outperformed the current state-of-the-art deep learning models. The proposed model achieved the best results on both datasets for QRS detection precision, recall, and F1 scores. More specifically, it achieved F1 score of 99.88% and 98.9% on the real ADFECGDB and PCDB datasets, respectively. These encouraging results highlight the suggested W-NETR's effectiveness in precisely extracting the fetal ECG, which was achieved with high SSIM and PSNR values in the results. This provides the bed set for long-term maternal and fetal monitoring via portable devices as the proposed system performs real-time execution.

Beamforming-based algorithms for recovering information from fetal electrocardiographic sensors

We deal with the extraction of the fetal electrocardiography (ECG) signal from the raw ECG signals of the mother by the beamforming- based algorithms. The foetal ECG sensors bring out signals containing information from the pregnant mother and the infant. Detailed and separate signals are already provided by the foetal ECG instruments; but for some specific studies related to the infant conditions, it is necessary to improve the quality of the signal with a dedicated processing. In this paper, four techniques, with some enhancements, are proposed to perform the processing; we have applied the following techniques: Least Mean Square (LMS) with adaptive noise cancellation technique, Discrete Wavelet Transform (DWT)-based technique, Empirical Wavelet Transform (EWT) technique, and Multiple Signal Classification (MUSIC). The LMS and the MUSIC pertain to beamforming approach. The techniques were used to decompose and identify the different elements constituting the source signal (mother's signal) and noise cancellation by Multivariate Empirical Mode Decomposition (MEMD) technique. The signal was adaptively decomposed by LMS, DWT and MUSIC according to optimised parameters to extract some hidden components of the source signal, such as the foetal features, QRS, heartbeat etc. The results have showed that LMS, with enhancements, is more effective in identifying and removing useless noise. The techniques were applied to the ECG signal of a 30-year-old healthy pregnant woman, which allowed to verify their applicability. The present research leads to the below main contributions among others: separation of the ECG signal of the foetus from the mother, highlighting the functional state of the foetal heart rhythm (heart rate and heartbeat,) and this can show us if the foetal ECG has malfunctions.

Fetal Electrocardiogram: St Waveform Analysis in Intrapartum Surveillance (Analisi Del Tratto St Dell’elettrocardiografia Fetale In Travaglio Di Parto)

ST-analysis of the fetal electrocardiogram (ECG) (STAN (®)) combined with cardiotocography (CTG) for intrapartum fetal monitoring has been developed following many years of animal research. Changes in the ST-segment of the fetal ECG correlated with fetal hypoxia occurring during labor. In 1993 the first randomized controlled trial (RCT), comparing CTG with CTG + ST-analysis was published. STAN (®) was introduced for daily practice in 2000. To date, six RCTs have been performed, out of which five have been published. Furthermore, there are six published meta-analyses. The meta-analyses showed that CTG + ST-analysis reduced the risks of vaginal operative delivery by about 10% and fetal blood sampling by 40%. There are conflicting results regarding the effect on metabolic acidosis, much because of controveries about which RCTs should be included in a meta-analysis, and because of differences in methodology, execution and quality of the meta-analyses. Several cohort studies have been published, some showing significant decrease of metabolic acidosis after the introduction of ST-analysis. In this review, we discuss not only the scientific evidence from the RCTs and meta-analyses, but also the limitations of these studies. In conclusion, ST-analysis is effective in reducing operative vaginal deliveries and fetal blood sampling but the effect on neonatal metabolic acidosis is still under debate. Further research is needed to determine the place of ST-analysis in the labor ward for daily practice.

Independent component analysis algorithms for non-invasive fetal electrocardiography.

The independent component analysis (ICA) based methods are among the most prevalent techniques used for non-invasive fetal electrocardiogram (NI-fECG) processing. Often, these methods are combined with other methods, such adaptive algorithms. However, there are many variants of the ICA methods and it is not clear which one is the most suitable for this task. The goal of this study is to test and objectively evaluate 11 variants of ICA methods combined with an adaptive fast transversal filter (FTF) for the purpose of extracting the NI-fECG. The methods were tested on two datasets, Labour dataset and Pregnancy dataset, which contained real records obtained during clinical practice. The efficiency of the methods was evaluated from the perspective of determining the accuracy of detection of QRS complexes through the parameters of accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and harmonic mean between SE and PPV (F1). The best results were achieved with a combination of FastICA and FTF, which yielded mean values of ACC = 83.72%, SE = 92.13%, PPV = 90.16%, and F1 = 91.14%. Time of calculation was also taken into consideration in the methods. Although FastICA was ranked to be the sixth fastest with its mean computation time of 0.452 s, it had the best ratio of performance and speed. The combination of FastICA and adaptive FTF filter turned out to be very promising. In addition, such device would require signals acquired from the abdominal area only; no need to acquire reference signal from the mother's chest.

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

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. 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. 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). 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.

Fetal ECG extraction from maternal ECG using deeply supervised LinkNet++ model

Fetal heart monitoring and early disease detection using non-invasive fetal electrocardiograms (fECG) can help substantially to reduce infant death through improved diagnosis of Coronary Heart Disease (CHD) in the fetus. Despite the potential benefits, non-invasive fECG extraction from maternal abdominal ECG (mECG) is a challenging problem due to multiple factors such as the overlap of maternal and fetal R-peaks, low amplitude of fECG, and various systematic and environmental noises. Conventional fECG extraction techniques, such as adaptive filters, independent component analysis (ICA), empirical mode decomposition (EMD), etc., face various performance issues due to the fECG extraction challenges. In this paper, we proposed a novel deep learning architecture, LinkNet++ (motivated by the original LinkNet) to extract fECG from abdominal mECG automatically and efficiently using two different publicly available datasets. LinkNet++ is equipped with a feature-addition method to combine deep and shallow levels with residual blocks to overcome the limitations of U-Net and UNet++ models. It also has deep supervised and densely connected convolution blocks to overcome the limitations of the original LinkNet. The proposed LinkNet++ model was evaluated using fECG signal reconstruction and fetal QRS (fQRS) detection. As a signal-to-signal synthesis model, LinkNet++ performed very well in two real-life datasets and achieved 85.58% and 87.60% Pearson correlation coefficients (PCC) between the ground truth and predicted fECG on two datasets, respectively. In terms of fQRS detection, it also outperformed most of the previous works and showed excellent performance with more than 99% of F1 scores on both datasets. Our results indicate that the proposed model can potentially extract fECG non-invasively with excellent signal quality, thereby providing an excellent diagnostic tool for various fetal heart diseases.

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

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

A Small Scale Optically Pumped Fetal Magnetocardiography System.

Fetal magnetocardiography (fMCG) is considered the best technique for diagnosis of fetal arrhythmia. It is superior to more widely used methods such as fetal, fetal electrocardiography, and cardiotocography for evaluation of fetal rhythm. The combination of fMCG and fetal echocardiography can provide a more comprehensive evaluation of fetal cardiac rhythm and function than is currently possible. In this study, we demonstrate a practical fMCG system based on optically pumped magnetometers (OPMs). Seven pregnant women with uncomplicated pregnancies underwent fMCG at 26-36 weeks' gestation. The recordings were made using an OPM-based fMCG system and a person-sized magnetic shield. The shield is much smaller than a shielded room and provides easy access with a large opening that allows the pregnant woman to lie comfortably in a prone position. The data show no significant loss of quality compared to data acquired in a shielded room. Measurements of standard cardiac time intervals yielded the following results: PR = 104 ± 6 ms, QRS = 52.6 ± 1.5 ms, and QTc = 387 ± 19 ms. These results are compatible with those from prior studies performed using superconducting quantum interference device (SQUID) fMCG systems. To our knowledge, this is the first European fMCG device with OPM technology commissioned for basic research in a pediatric cardiology unit. We demonstrated a patient-friendly, comfortable, and open fMCG system. The data yielded consistent cardiac intervals, measured from time-averaged waveforms, compatible with published SQUID and OPM data. This is an important step toward making the method widely accessible.

Fetal ECG Extraction and QRS Detection Using Advanced Adaptive Filtering-Based Signal Decomposition and Peak Threshold Technique from Abdominal ECG Signals

Fetal ECG Extraction and QRS Detection Using Advanced Adaptive Filtering-Based Signal Decomposition and Peak Threshold Technique from Abdominal ECG Signals

PO-01-207 VERNIX CASEOSA DISTRIBUTION MODULATES NON-INVASIVE FETAL ECG SIGNAL: A COMPUTATIONAL STUDY

Non-invasive fetal ECG (FECG) shows promise as a diagnostic method to monitor fetal health. However, extracting the fetal cardiac signal remains a challenge due to low signal-to-noise ratio. An important factor is a low-conducting layer which forms around the fetus from week 28 of gestation called the vernix caseosa (VC). We developed an anatomically detailed and biophysically accurate computational model of a pregnant torso to investigate the effects of the VC on the FECG. The model was constructed by combining geometries of a pregnant woman with a 24 week infant and a torso with ventricles (a). Ionic current properties of the maternal heart is modelled using the Ten Tusscher human ventricular cardiomyocyte model, while a modified version adapted to match fetal electrophysiology is used for the fetal heart. 22 distribution patterns of the VC was generated using combinations of 3 regions and 4 thickness. No VC case was used as control. Electrical propagation was simulated using the pseudo-bidomain formulation. Sinus rhythm was simulated with a baseline cycle of 500 ms and 450 ms for the maternal and fetal hearts, respectively. When VC was represented as homogeneous, changing the VC thickness from 0.5 mm to 3 mm resulted in reduction of QRS maximum amplitude from 6 x 10-5 to 6 x 10-6 mV (c). However, in the heterogeneous VC models, there is no significant difference in signal as thickness was changed (d). The location of the VC significantly affected the signal (e). VC only covering the head was almost identical to control with a relative difference measure of only 1.35%. However, introduction of VC on the back resulted in greatest difference in potential distribution with a relative difference measure ranging between 34.5 and 49%. We have developed a novel, highly-detailed computational model of a pregnant torso that shows how VC distribution affects the FECG. This model can be used to improve techniques in extracting FECG at different gestational stages.

Analysis on population-based algorithm optimized filter for non-invasive fECG extraction

Metaheuristic algorithms (MAs) have become one of the primary tools for optimization in diverse domains, including non-invasive fetal electrocardiogram (fECG) extraction. Research reveals that hyperparameters affect the performance of MAs differently to problems, and some algorithms are problem-specific designed and may produce bad results for different problems. Hence, three questions arise, (1) how much can we trust MAs when solving boxed-constrained fECG extraction problems, and (2) which type of MAs are suitable and adequate for fECG extraction? (3) do MAs find acceptable solutions to a problem that does not formulate the problem comprehensively with an imperfect objective function? This paper focuses on these three inquiries and proposes a framework providing an MA-assisted adaptive filter for non-invasive fECG extraction. The proposed framework has three key components: data pre-processing, MA-based adaptive filter, and post-processing. The pre-processing starts to process the signals detected by pregnant women and extract the desired signal by independent component analysis. Such output signals are then passed to a filter and optimized by population-based algorithms, producing an optimal solution. After that, this solution, as filter weights, will be used for signal extraction and be processed by post-processing. Importantly, the proposed framework is user-friendly that can import any MA algorithm to run and disassemble as a separate assisting tool. This work investigated eight classic and disparate MA algorithms on the Abdominal and Direct Fetal ECG Database (ADFECGDB) dataset, offering comprehensive experimental analysis. We infer from the results that the recordings r01,r02,r03,r05,r08, and r09, where the signals are less noisy and can be solved well. While for recordings r04,r07,r06, and r10, the performance can be influenced by hyperparameters for any test algorithms, such as the population size, window size, and other parameters in MAs. Meanwhile, we found that MA may not be the primary target influencing the performance vary.

Comparative Assessment of BSS Techniques for Non-Invasive Extraction of Fetal ECG from Abdominal ECG Signal

Comparative Assessment of BSS Techniques for Non-Invasive Extraction of Fetal ECG from Abdominal ECG Signal

Separation of the fetal heart signal in a synchronous network consisting of maternal and fetal hearts

This paper studies the heart's oscillation model to separate fetal and maternal ECGs from abdominal recordings. To this aim, two phases are designed. A Modified version of the Duffing-Van der Pol oscillator is considered a computational heart model in the modeling phase. To evaluate the interaction effects of the maternal and fetal heart and the differences and features of the fetal heart structure, the fetal heart model is Modified based on the maternal heart model. A non-identical network is employed as an interactive network of the mother and the fetus's heart. Then the degree of the network synchronization is measured with the help of a pattern synchronization index of the non-identical network. An attempt is made to separate the fetal signal from the mother's in abdominal signals in the separation phase. Two problem-solving approaches are explained; the step-by-step mode that calculates the signal at any given moment and the construction of general equations. These approaches end up calculating the variables, which stand for maternal and fetal signal. That makes it possible to achieve the separation of maternal and fetal ECGs.

Ambulatory antenatal fetal electrocardiography in high-risk pregnancies (AMBER): protocol for a pilot prospective cohort study

IntroductionFetal heart rate (FHR) monitoring is a vital aspect of fetal well-being assessment, and the current method of computerised cardiotocography (cCTG) is limited to the hospital setting. Non-invasive fetal ECG...

Automatic Optimization of Multichannel Electrode Configurations for Robust Fetal Heart Rate Detection by Blind Source Separation.

Fetal heart rate (fHR) evaluation is fundamental to guarantee timely medical intervention in case of pregnancy complications. Due to the limitations of traditional cardiotocography, multichannel electrophysiological recording was proposed as a viable alternative, which requires Blind Source Separation (BSS) techniques. Yet effective and reliable separation of the fetal ECG remains challenging due to multiple noise sources and the effects of varying fetal position. In this work, we demonstrate that the adopted electrode configuration plays a key role in the effectiveness of BSS and propose guidelines for optimal electrode positioning. Moreover, a model is proposed to automatically predict the most suited configuration for accurate BSS-based fHR estimation with a minimal number of leads, to facilitate practical implementation. We compared fHR estimation accuracy with different electrode configurations on in-silico data, identifying the optimal configuration for a recent BSS method. Based on features extracted from raw signals, we proposed a support vector regression model to automatically identify the best electrode configuration in terms of fHR estimation accuracy and to dynamically adjust it to varying fetal presentation. Evaluation was performed on real and synthetic data. Guidelines for the optimal electrode configuration are proposed by using 4 leads. Prediction of configuration quality shows 80.9% accuracy; the optimal configurat- ion is recognized in 92.2% of the subjects. The proposed method successfully predicts the quality of the configurations, demonstrating the impact of the electrode configuration on the BSS performance. The method holds potential for long-term fetal monitoring, by dynamically choosing the optimal configuration.

DETECTION OF FETAL ELECTROCARDIOGRAM SIGNALS FROM MATERNAL ABDOMINAL ECG RECORDINGS

Fetal electrocardiogram (fECG) is a signal that contains vital information about the health of the fetus throughout pregnancy. During pregnancy, it is important to monitor and analyse this signal because it represents the electrical activity of the developing fetal heart. Early detection of fetal ECG problems during the fetus' development is crucial because it allows early treatment and provides knowledge about diseases that may emerge at a later time. Extraction of fetal ECG from the abdomen ECG signal is valuable in these aspects. In order to extract the fetal ECG from the recorded abdomen ECG signals correctly, it must be handled appropriately. It could be challenging to separate the fetal ECG signal from other physiological artifacts and noises in the mother abdominal signal. In this study, signal processing techniques were used to separate the fetus ECG signal from real abdominal ECG recordings. These methods include Ensemble Empirical Based Denoising, Finite Impulse Response Filter, Independent Component Analysis, and Pan &amp;amp; Tompkins approach. The results show that utilizing only the ICA technique to extract fECG signals is insufficient and that additional algorithms, such as those indicated above, should be used together. The mECG and fECG signals can be successfully extracted using the suggested approach.

Australian women’s experiences of wearing a non-invasive fetal electrocardiography (NIFECG) device during labour

BackgroundContinuous electronic fetal monitoring devices can restrict women’s freedom of movement and choice of positioning during labour and birth. Despite the use of continuous electronic fetal monitoring for the past 50 years, little attention has been paid to women’s experiences of wearing different fetal monitoring devices in labour. AimTo explore women’s views and experiences of wearing a beltless continuous electronic fetal monitoring device, the non-invasive fetal electrocardiogram during labour. MethodsA qualitative descriptive approach was taken. Recruitment was via a larger clinical feasibility study. Some women who consented to take part in the clinical feasibility study also consented to being interviewed during the postnatal period. Transcripts were thematically analysed. FindingsWomen reported improved comfort when wearing the non-invasive fetal electrocardiogram device. They appreciated how it enabled freedom of movement and an ability to actively participate in labour. They compared their experience with previous use of cardiotocography which they felt compromised their bodily autonomy. All forms of continuous electronic fetal monitoring experienced by women resulted in the unwelcome experience of ‘Poking and prodding’ by the midwife. DiscussionContinuous electronic fetal monitoring can negatively impact women’s labour and birth experience, particularly when the measurement of fetal wellbeing is prioritised. ConclusionThe way in which continuous electronic fetal monitoring technology is designed and used is an important component of optimising physiological processes and positive experiences for women during labour and birth for women with complex pregnancies. Non-invasive fetal electrocardiograpy is a promising additional option for women.

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.