Non-invasive Fetal Electrocardiography Research Articles

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

Objective. Perinatal asphyxia poses a significant risk to neonatal health, necessitating accurate fetal heart rate monitoring for effective detection and management. The current gold standard, cardiotocography, has inherent limitations, highlighting the need for alternative approaches. The emerging technology of non-invasive fetal electrocardiography shows promise as a new sensing technology for fetal cardiac activity, offering potential advancements in the detection and management of perinatal asphyxia. Although algorithms for fetal QRS detection have been developed in the past, only a few of them demonstrate accurate performance in the presence of noise and artifacts. Approach. In this work, we propose Power-MF, a new algorithm for fetal QRS detection combining power spectral density and matched filter techniques. We benchmark Power-MF against three open-source algorithms on two recently published datasets (Abdominal and Direct Fetal ECG Database: ADFECG, subsets B1 Pregnancy and B2 Labour; Non-invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research: NInFEA). Main results. Our results show that Power-MF outperforms state-of-the-art algorithms on ADFECG (B1 Pregnancy: 99.5% ± 0.5% F1-score, B2 Labour: 98.0% ± 3.0% F1-score) and on NInFEA in three of six electrode configurations by being more robust against noise. Significance. Through this work, we contribute to improving the accuracy and reliability of fetal cardiac monitoring, an essential step toward early detection of perinatal asphyxia with the long-term goal of reducing costs and making prenatal care more accessible.

A deep learning framework for noninvasive fetal ECG signal extraction.

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

PCA-Based Preprocessing for Clustering-Based Fetal Heart Rate Extraction in Non-Invasive Fetal Electrocardiograms

Non-invasive fetal electrocardiography (NI-ECG) is based on the acquisition of signals from electrodes on the mother’s abdominal surface. This abdominal ECG (aECG) signal consists of the maternal ECG (mECG) along with the fetal ECG (fECG) and other noises and artifacts. These records allow the acquisition of valuable and reliable information that helps ensure fetal well-being during pregnancy. This paper proposes a procedure based on principal component analysis (PCA) to obtain a single-channel master abdominal ECG record that can be used as input to fetal heart rate extraction techniques. The new procedure requires three main processing stages: PCA-based analysis for fECG-component extraction, polarity test, and curve fitting. To show the advantages of the proposal, this PCA-based method has been used as the feeding stage to a previously developed clustering-based method for single-channel aECG fetal heart rate monitoring. The results obtained for a set of real abdominal ECG recordings from annotated public aECG databases, the Abdominal and Direct Fetal ECG Database and the Challenge 2013 Training Set A, show improved efficiency in fetal heart rate extraction and illustrate the benefits derived from the use of such a master abdominal ECG channel. This allows us to achieve proper fetal heart rate monitoring without the need for manual inspection and selection of channels to be processed, while also allowing us to analyze records that would have been discarded otherwise.

An Integrated Framework for Assessing the Quality of Non-invasive Fetal Electrocardiography Signals

An Integrated Framework for Assessing the Quality of Non-invasive Fetal Electrocardiography Signals

Evaluation and patient experience of wireless noninvasive fetal heart rate monitoring devices.

In clinical practice, fetal heart rate monitoring is performed intermittently using Doppler ultrasound, typically for 30 minutes. In case of a non-reassuring heart rate pattern, monitoring is usually prolonged. Noninvasive fetal electrocardiography may be more suitable for prolonged monitoring due to improved patient comfort and signal quality. This study evaluates the performance and patient experience of four noninvasive electrocardiography devices to assess candidate devices for prolonged noninvasive fetal heart rate monitoring. Non-critically sick women with a singleton pregnancy from 24 weeks of gestation were eligible for inclusion. Fetal heart rate monitoring was performed during standard care with a Doppler ultrasound device (Philips Avalon-FM30) alone or with this Doppler ultrasound device simultaneously with one of four noninvasive electrocardiography devices (Nemo Fetal Monitoring System, Philips Avalon-Beltless, Demcon Dipha-16 and Dräger Infinity-M300). Performance was evaluated by: success rate, positive percent agreement, bias, 95% limits of agreement, regression line, root mean square error and visual agreement using FIGO guidelines. Patient experience was captured using a self-made questionnaire. A total of 10 women were included per device. For fetal heart rate, Nemo performed best (success rate: 99.4%, positive percent agreement: 94.2%, root mean square error 5.1 BPM, bias: 0.5 BPM, 95% limits of agreement: -9.7 - 10.7 BPM, regression line: y = -0.1x + 11.1) and the cardiotocography tracings obtained simultaneously by Nemo and Avalon-FM30 received the same FIGO classification. Comparable results were found with the Avalon-Beltless from 36 weeks of gestation, whereas the Dipha-16 and Infinity-M300 performed significantly worse. The Avalon-Beltless, Nemo and Infinity-M300 closely matched the performance of the Avalon-FM30 for maternal heart rate, whereas the performance of the Dipha-16 deviated more. Patient experience scores were higher for the noninvasive electrocardiography devices. Both Nemo and Avalon-Beltless are suitable devices for (prolonged) noninvasive fetal heart rate monitoring, taking their intended use into account. But outside its intended use limit of 36 weeks' gestation, the Avalon-Beltless performs less well, comparable to the Dipha-16 and Infinity-M300, making them currently unsuitable for (prolonged) noninvasive fetal heart rate monitoring. Noninvasive electrocardiography devices appear to be preferred due to greater comfort and mobility.

Multichannel high noise level ECG denoising based on adversarial deep learning

This paper proposes a denoising method based on an adversarial deep learning approach for the post-processing of multi-channel fetal electrocardiogram (ECG) signals. As it’s well known, noise leads to misinterpretations of fetal ECG signals and thus limits the use of fetal electrocardiography for healthcare applications. Therefore, denoising algorithms are essential for the exploitation of non-invasive fetal ECG. The proposed method is based on the combination of three end-to-end trained sub-networks to convert noisy fetal ECG signals into clean signals. The first two sub-networks are linked by skip connections and form a deep convolutional network that downsamples the noisy signals into a latent representation and subsequently upsamples this latent representation to recover clean signals. The third sub-network aims to boost the decoder sub-network to generate realistic clean signals. Experiments carried out on synthetic and real data showed that the proposed method improved by the signal-to-noise (SNR) of fetal ECG signals with input SNR ranging from -,30 to 0 dB by an average of 20 dB, and improve fetal signal quality by significantly increasing the number of true detected QRS complexes and halving QRS complex detection errors.

Template subtraction based methods for non-invasive fetal electrocardiography extraction

Assessment of fetal heart rate (fHR) through non-invasive fetal electrocardiogram (fECG) is challenging task. This study compares the performance of five template subtraction (TS) methods on Labor (12 5-min recordings) and Pregnancy datasets (10 20-min recordings). The methods include TS without adaptation, TS using singular value decomposition (TS_{textrm{SVD}}), TS using linear prediction (TS_{textrm{LP}}), TS using scaling factor (TS_{textrm{SF}}), and sequential analysis (SA). The influence of the chosen maternal wavelet for the continuous wavelet transform (CWT) detector is also compared. The F1 score was used to measure performance. Each recording in both datasets consisted of four signals, resulting in a total comparison of 88 signals for the TS-based methods. The study reported the following results: F1 = 95.71% with TS, F1 = 95.93% with TS_{textrm{SVD}}, F1 = 95.30% with TS_{textrm{LP}}, F1 = 95.82% with TS_{textrm{SF}}, and F1 = 95.99% with SA. The study identified gaus3 as the suitable maternal wavelet for fetal R-peak detection using the CWT detector. Furthermore, the study classified signals from the tested datasets into categories of high, medium, and low quality, providing valuable insights for subsequent fECG signal extraction. This research contributes to advancing the understanding of non-invasive fECG signal processing and lays the groundwork for improving fetal monitoring in clinical settings.

The value of some indices of heart rate variability in the diagnosis of fetal growth retardation

Fetal growth retardation (FGR) is one of the most serious problems in current perinatology. This pathology leads not only to the deterioration of perinatal outcomes but also significantly increases morbidity during adult life. FGR is known to be featured by its delayed “neurological maturation”. This is manifested in a decrease in autonomic nervous regulation according to the fetal heart rate variability (HRV) scores and indices. The objective: to study HRV in fetuses with normal biometric indices compared to similar parameters of growth retarded fetuses. Materials and methods. The 47 pregnant women who had fetuses with normal biometric indices (I group) and 33 patients with fetal growth retardation (II group) were included in the study. The investigation of the fetal HRV was performed by processing the R–R intervals obtained via non-invasive electrocardiogram (ECG) and involved the following indices: STV (short term variations), LTV (long term variations), AC/DC (acceleration capacity/deceleration capacity), TP (total power), SI (stress index), SDNN (standard deviation of normal to normal intervals), EnRE (entropy of the time series), D2 (correlation dimension of the time series), Z (temporary irreversibility) і FL (measure of fuzzy logic, integrated measure of HRV). Results. The obtained data indicated that the indices of fetal HRV were reduced in the patients with FGR. These indices have utility in screening programs for the detection of fetal intrauterine nutrition and growth disturbances. The results of the study allow us to believe that the ability to accelerate heart rate patterns is partly preserved even during FGR. This may indicate a very ancient phylogenetic nature of the reaction of the heart system of the fetus to its motile activity. However, decelerations according to the DC were typical for FGR. A non-invasive fetal ECG could become a useful supplementary method in the current methods’ spectrum used in perinatal medicine. Conclusions. Therefore, the development of algorithms for the diagnosis of FGR requires the inclusion of the following HRV indices: STV, LTV, DC, TP, SI, SDNN, D2, Z, and FL. These biophysical markers of FGR can be used as a screening program in conditions of lack of access to high-quality ultrasound diagnostics or be a valuable addition to existing perinatal strategies.

Fetal ECG Arrhythmia Detection Based on DensNet Transfer Learning

Purpose: The mortality rate of fetuses due to heart defects is a major concern for clinicians. The fetus's heart is monitored non-invasively using the abdominal Electrocardiogram (ECG) of the mother. Most of the methods in literature diagnose fetal arrhythmia based on fetal heart rate. However, there are various challenges in fetal heart rate monitoring and arrhythmia detection. Therefore, very few methods are explored for fetal arrhythmia classification and have not achieved promising results.
 Materials and Methods: In this article, a fetal arrhythmia classification method is investigated. The method has exploited the transfer learning principle where DenseNet architecture is utilized to learn fetal ECG patterns. Fetal ECG (fECG) signal extracted from the mothers abdominal has been processed for denoising and heartbeats are segmented using signal processing techniques. The extracted heartbeats have transformed into 2D fECG images to re-train the pre-trained DenseNet architecture.
 Results: The proposed method has been evaluated on the publicly available Non-Invasive Fetal Arrhythmia Database (NIFADB) of Physionet and achieved 98.56% classification accuracy, thus outperforming other existing methods.
 Conclusion: The arrhythmia in a fetus can be detected using a non-invasive fetal ECG. Due to the faster convergence of the learning algorithm, the proposed method offers better fetal diagnosis in real-time.

Accurate Fetal QRS-Complex Classification from Abdominal Electrocardiogram Using Deep Learning

Fetal heart monitoring during pregnancy plays a critical role in diagnosing congenital heart disease (CHD). A noninvasive fetal electrocardiogram (fECG) provides additional clinical information for fetal heart monitoring. To date, the analysis of noninvasive fECG is challenging due to the cancellation of maternal QRS-complexes, despite significant advances in electrocardiography. Fetal QRS-complex is highly considered to measure fetal heart rate to detect some fetal abnormalities such as arrhythmia. In this study, we proposed a deep learning (DL) framework that stacked a convolutional layer and bidirectional long short-term memory for fetal QRS-complexes classification. The fECG signals are first preprocessed using discrete wavelet transform (DWT) to remove the noise or inferences. The following step beats and QRS-complex segmentation. The last step is fetal QRS-complex classification based on DL. In the experiment of Physionet/Computing in Cardiology Challenge 2013, this study achieved 100% accuracy, sensitivity, specificity, precision, and F1-score. A stacked DL model demonstrates an effective tool for fetal QRS-complex classification and contributes to clinical applications for long-term maternal and fetal monitoring.

Detecting anomalies in fetal electrocardiogram records using deep learning models

In order to assess the fetus health and make timely decisions throughout pregnancy, Fetal Electrocardiography (FECG) monitoring is essential. Huge datasets for electrocardiograms are freely accessible from Physionet ATM Dataset1- Abdominal and Direct Fetal ECG Database (adfecgdb), Dataset2- Fetal ECG Synthetic Database (fecgsyndb), Dataset3- Non-Invasive Fetal ECG Database(nifecgdb). In this study, we strive to categorize normal and abnormal (Atrial fibrillation) FECG from three online dataset which contains FECG recordings as major details. Deep learning models like Transfer Learning (TL) and Convolutional Neural Networks (CNN) are being investigated. The composite abdominal signal and the FECG are separated using a wavelet transform approach. The best model for categorizing the parameters of the FECG is determined through a comparative analysis and performance is improved using Continuous Wavelet Transform (CWT). The accuracy of the CNN-based technique is found to be 98.59%, whereas the accuracy of the transfer learning model is 99.01% for FECG classification. The computation of metric parameters for all the datasets is done. The classification of normal and abnormal (Atrial fibrillation) is best performed in TL model compared to CNN. Real-time data analysis is done for PQRST plotting and comparative study is done using Net Reclassification Improvement (NRI) and obtained NRI = 13%, z static 0f 3.7641, p-Value of 0.00016721. Acute Myocardial Infraction (AMI) identification is done based on ST segment of Maternal ECG (MECG) images to analyze the heart attack risk. The proposed work can be utilized to track FECG waveforms in real-time for wearable technology because of its end-to-end properties and expandable intrinsic for diagnosing multi-lead heart disorders.

A Dual Self-Calibrating Framework for Noninvasive Fetal ECG R-Peak Detection

Fetal heart rate (fHR) is critical for assessing fetal health and diagnosing disorders such as fetal distress, congenital heart disease, and intrauterine growth retardation. With the rapid development of the Internet of Medical Things (IoT), fetal R-peak detection plays an important role in diagnosing heart defects during pregnancy. However, due to the nonlinear mixing of multiple sources in the non-invasive signals and the low signal-to-noise ratio (SNR), it is difficult to obtain accurate R-peak detection result. This article presents a dual Self-calibrating system based on a Spectral Attention Kernel Independent Component Analysis (SA-KICA) module and a Selfcalibrating fetal R-peak detection (SC-FRD) module. SA-KICA is an ICA-based calibration module constructed by SA mechanism, which was sought from Short-Time Fourier Transform (STFT) and was shipped back to original signal with convolution to achieve perfect maternal electrocardiogram (MECG) separation in high-dimensional linear separable space. Then a periodic and morphological based Channel Selector is designed to select the optimal MECG. After MECG removal, to further improve the performance of fetal R-peak detection, the SC-FRD module is introduced to utilize the interior peak information and selfcalibrating strategy, which includes variance-based fetal R-peak seed selection, time-varying coarse prediction and adaptive probability mask calibration. The proposed framework is a primary attempt to concurrently introduce the nonlinear feature, spectral information and self-calibrating strategy in the field of fetal ECG processing. The framework achieved excellent performance in fetal R-peak detection accuracy on a simulated dataset and two public datasets with varying divergence and richness of resources. The experimental results show that our framework is superior to existing methods and can be used as a potential fetal monitoring method in the application of Internet of Medical Things. The code is released in https://github.com/bfyjr/NI-FECG-Extraction.

An optimized hybrid methodology for non-invasive fetal electrocardiogram signal extraction and monitoring

An optimized hybrid methodology for non-invasive fetal electrocardiogram signal extraction and monitoring

A novel deep learning technique for morphology preserved fetal ECG extraction from mother ECG using 1D-CycleGAN

The non-invasive fetal electrocardiogram (fECG) enables easy detection of developing heart abnormalities, leading to a significant reduction in infant mortality rate and post-natal complications. Due to the overlapping of maternal and fetal R-peaks, the low amplitude of the fECG, systematic and ambient noises, typical signal extraction methods, such as adaptive filters, independent component analysis, empirical mode decomposition, etc., are unable to produce satisfactory fECG. While some techniques can produce accurate QRS waves, they often ignore other important aspects of the ECG. Utilizing extensive preprocessing and an appropriate framework, our approach, built upon 1D CycleGAN, achieves fECG signal reconstruction from the mECG signal while preserving its morphology. The performance of our solution was evaluated by combining two available datasets from Physionet, “Abdominal and Direct Fetal ECG Database” and “Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations”, where it achieved an average PCC and Spectral-Correlation score of 88.4% and 89.4%, respectively. It detects the fQRS of the signal with accuracy, precision, recall and F1 score of 92.6%, 97.6%, 94.8% and 96.4%, respectively. It can also accurately produce the estimation of fetal heart rate and R-R interval with an error of 0.25% and 0.27%, respectively. The main contribution of our work is that, unlike similar studies, it can retain the morphology of the ECG signal with high fidelity. The accuracy of our solution for fetal heart rate and R-R interval length is comparable to existing state-of-the-art techniques. This makes it a highly effective tool for early diagnosis of fetal heart diseases and regular health checkups of the fetus.

Fetal electrocardiography and artificial intelligence for prenatal detection of congenital heart disease.

This study aims to investigate non-invasive electrocardiography as a method for the detection of congenital heart disease (CHD) with the help of artificial intelligence. An artificial neural network was trained for the identification of CHD using non-invasively obtained fetal electrocardiograms. With the help of a Bayesian updating rule, multiple electrocardiographs were used to increase the algorithm's performance. Using 122 measurements containing 65 healthy and 57 CHD cases, the accuracy, sensitivity, and specificity were found to be 71%, 63%, and 77%, respectively. The sensitivity was however 75% and 69% for CHD cases requiring an intervention in the neonatal period and first year of life, respectively. Furthermore, a positive effect of measurement length on the detection performance was observed, reaching optimal performance when using 14 electrocardiography segments (37.5 min) or more. A small negative trend between gestational age and accuracy was found. The proposed method combining recent advances in obtaining non-invasive fetal electrocardiography with artificial intelligence for the automatic detection of CHD achieved a detection rate of 63% for all CHD and 75% for critical CHD. This feasibility study shows that detection rates of CHD might improve by using electrocardiography-based screening complementary to the standard ultrasound-based screening. More research is required to improve performance and determine the benefits to clinical practice.

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.

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.

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.

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...