Fetal Electrocardiogram Research Articles

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.

Novel approach for ECG separation using adaptive constrained IVABMGGMM

In this paper, we introduce the constrained independent vector analysis integrated with the bounded multivariate generalized Gaussian mixture model (cIVABMGGMM) to tackle the limitations of independent component analysis (ICA) when applied to multivariate data, accompanied by its adaptive version, the acIVABMGGMM, aimed at alleviating associated constraints. The acIVABMGGMM employs a full covariance matrix that considers feature correlations, effectively addressing the challenges posed by ICA and independent vector analysis (IVA) models when analyzing multivariate data. The innovative acIVABMGGMM framework merges the adaptability inherent in data-driven methods with the capability to manage noise and other artifacts often encountered in model-based approaches. This technique effectively employs prior knowledge to guide the solution, avoiding the imposition of inaccurate constraints. To overcome these challenges, our set of two constrained methods incorporates prior source information into the IVA model, effectively mitigating its limitations in data with a high number of sources. We assess the efficacy of our proposed models through three distinct ECG separation experiments, which include heartbeat separation, fetal ECG extraction, and arrhythmia detection. Notably, the performance metrics demonstrate the superiority of our models over the baseline approaches in the conducted experiments.

A novel hybrid method for calculating the fetal heart rate from the non-invasive abdominal Electrocardiogram signal

Continuous monitoring of fetal health requires the accurate calculation of the Fetal Heart Rate (FHR) by efficiently isolating fetal Electrocardiogram (fECG) from non-invasive abdominal Electrocardiogram (aECG). There are numerous solutions proposed for this problem, having its own restrictions. This paper introduces a novel three-stage hybrid adaptive approach for fECG extraction and FHR determination from aECG. The proposed framework is an amalgamation of independent component analysis (ICA), adaptive exponential functional link network (AEFLN), and novel wavelet thresholding (NWT). The proposed work aims to determine the best quality fetal R-peaks for calculating fetal heart rate (FHR). ICA is a blind source separation technique that uses all the information from the multichannel inputs to determine the maternal Electrocardiogram (mECG) and enhanced aECG. In the next stage, a non-linear adaptive filter, AEFLN, is used for the extraction of fECG. This approach requires no additional algorithm for polarity reversal and time alignment before AEFLN. At last, NWT determines the fetal R-peak envelopes from the residual fECG. The R-peak envelopes are used to analyze FHR, and its accuracy was accessed using the FHR trace. So far, FastICA-AEFLN-NWT has not been used as a hybrid method for fECG extraction and fetal R-peak detection. The algorithm’s effectiveness was tested on both real databases from Daisy and Physionet and synthetic databases from Physionet. The proposed hybrid method attains 95.29 % of sensitivity (SE), 96.68 % of Positive Predictive Value (PPV), 95.92 % of F1 score, and FHR measurement (FHRmeas) is 29.72 bpm2. The proposed methodology outperforms the state-of-art methods in fECG extraction.

Fetal QRS extraction from single-channel abdominal ECG using adaptive improved permutation entropy.

Fetal electrocardiogram (fECG) monitoring is crucial for assessing fetal condition during pregnancy. However, current fECG extraction algorithms are not suitable for wearable devices due to their high computational cost and multi-channel signal requirement. The paper introduces a novel and efficient algorithm called Adaptive Improved Permutation Entropy (AIPE), which can extract fetal QRS from a single-channel abdominal ECG (aECG). The proposed algorithm is robust and computationally efficient, making it a reliable and effective solution for wearable devices. To evaluate the performance of the proposed algorithm, we utilized our clinical data obtained from a pilot study with 10 subjects, each recording lasting 20min. Additionally, data from the PhysioNet 2013 Challenge bank with labeled QRS complex annotations were simulated. The proposed methodology demonstrates an average positive predictive value ([Formula: see text]) of 91.0227%, sensitivity (Se) of 90.4726%, and F1 score of 90.6525% from the PhysioNet 2013 Challenge bank, outperforming other methods. The results suggest that AIPE could enable continuous home-based monitoring of unborn babies, even when mothers are not engaging in any hard physical activities.

Fetal electrocardiogram prediction using machine learning: a random forest-based approach

<p class="IOPText">Monitoring fetal health during pregnancy ensures safe delivery and the newborn’s well-being. The fetal electrocardiogram (fetal ECG) is a valuable tool for assessing fetal cardiac health, but interpretation of ECG data can be challenging due to its complexity and variability. In this work, we explore the application of machine learning, particularly random forest, to predict and analyze fetal ECGs. With its ability to manage large datasets and provide precise insights, random forest is a promising solution for this challenge. By comparing our random forest-based approach with other standard machine learning techniques such as artificial neural network (ANN), support vector machines (SVM), and recurrent neural networks (RNN), we observed that our solution outperformed these methods in accuracy, robustness, and reliability. This article details the methodology used, the implementation of the algorithm, as well as the comparative results obtained. Emphasis is placed on the benefits of random forest in this specific medical context, highlighting its potential as a future tool for fetal ECG prediction. Ultimately, our research suggests a shift toward random forest-based solutions for more efficient and accurate analysis of fetal ECGs, with direct implications for clinical practice and fetal well-being.</p>

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.

A novel Discrete Artificial Bee Colony algorithm combined with adaptive filtering to extract Fetal Electrocardiogram signals

The Fetal Electrocardiogram (FECG) signal plays a crucial role in monitoring the health of the fetus, but there are numerous challenges in eliminating the maternal thorax signal and reducing noise interference. This paper proposes a novel objective function that combines a Least Mean Squares (LMS) adaptive filter with a heuristic algorithms to enhance the quality of the extracted FECG signal. To achieve better results, we introduce the Discrete Artificial Bee Colony (DABC) algorithm with a new initialization strategy, a random wavelet basic function strategy, and Gaussian distribution. These improvements enhance global search capabilities and ensure a faster convergence rate. The application of heuristic algorithms can reduce noise signals and provides clearer and more accurate results compared to the traditional LMS filter. Furthermore, the effectiveness of this innovative algorithm is compared with other widely used heuristic algorithms. The experiment results demonstrate that the novel algorithm significantly enhances performance by up to 8% compared to other conventional extraction methods in some indicators.

Optimized systolic array filters with noise classification for extracting FECG

To preserve the lives of both mother and foetus during the early phases of childbirth, heart abnormality diagnosis is essential. In remote places where understanding of maternal care is limited, the death rate caused by carelessness or a failure to detect abnormalities is still a problem. The signal will contain various external or internal noise sources to isolate the key components of the foetal heart rate from the mother's belly during labour. The likelihood that the genuine signal would be misinterpreted and result in a false report will increase in the presence of such noise sources. Although there are many software programs available for extracting the QRS features from foetal ECG signals, it is unavoidable that specialized hardware is required for a significant reduction in both area and power. This paper's main goal is to extract the QRS complex using LDA, then improve Social Spider classifier performance using the suggested TAODV as a distance metric calculator, and then compare against existing methods to discover sounds that are distorting the normal heart rate. Systolic array filter with suggested Glitch Avoidance Circuit employing MUX is simulated using Cadence Virtuoso in 65nm technology to remove noise from the observed QRS complex. Over 100 records with the necessary examples from MIT-BIH Arrhythmia were used in the simulations, and it was discovered that MATLAB 2010b was used to adopt a unique technique for classifying noise. The suggested TAODV-based SSA classifier's accuracy is 96.8%, whereas the accuracy of a filter with a glitch avoidance circuit is 96.13%. The primary benefit of these strategies comprises cutting-edge hardware and computational solutions.

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.

A wearable system to track vital signs communicated from the mother to the fetus.

The monitoring of fetal heart rate (FHR) before intrapartum has been crucial in modern obstetrics. FHR has been used for about 300 years to determine fetal status, leading to the development of monitoring devices to prevent fetal death during gestation. While medical devices like fetal electrocardiograms exist for disease detection, their size and cost limit individual use. To address cardiovascular issues during pregnancy, a mobile system is developed to display heart rates and blood pressure on mobile devices. The system is generated from a medical device with Bluetooth communication, supplementing traditional monitoring. The study focuses on creating a mobile system that connects to mobile operating systems, enhancing treatment, diagnosis, and patient monitoring. The mobile system displays cardiovascular data obtained from the medical device. The results are expected to have an immediate impact on cases where abnormal measurement parameters of the monitoring system occur during pregnancy. The use of mobile systems or applications on smartphones is seen as beneficial in distributing processing and census of embedded health systems. The study highlights the potential benefits of mobile systems in distributing processing for health systems, particularly in addressing cardiovascular problems during pregnancy. The creation of a mobile system for displaying cardiovascular data could significantly improve monitoring and early detection.

Joint Improved Fast Independent Component Analysis and Singular Value Decomposition for Fetal Electrocardiogram Extraction.

Combined the improved fast independent component analysis (FastICA) algorithm with the singular value decomposition algorithm, a single-channel fetal electrocardiogram (fECG) extraction method is proposed. First, the improved FastICA algorithm is used to estimate the maternal ECG component from a single-channel abdominal signal of pregnant women using an overrelaxation factor. Then, a preliminary estimate of the fECG signal is obtained by subtracting from the single-channel abdominal signal. Subsequently, the singular value decomposition algorithm is used to denoise the preliminarily estimated fECG signal to obtain a high signal-to-noise ratio. In addition, in the singular value decomposition algorithm for fetal arrhythmia, an improved method for constructing the ECG signal reconstruction matrix is proposed. Finally, the fECG extraction experiments on synthetic abdominal signals and actual abdominal signals (data from 49 abdominal channels sourced from DAISY database and the non-invasive fECG database in PhysioNet) are carried out. The experimental results show that the method in this paper can effectively improve the signal-to-noise ratio and the accuracy of fECG signal extraction, and is suitable for maternal or fetal arrhythmias. Compared with the FastICA algorithm, the signal-to-noise ratio of the fECG signal extracted by the method in this paper is improved by about 5 dB, and the accuracy of fECG extraction in the PhysioNet database can reach 96.54%.

Spatio-spectral independent component analysis for fetal ECG extraction from two-channel maternal abdominal signals

Independent component analysis (ICA) is widely used to separate maternal and fetal electrocardiograms. However, it has become less effective due to the efforts to reduce the number of recording electrodes. To address this issue, we propose an extension of ICA that can extract the fetal electrocardiogram from only two maternal abdominal electric signals. We solve this problem by increasing the dimension of the observed signals using the method of delays, followed by spatio-spectral filtering to separate the source signals. By iteratively applying this approach, we can extract signals that are not separable using the original observations alone. These signals are then clustered to create signal subspaces corresponding to different sources, allowing for a rough reconstruction of signal components produced by these sources. This initial decomposition can then be refined by using the reconstructed components as new observations, extending the original ones, and applying ICA to this extended signal representation.Applied to two-channel maternal abdominal signals, the proposed method was able to extract 3 source signals (two maternal and one fetal), resulting in the achievement of the goal of over-complete blind source separation (BSS). Furthermore, the method enabled the successful detection of fetal QRS (fQRS) complexes in experiments on two datasets of real-world maternal abdominal signals. For the ADFECGDB dataset, the method reached the sensitivity, positive predictivity, and F1 score of 100%, 99.97%, and 99.98%, respectively, outperforming all reference methods. For the PREGNANCY dataset, the corresponding values were 98.95%, 98.92%, and 98.93%, second only to one reference method.

Impact of Vernix Caseosa Distribution on Non-Invasive Fetal ECG Morphology: A Computational Study.

Accurate monitoring of fetal cardiac activity is paramount for the early detection of fetal pathologies during pregnancy. Non-invasive fetal ECG has shown promise, offering advantages over traditional fetal monitoring techniques such as cardiotocography. However, extracting fetal signals from maternal abdominal recordings poses challenges, particularly due to the presence of the vernix caseosa, a fatty layer surrounding the fetus. This study aims to investigate how vernix caseosa distribution influences ECG morphology in a novel computational framework. A multi-compartment volume conductor, integrating fetal and maternal hearts, fetal body, amniotic fluid, and vernix caseosa embedded in a maternal torso, is constructed. Vernix caseosa distribution is varied homogeneously and heterogeneously on the fetal body. Fetal cardiac activity is simulated using a pseudo-bidomain formulation. Resulting body surface potential and ECG is analysed in terms of RDM, lnMAG, QRS complex and T-wave morphology at six abdominal sensor placements. Results indicate vernix caseosa conductive properties and presence on the fetal head do not notably interfere with ECG readings, except in rare instances where the signal strength is extremely low. Signal strength is reduced more when covering back compared to front of the fetus. Nonetheless, both scenarios have a notable impact on ECG signal and T/QRS ratio, aligning with earlier findings suggesting caution in interpreting T/QRS ratio when vernix caseosa is present. The presence of vernix caseosa warrants careful consideration regarding ECG and especially T/QRS ratio interpretation. The study contributes to advancing the understanding of non-invasive fetal ECG.

A Fast Adaptive LPCA Method for Fetal ECG Extraction Based on Multichannel Signals

A Fast Adaptive LPCA Method for Fetal ECG Extraction Based on Multichannel Signals

Fetal and Maternal Electrocardiogram ECG Prediction using Convolutional Neural Networks

Predicting fetal and maternal electrocardiograms (ECGs) is crucial in advanced prenatal monitoring. In this study, we explore the effectiveness of Convolutional Neural Networks (CNNs), using a carefully developed methodology to predict the category of fetal (F) or maternal (M) ECGs. In the first part, we trained a CNN model to predict fetal and maternal ECG images. In the following sections, the study results will be revealed. The CNN model demonstrated its ability to effectively discriminate between fetal and maternal patterns using automatically learned features

Integration of Time-Frequency Analysis and Regularization Technique for Improved Identification of Fetal Electrocardiogram

This research article presents a novel methodology for effectively extracting the fetal electrocardiogram (FECG) from a single-channel signal acquired on the maternal abdomen. The signal comprises a mixture of the FECG, maternal electrocardiogram (MECG), and ambient noise. The central concept involves projecting the signal into higher-dimensional spaces and leveraging the assumption of statistical independence among the constituent components to achieve their separation from the mixture. To accomplish this, singular value decomposition (SVD) is initially applied to the spectrogram, followed by an iterative application of independent component analysis (ICA) on the principal components. The SVD technique contributes to the enhanced separability of each individual component, while ICA facilitates the promotion of statistical independence between the fetal and maternal ECGs. Furthermore, we refine and customize the aforementioned approach specifically for ECG signals by incorporating knowledge of the frequency distribution of the MECG and other inherent ECG characteristics. The effectiveness of the proposed methodology is validated through comprehensive experimental studies, demonstrating its superior accuracy and performance compared to existing techniques.

Generalized recursive algorithm for fetal electrocardiogram isolation from non-invasive maternal electrocardiogram

Non-invasive maternal electrocardiogram recording is the least unpleasant method to record a weak fetal electrocardiogram signal. The importance of this recording lies in the fact that it reveals crucial information about the fetal health state, especially during the last four weeks of pregnancy. This paper will be concerned with a new adaptive algorithm, namely the generalized recursive algorithm, to isolate and get the fetal electrocardiogram from the abdominal maternal electrocardiogram. This is achieved using a non-invasive method for bi-channel maternal electrocardiogram recordings i.e., with the thoracic maternal electrocardiogram as a reference signal, and the abdominal maternal electrocardiogram as a primary signal. Prior to this procedure, the discrete wavelet transform (DWT) method is applied to the abdominal electrocardiogram signal to clean it from any additive noise and the baseline wandering that is generally present on the raw recordings. The proposed new adaptive filter is shown to deliver improved characteristics through simulations. These simulations were performed on both synthetic and actual signals. This work was compared with the normalized least mean square algorithm.

Enhancing fetal electrocardiogram classification: A hybrid approach incorporating multimodal data fusion and advanced deep learning models

Enhancing fetal electrocardiogram classification: A hybrid approach incorporating multimodal data fusion and advanced deep learning models