Method of fetal electrocardiogram extraction based onν‐support vector regression

The signal strength of the maternal ECG (MECG) is usually many times that of the fetal ECG (FECG). Separating FECG from abdominal ECG (AECG) is therefore always a challenge. Some multiple-lead algorithms use the thoracic MECG to cancel the MECG in the AECG to get FECG, though this is inconvenient for the patient during long-term monitoring. Hence, this paper describes an adaptive method to separate fetal ECG from composite electrocardiography (ECG) that consists of both maternal and fetal ECGs by using adaptive linear neural network (ADALINE) for easy fetal monitoring. The input signal is the maternal signal and the target signal is the composite signal. The network emulate maternal signal as closely as possible to abdominal signal, thus only predict the maternal ECG in the abdominal ECG. The network error equals abdominal ECG minus maternal ECG, which is the fetal ECG. The characteristic that enables fetal extraction is due to the correlation between maternal ECG signals and the abdominal ECG signal of pregnant woman. A graphic user interface (GUI) program is written in Matlab to detect the changes in extracted fetal ECG by different values of momentum, learning rate and initial weights used in the network. However, the learning rate, momentum and initial weights are adjusted until the results are reasonably well. It is found that filtering performs best by high learning rate, low momentum and small initial weights. Key words: Neural network, fetal monitoring, fetal electrocardiography (ECG), QRS, maternal ECG, pregnancy.

Fetal heart rate estimation using fractional Fourier transform and wavelet analysis

Fetal ECG (FECG) monitoring using abdominal maternal signals is a non-invasive technique that allows early detection of changes in fetal wellbeing. Several other signal components have stronger energy than the FECG, the most important being maternal ECG (MECG) and, especially during labor, uterine EMG. This study proposes a new method to subtract MECG after detecting and removing abdominal signal segments with high-amplitude variations due to uterine contractions. The method removes MECG from abdominal signals using an approximation of the current MECG segment based on a linear combination of previous MECG segments aligned on the R-peak. The coefficients of the linear model are computed so that the squared error of the approximation over the whole current segment is minimized. Abdominal signal segments strongly affected by uterine contractions are detected by applying median filtering. The methods proposed are tested on real abdominal data recorded during labor, with FECG recorded using scalp electrodes synchronously recorded for comparison.

Fetal Electrocardiogram (ECG) signal extraction from non-invasive abdominal ECG signal is one of the important clinical practices followed to observe the fetal health state. Information about heart growth and health conditions of a fetus can be observed from fetal ECG signals. However, acquiring fetal ECG from abdominal ECG signals is still considered as a challenging task in biomedical analysis. This is mainly due to corrupted high amplitude maternal ECG signals, low signal to noise ratio of fetal ECG signal, difficulties in reduction of QRS (Q wave, R wave, S wave) complexities, fetal ECG signal superimposed characteristics, other motion, and electromyography artifacts. To reduce these conventional challenges, in fetal ECG analysis of a novel Conditional Generative adversarial network (CGAN) is introduced in this research work to extract the fetal ECG signal. The proposed classification model was classified efficiently in fetal ECG signals from non-invasive abdominal ECG signals. The experimental analysis demonstrates that the proposed network model provides better results in terms of sensitivity, specificity, and accuracy compared to the conventional fetal ECG extraction models like singular value decomposition, periodic component analysis, and Adaptive neuro-fuzzy inference system.

Foetal electrocardiogram (FECG) signal could assist the clinicians in making appropriate and timely decisions during pregnancy and labour since it contains information about the status of the foetus. The extraction and detection of the FECG signal from the composite abdominal signals of the mother with powerful and advance methodology is becoming a very important requirement in foetal monitoring. This paper illustrates the algorithm developed based on neural network approach to provide efficient and effective way of separating FECG signal from the abdominal ECG (AECG) signals. The FECG signal is isolated from the abdominal signal by back propagation neural network approach. The input signal is considered as maternal ECG (MECG) obtained from the thorax of the mother, and the target signal is abdominal signal (AECG) which has both MECG and FECG. The output of the network is subtracted from the target input (AECG). To reduce the difference between the input and target signal the weights have been updated every step, so that acceptable FECG signal can be obtained. To enhance the FECG signal, post-processing steps are followed which involve undecimated wavelet transform (UWT), thresholding and filtering technique.

Fetal electrocardiogram (FECG) signals directly reflect the electrical activity of the fetal heart, enabling the assessment of fetal cardiac health. To effectively separate and extract FECG signals from maternal abdominal electrocardiogram (ECG) signals, this study proposed a W-shaped parallel network, termed Attention R2W-Net, which consisted of two Attention R2U-Nets. In the encoder and decoder, recurrent residual modules were used to replace feedforward convolutional layers, significantly enhancing feature representation and improving noise suppression. Additionally, attention gates were used to replace skip connections, enabling precise correction of low-resolution features using deep features and further improving model performance. The decoders at both ends of the network were utilized to reconstruct FECG and MECG signals, respectively. The algorithm was validated using simulated and real datasets, achieving F1 scores of 99.17%, 98.03%, and 97.08% on the ADFECGDB, PCDB, and NIFECGDB datasets, respectively, demonstrating superior performance in both subjective visual effects and objective evaluation metrics. Attention R2W-Net’s ability to extract robustly and accurately FECG signals, even in complex noisy environments, make it a reliable tool for FECG extraction. The proposed method’s efficiency and accuracy highlight its potential for widespread clinical application, contributing to improved early diagnosis of fetal cardiac abnormalities.

This paper presents a novel method for extracting the fetal ECG (FECG) from a single-lead abdominal signal. A dynamical model for a modified abdominal signal is proposed, in which both the maternal ECG (MECG) and the FECG are modeled, and then a parallel marginalized particle filter (par-MPF) is used for tracking the abdominal signal. Finally, the FECG and MECG are simultaneously separated. Several experiments are conducted using both simulated and clinical signals. The results indicate that the method proposed in this paper effectively extracts the FECG and outperforms other Bayesian filtering algorithms.

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.

Performing a fetal electrocardiogram (ECG) analysis, which contains important information about the status of a fetal, can help to detect fetus health even before birth. Since the fetal ECG extracted from the ECG signal recorded from the mother's abdomen, this extraction problem can be seen as a source separation problem, of recovering source signals from signal mixtures. In this paper, a method for separation of fetal ECG from abdominal ECG using activation scaled non-negative matrix factorization (NMF) is proposed. The performance of the proposed method is also compared with independent component analysis. The proposed method is tested under three different scenarios. First, the original abdominal ECG signal is used for fetal separation. Second, the recovered abdominal ECG after compression is used for separation. Third, the fetal ECG is extracted from the compressed domain of the abdominal ECG. We applied scaling on the activation matrix obtained using NMF for emphasizing the fetal ECG present in abdominal ECG. The improved-regularized least-squares [Formula: see text] algorithm is used for signal reconstruction, which provides better reconstruction quality and less processing time in comparison with other existing methods. The proposed algorithm is evaluated and tested on real abdominal recordings obtained from two different datasets from Physionet. The first dataset used for this paper is Silesia dataset for abdominal and direct f-ECG, and the second dataset we considered is Set-A of the Physionet challenge. The obtained outcomes reveal that it is possible to separate fetal ECG from single-channel abdominal ECG signal, which can help us to achieve energy-efficient transmission, and cost-effective fetal ECG remote monitoring for Internet-of-Things applications, where device battery and computational capacity are limited.

Blind Source Separation (BSS) of Mixed Maternal and Fetal Electrocardiogram (ECG) Signal: A comparative Study

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%.

The fetal ECG can be detected in the recorded abdominal signals. A new procedure to compute the fetal heart rate (FHR) is proposed. The abdominal signal is first preprocessed in order to remove the baseline and the uterine contractions. Then the ECG of the mother (MECG) is removed using coherent averaging and optimizing the averaged MECG template. The channels containing the clearest fetal ECG signal (FECG) are identified by the autocorrelation function. The FECG is enhanced by the cross correlation between the two channels that show the strongest FECG. This enhancement is possible since the residual noise in the abdominal signal after removal of baseline, uterine contractions and maternal ECG is not correlated among the channels. The fetal R-Peaks are then detected and the FHR is computed. The obtained FHR is further corrected, using the information about the MECG and about the FECG.

A method applying independent component analysis (ICA) to detect the electrocardiogram of a prenatal cattle foetus is described. Three channels of signal, one is from chest lead and two are from abdominal leads, are picked up noninvasively by attaching disposable cutaneous electrodes on the body surface of a maternal cow. Measured signals are the mixtures of components including maternal ECG, foetal ECG and random noise. Such mixture procedure is expressed by a time-invariant linear model. To separate foetal ECG from maternal ECG and random noise, the ICA method is applied to rind an optimal separating matrix only using measured signals. The optimization uses hyperbolic tangent as a contrast function and minimizes it based upon the principle of mutual information. The method was examined by simulation signals generated by random mixture of two-signal and one-noise sources. Real signals, measured from a pregnant cow having 177-day gestation, are used to verify the separation. The results show that in simulation, both signal and one noise are clearly separated. Real measurement is successfully distinguished into three signal sources, maternal ECG, foetal ECG and random noise. The results suggested the effectiveness of ICA approach in detecting foetal ECG from maternal body surface measurement.

Foetal Electrocardiogram (FECG) monitoring is a technique for obtaining important information about the condition of the foetus during pregnancy and labour. In this study, we propose a new method of using combination of hybrid soft-computing technique called Adaptive Neuro Fuzzy Inference System (ANFIS) along with wavelets to estimate the Maternal Electrocardiogram (MECG) and to extract the FECG signal from the mother's Abdominal Electrocardiogram (AECG). Two methods have been developed wherein: using wavelets as a pre-processing tool combined with ANFIS; using wavelet as a post-processing tool combined with ANFIS. The results obtained by both the methods were analysed in terms of Signal to Noise Ratio (SNR), correlation coefficients and with performance indices. These proposed methods successfully removed the artefacts and extracted the desired FECG signal.

Real-time signal processing of an intrauterine catheter's fetal electrocardiogram