Extracting Fetal ECG Using Least Square Algorithm to Corresponding Points in Phase Space Constructed by Multi-Channel Signals

Fetal heart rate estimation using fractional Fourier transform and wavelet analysis

In this paper, we investigate the use of adaptive neuro-fuzzy inference systems (ANFIS) for fetal electrocardiogram (FECG) extraction from two ECG signals recorded at the thoracic and abdominal areas of the mother's skin. The thoracic ECG is assumed to be almost completely maternal (MECG) while the abdominal ECG is considered to be composite as it contains both the mother's and the fetus' ECG signals. The maternal component in the abdominal ECG signal is a nonlinearly transformed version of the MECG. We use an ANFIS network to identify this nonlinear relationship, and to align the MECG signal with the maternal component in the abdominal ECG signal. Thus, we extract the FECG component by subtracting the aligned version of the MECG signal from the abdominal ECG signal. We validate our technique on both real and synthetic ECG signals. Our results demonstrate the effectiveness of the proposed technique in extracting the FECG component from abdominal signals of very low maternal to fetal signal-to-noise ratios. The results also show that the technique is capable of extracting the FECG even when it is totally embedded within the maternal QRS complex.

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.

The extraction of Fetal ECG (FECG) is still a difficult task in non-invasive approach since the frequency components of dominant maternal ECG and fetal ECG signals are generally overlapped. Besides, the baseline wander and high-frequency noise make the clear FECG difficult to be extracted. In this paper, we proposed a new combination of independent component analysis (ICA), improved complete ensemble empirical mode decomposition (ICEEMDAN), and wavelet shrinkage (WS) de-noising (ICA-ICEEMDAN-WS) to extract FECG while reducing the noise. ICA algorithm as the first step of our proposed method separated the mixed abdominal ECG signal to obtain the noisy FECG. Then for further denoisng and obtain higher SNR, noisy FECG was decomposed by the newly proposed ICEEMDAN that is more accurate for non-linear and non-stationary bio-signal processing. The informative extracted components were then determined based on the statistical significance test. WS de-noising removed the remained noisy subcomponents and finally the baseline wander was reduced by partial reconstruction. The performance of ICA-ICEEMDAN-WS method was evaluated using simulated and real data sets. Our results showed that our proposed method outperformed ICA-EEMD-WS, the recently proposed algorithm based on ensemble empirical mode decomposition

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.

A new method based on v-support vector regression (v-SVR) is proposed to extract the fetal electrocardiogram (FECG) from the abdominal signal recorded at the abdominal areas of the pregnant woman. The maternal electrocardiogram (MECG) component in the abdominal signal is a non-linearly transformed version of the MECG and the non-linear transform is estimated by v-SVR. The optimal estimation of the MECG component is obtained by the MECG undergoing the estimated non-linear transform. Then the FECG is extracted by subtracting the estimated MECG component from the abdominal signal. The method is validated by the experiments on both synthetic and real electrocardiogram (ECG) signals. The visual results and signal-to-noise ratio (SNR) are used to evaluate the performance of the FECG extraction methods. The experimental results indicated that the proposed method can be used for extracting the FECG from the abdominal signal.

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%.

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.

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

Heart disease is one of the critical diseases and World Health Organization (WHO) shows that large parentage of people is dying due to heart disease. But, the timely and accurate monitoring of heart disease still remains as a challenging problem. This paper proposes a portable biomedical device for monitoring fetal ECG signal from the abdominal signal and analysis of the fetal heart rate variability (FHR) to ensure the fetal wellbeing. The portable device for monitoring FECG was implemented using Arduino and AD8232 ECG sensor module. In this work, Fetal ECG extraction is done with a single composite maternal ECG signal. The ECG signal is processed using Wavelet transform for fetal ECG signal extraction. The experimentally observed results show the better extraction of FECG and that could lead towards the appropriate analysis of heart disease.

Fetal heart rate monitoring through non-invasive electrocardiography is of great relevance in clinical practice to supervise the fetal health during pregnancy. However, the analysis of fetal ECG is considered a challenging problem for biomedical and signal processing communities. This is mainly due to the low signal-to-noise ratio of fetal ECG and the difficulties in cancellation of maternal QRS complexes, motion, etc. This paper presents a survey of different unsupervised classification algorithms for the detection of fetal QRS complexes from abdominal ECG signals. Concretely, clustering algorithms are applied to classify signal features into noise, maternal QRS complexes and fetal QRS complexes. Hierarchical, k-means, k-medoids, fuzzy c-means, and dominant sets were the selected algorithms for this work. A MATLAB GUI has been developed to automatically apply the clustering algorithms and display FHR monitoring. Real abdominal ECG signals have been used for this study, which validate the proposed method and show high efficiency.

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.

Non-invasive extraction of fetal electrocardiogram (FECG) by processing the abdominal signals is emerging as a promising approach in the areas of obstetrics and gynecology. This paper presents a two-stage improved non-linear adaptive filter for FECG extraction. The reference input to the adaptive noise canceler (ANC) is first processed using an adaptive neuro-fuzzy inference system (ANFIS) to estimate the non-linear maternal component in abdominal signals. A parallel sub-filter (PSF) ANC is proposed to assess the fetal ECG from the abdominal signal. The PSF-ANC decomposes a single adaptive filter into multiple sub-filters to improve the convergence performance. The filter coefficients of PSF-ANC adaptively obtained using normalised least mean square algorithm by minimizing the mean square error. Different error and common error algorithms are proposed based on the computation of the error signal. A synthetic data from the FECG synthetic database is used to evaluate the convergence performance. Two real-time data from the Daisy database and the Non-invasive FECG database from Physionet are used to evaluate the proposed ANFIS-PSF's performance qualitative and quantitatively. The results justify the performance improvement of proposed ANFIS-PSF ANC compared to the state of art techniques. The proposed scheme achieves a sensitivity of 97.92%, 94.52% accuracy, a positive predictive value of 94.66%, and an F1 score of 96.12%.

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.

Analysis of the fetal ECG (fECG) allows physicians to detect changes in the well-being state of the fetus. But when assessing the fECG through the abdominal signals (ADS), its very low amplitude causes a problem, as the fECG representation in the ADS is buried in a mixture of other signals with stronger energy. Different methods have been proposed in the past to extract the transabdominal fECG for instantaneous fetal heart rate (fHR) computation; four representatives of them are selected for an accurate comparison of their performance in fECG extraction and in fHR estimation. A model for the ADS including all the possible disturbances is developed within this study to generate simulated data as they are required for the quantitative comparison of the algorithms. Their performances and limits considering both the enhancement of the fECG and the ability to preserve fECG morphology are analyzed using the simulated data. The results clearly show that linear methods for maternal ECG removal provide better results with respect to the extraction of the fECG morphology. The algorithms are then tested on real ADS data recorded during labor. Finally, the advantage of considering linear methods for ADS processing is discussed.