Fast Technique for Noninvasive Fetal ECG Extraction

Sensors

This paper presents a new non-invasive deterministic algorithm of extracting the fetal Electrocardiogram (FECG) signal based on a new null space idempotent transformation matrix (NSITM). The mixture matrix is used to compute the ITM. Then, the fetal ECG (FECG) and maternal ECG (MECG) signals are extracted from the null space of the ITM. Next, MECG and FECG peaks detection, control logic, and adaptive comb filter are used to remove the unwanted MECG component from the raw FECG signal, thus extracting a clean FECG signal. The visual results from Daisy and Physionet real databases indicate that the proposed algorithm is effective in extracting the FECG signal, which can be compared with principal component analysis (PCA), fast independent component analysis (FastICA), and parallel linear predictor (PLP) filter algorithms. Results from Physionet synthesized ECG data show considerable improvement in extraction performances over other algorithms used in this work, considering different ad...

Blind source separation (BSS) is used in many fields of signal and image processing. It is used for separating a set of source signals from mixed signals without the aid of information about the source signals or the mixing process. The paper mainly focuses on (1) Separation of maternal and fetal ECG signal (2) Performance measure of various BSS and JBSS algorithm in term of SIR and execution time. Various BSS algorithm like ICA, FASTICA, JADE and JBSS algorithms like MCCA, SOBI, JBSS_SOS, JBSS_CUM4 are used for blindly separating the source signals. And the impacts on separation of maternal and fetal signal are examined. The simulations are conducted in MATLAB using Non-Invasive ECG of pregnant women from PhysioNet database. The JBSS_CUM4 algorithm shows better performance with the SIR value of 29.58dB for MECG and 7.44dB for FECG.

Theoretical Biology and Medical Modelling, 2015

Background: The electrocardiogram (ECG) is a diagnostic tool that records the electrical activity of the heart, and depicts it as a series of graph-like tracings, or waves. Being able to interpret these details allows diagnosis of a wide range of heart problems. Fetal electrocardiogram (FECG) extraction has an important impact in medical diagnostics during the mother pregnancy period. Since the observed FECG signals are often mixed with the maternal ECG (MECG) and the noise induced by the movement of electrodes or by mother motion, the separation process of the ECG signal sources from the observed data becomes quite complicated. One of its complexity is when the ECG sources are dependent, thus, in this paper we introduce a new approach of blind source separation (BSS) in the noisy context for both independent and dependent ECG signal source. This approach consist in denoising the observed ECG signals using a bilateral total variation (BTV) filter; then minimizing the Kullbak-Leibler divergence between copula densities to separate the FECG signal from the MECG one. Results: We present simulation results illustrating the performance of our proposed method. We will consider many examples of independent/dependent source component signals. The results will be compared with those of the classical method called independent component analysis (ICA) under the same conditions. The accuracy of source estimation is evaluated through a criterion, called again the signal-to-noise-ratio (SNR). The first experiment shows that our proposed method gives accurate estimation of sources in the standard case of independent components, with performance around 27 dB in term of SNR. In the second experiment, we show the capability of the proposed algorithm to successfully separate two noisy mixtures of dependent source components-with classical criterion devoted to the independent case-fails, and that our method is able to deal with the dependent case with good performance. Conclusions: In this work, we focus specifically on the separation of the ECG signal sources taken from skin two electrodes located on a pregnant woman's body. The ECG separation is interpreted as a noisy linear BSS problem with instantaneous mixtures. Firstly, a denoising step is required to reduce the noise due to motion artifacts using a

Lecture Notes in Computer Science, 2004

Recently, non-invasive techniques to measure the fetal electrocardiogram (FECG) signal have given very promising results. However, the important question of the number and the location of the external sensors has been often discarded. In this paper, an electrode-array approach is proposed; it is combined with a sensor selection algorithm using a mutual information criterion. The sensor selection algorithm is run in parallel to an independent component analysis of the selected signals. The aim of this method is to make a real time extraction of the FECG possible. The results are shown on simulated biomedical signals.

Advances in Electrical and Electronic Engineering

Abdominal fetal ElectroCardioGrams (fECGs) carry a wealth of information about the fetus including fetal Heart Rate (fHR) and signal morphology during different stages of pregnancy. Here we report our results on the implementation and evaluation of two non-adaptive signal processing methods suitable for fECG signal extraction, namely: the Independent Component Analysis (ICA) and the Principal Component Analysis (PCA) Methods. We used the fetal heart rate extracted from fECG signals (in Beats Per Minute-BPM) and Signal-to-Noise Ratio (SNR) as effective performance evaluation metrics for our applied methods. Our findings demonstrated that given adequate SNR, these methods produced excellent results in accurate determination of fHR. Furthermore, we found out that compared to the PCA Method, the ICA Method produces a lower variance in the detection of the fHR.

IEEE Transactions on Biomedical Engineering, 2001

The problem of the fetal electrocardiogram (FECG) extraction from maternal skin electrode measurements can be modeled from the perspective of blind source separation (BSS). Since no comparison between BSS techniques and other signal processing methods has been made, we compare a BSS procedure based on higher-order statistics and Widrow's multireference adaptive noise cancelling approach. As a best-case scenario for this latter method, optimal Wiener-Hopf solutions are considered. Both procedures are applied to real multichannel ECG recordings obtained from a pregnant woman. The experimental outcomes demonstrate the more robust performance of the blind technique and, in turn, verify the validity of the BSS model in this important biomedical application.

Frontiers in physiology, 2018

Non-adaptive signal processing methods have been successfully applied to extract fetal electrocardiograms (fECGs) from maternal abdominal electrocardiograms (aECGs); and initial tests to evaluate the efficacy of these methods have been carried out by using synthetic data. Nevertheless, performance evaluation of such methods using real data is a much more challenging task and has neither been fully undertaken nor reported in the literature. Therefore, in this investigation, we aimed to compare the effectiveness of two popular non-adaptive methods (the ICA and PCA) to explore the non-invasive (NI) extraction (separation) of fECGs, also known as NI-fECGs from aECGs. The performance of these well-known methods was enhanced by an adaptive algorithm, compensating amplitude difference and time shift between the estimated components. We used real signals compiled in 12 recordings (real01-real12). Five of the recordings were from the publicly available database (PhysioNet-Abdominal and Direc...

2007

In this study we compare the performance of six independent components analysis (ICA) algorithms on 16 real fetal magnetocardiographic (fMCG) datasets for the application of extracting the fetal cardiac signal. We also compare the extraction results for real data with the results previously obtained for synthetic data. The six ICA algorithms are FastICA, CubICA, JADE, Infomax, MRMI-SIG and TDSEP. The results obtained using real fMCG data indicate that the FastICA method consistently outperforms the others in regard to separation quality and that the performance of an ICA method that uses temporal information suffers in the presence of noise. These two results confirm the previous results obtained using synthetic fMCG data. There were also two notable differences between the studies based on real and synthetic data. The differences are that all six ICA algorithms are independent of gestational age and sensor dimensionality for synthetic data, but depend on gestational age and sensor dimensionality for real data. It is possible to explain these differences by assuming that the number of point sources needed to completely explain the data is larger than the dimensionality used in the ICA extraction.

2008

Fetal ECG (FECG) extraction from maternal abdominal potential recordings is a task of paramount importance for pediatric cardiologists, but there is a lack of established solutions for it. In this paper the real-time implementation of a block-on-line Independent Component Analysis (ICA) algorithm for FECG extraction is presented and evaluated over real long lasting recordings. The problem of the signals permutation, typical of ICA algorithms and particularly severe for block-on-line ones, is analyzed in detail. The comparison with batch approaches applied to different segments of the signals demonstrates the quality of the proposed solution. The performances of the real-time implementation enable further developments of the system to automatically provide other interesting clinical parameters.

2019

Accurate detection and monitoring of fetal electrocardiography (fECG) and its clinical application is being examined under several studies. Knowledge of fetal cardiac parameters is critical for taking prompt clinical decisions for effective antenatal and neonatal care. This paper presents an approach for extracting fECG from the maternal electrical abdominal signal, non-invasively, using a single electrode. We have implemented the Pan-Tompkins algorithm for detecting R peaks from maternal ECG (mECG), Principal Component Analysis (PCA) for mECG attenuation, and subsequently, an improved Pan-Tompkins algorithm for detecting fetal R peaks. The algorithm was implemented on a database obtained from an online repository, Physionet, and validated using the corresponding scalp fetal ECG, which is the gold standard.

Physics in Medicine and Biology, 2006

Independent component analysis (ICA) algorithms have been successfully used for signal extraction tasks in the field of biomedical signal processing. We studied the performances of six algorithms (FastICA, CubICA, JADE, Infomax, TDSEP and MRMI-SIG) for fetal magnetocardiography (fMCG). Synthetic datasets were used to check the quality of the separated components against the original traces. Real fMCG recordings were simulated with linear combinations of typical fMCG source signals: maternal and fetal cardiac activity, ambient noise, maternal respiration, sensor spikes and thermal noise. Clusters of different dimensions (19, 36 and 55 sensors) were prepared to represent different MCG systems. Two types of signal-to-interference ratios (SIR) were measured. The first involves averaging over all estimated components and the second is based solely on the fetal trace. The computation time to reach a minimum of 20 dB SIR was measured for all six algorithms. No significant dependency on gestational age or cluster dimension was observed. Infomax performed poorly when a sub-Gaussian source was included; TDSEP and MRMI-SIG were sensitive to additive noise, whereas FastICA, CubICA and JADE showed the best performances. Of all six methods considered, FastICA had the best overall performance in terms of both separation quality and computation times.

The paper describes a blind filtering system and algorithm for non-invasive retrieval of the fetal ECG in gestational weeks 20-30, from maternal abdominal surface-electrodes. Results are shown where the timing of the P and T waves of the fetal ECG are successfully retrieved, Early non-invasive diagnosis of these components early in pregnancy is essential for possible treating certain fetal cardiac defect with even oral medication to the mother.

Technologies, 2020

Monitoring of fetal electrocardiogram (fECG) would provide useful information about fetal wellbeing as well as any abnormal development during pregnancy. Recent advances in flexible electronics and wearable technologies have enabled compact devices to acquire personal physiological signals in the home setting, including those of expectant mothers. However, the high noise level in the daily life renders long-entrenched challenges to extract fECG from the combined fetal/maternal ECG signal recorded in the abdominal area of the mother. Thus, an efficient fECG extraction scheme is a dire need. In this work, we intensively explored various extraction algorithms, including template subtraction (TS), independent component analysis (ICA), and extended Kalman filter (EKF) using the data from the PhysioNet 2013 Challenge. Furthermore, the modified data with Gaussian and motion noise added, mimicking a practical scenario, were utilized to examine the performance of algorithms. Finally, we comb...

Animal Science Journal, 2004

The fetal heart rate is indispensable for monitoring the health of unborn cattle fetuses. To monitor the fetal heart rate, a method employing independent component analysis (ICA) to extract the fetal electrocardiogram (fECG) from potentials measured on the maternal body surface and composed of a mixture of the maternal ECG (mECG), fECG, baseline drift and noise is described. A mixing of the raw data was simplified using a linear time-invariant model. To separate the fECG from the mECG, baseline drift, and noise, an ICA strategy was applied, using a hyperbolic tangent as the contrast function and treating mutual information with the minimization principle to find the optimum demixing matrix to derive the fECG from the measured signals. After the feasibility of this method was shown on simulated signals obtained by randomly mixing pure fECG, pure mECG, low frequency sinusoidal drift and noise, real signals from three cloned pregnant Holstein cows with 157, 177 and 224-day gestation periods were used to verify the separation method. The results show that the fECG, mECG, low-frequency sinusoidal drift and noise can be clearly segregated in simulations, and that the fECG, mECG, baseline drift and noise can be successfully derived from real signals. The ICA approach has great potential in effectively detecting the fECG from maternal body surface potentials.

IEEE Transactions on Biomedical Engineering, 2005

This paper addresses the problem of fetal electrocardiogram extraction using blind source separation (BSS) in the wavelet domain. A new approach is proposed, which is particularly advantageous when the mixing environment is noisy and time-varying, and that is shown, analytically and in simulation, to improve the convergence rate of the natural gradient algorithm. The distribution of the wavelet coefficients of the source signals is then modeled by a generalized Gaussian probability density, whereby in the time-scale domain the problem of selecting appropriate nonlinearities when separating mixtures of both sub-and super-Gaussian signals is mitigated, as shown by experimental results.