Search

Fetal electrocardiogram (fECG) assessment is essential throughout pregnancy to monitor the wellbeing and development of the fetus, and to possibly diagnose potential congenital heart defects. Due to the high noise incorporated in the abdominal ECG (aECG) signals, the extraction of fECG has been challenging. And it is even a lot more difficult for fECG extraction if only one channel of aECG is provided, i.e., in a compact patch device. In this paper, we propose a novel algorithm based on the Ensemble Kalman filter (EnKF) for non-invasive fECG extraction from a single-channel aECG signal. To assess the performance of the proposed algorithm, we used our own clinical data, obtained from a pilot study with 10 subjects each of 20 min recording, and data from the PhysioNet 2013 Challenge bank with labeled QRS complex annotations. The proposed methodology shows the average positive predictive value (PPV) of 97.59%, sensitivity (SE) of 96.91%, and F1-score of 97.25% from the PhysioNet 2013 Challenge bank. Our results also indicate that the proposed algorithm is reliable and effective, and it outperforms the recently proposed extended Kalman filter (EKF) based algorithm.

A retrospective aspect of prenatal complexities during pregnancy and advancements in technology shows the need for unscathed fetal ECG extraction from a single mother abdominal ECG (abdECG). The proposed work introduces a Non-invasive Single Channel Integration Technique (NSCIT) depicting a cumulative trapezoidal mathematical model with an LMS adaptive algorithm for mother and fetal ECG extraction with improved Signal-to-Noise Ratio (SNR). Besides separation, fetal ECG (fECG) features are extracted, simulated, analyzed, and compared with standards to generate fetus cardiac growth during later Gestation Period (GP) of 21st to 40th week of pregnancy. The variants of the wavelet transform, such as Dual-Tree Complex Wavelet Transform (DTCWT) for pre-processing and Maximal Overlap Discrete Wavelet Transform (MODWT) for post-processing, are exploited using a multi-resolution analysis. The NSCIT algorithm with LMS adaptive technique has shown 100% accuracy for detecting mother ECG and specific fetal ECG extraction channels. The improved accuracy using abdominal lead 4 is 96.36%, and overall abdominal mixed lead accuracy is 93.32% compared with recent existing literature. The maximum error in comparing Power Spectral Density (PSD) of actual and extracted fECG and mECG is significantly less. The calculated correlation coefficient between actual and extracted fetal QRS width, fetal R-peak intervals (R-R), and fetal heart rate (fHR) for Db1 are 0.70, 0.99, and 0.67, respectively. The research outcomes show that fECG SNR increases with GP, and it is maximum for the GP of 40th week. This fECG morphological analysis before childbirth will efficaciously contribute to sustainable fetal healthcare. Highlights: • Cumulative trapezoidal with the least mean square (LMS) adaptive filter as the separation technique of mother and fetal ECG. • Dual-tree complex wavelet transform (DTCWT) as a pre-processing technique. • The influence of the Gestation Period (GP) on improved SNR of fetal ECG. • Extracting and analyzing fetal morphological parameters using the maximal overlap discrete wavelet transform (MODWT). • Research has verified in the Physiobank public databases, namely, Abdominal and Direct fetal ECG database(adfecgdb) and non-invasive Fetal ECG Database (nifecgdb). [ABSTRACT FROM AUTHOR]

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 combined different algorithms together, yielding promising results, with the best performance in the F1 score of 92.61% achieved by an algorithm combining ICA and TS. With the data modified by adding different types of noise, the combination of ICA-TS-ICA showed the highest F1 score of 85.4%. It should be noted that these combined approaches required higher computational complexity, including execution time and allocated memory compared with other methods. Owing to comprehensive examination through various evaluation metrics in different extraction algorithms, this study provides insights into the implementation and operation of state-of-the-art fetal and maternal monitoring systems in the era of mobile health.

Extracting the Electrocardiogram (ECG) of a fetus from the ECG signal of the maternal abdomen is a challenging task due to different artifacts. The paper proposes a $N$ -tap non-causal adaptive filter (NC-AF) that update the weight by considering the $N$ number of past weights and $N-1$ number of the reference signal and error signal samples after the processing sample number $n$. Using the maternal abdominal signal as the primary signal and thorax signal as the reference input, the output $e(n)$ is obtained from the mean of $N$ number of errors. The filtering performance of NC-AF was evaluated using the Synthetic dataset and Daisy dataset with the metrics such as correlation coefficient ($\gamma$), peak root mean square difference (PRD), the output signal to noise ratio (SNR), root mean square error (RMSE), and fetal R-peak detection accuracy (FRPDA). The NC-AF provides a maximum correlation coefficient, PRD, SNR, RMSE and FRPDA of 0.9851, 83.04%, 8.52 dB, 0.208 and 97.09% respectively with filter length $N=38$. The paper also proposes the architecture of NC-AF that can be implemented in hardware like FPGA. Further, the NC-AF was implemented on Virtex-7 FPGA and its performance is evaluated in terms of resource utilization, throughput, and power consumption. For filter length $N=38$ and wordlength $L=24$ , the maximum performance of the filter can be attained with a power consumption of $\text{1.287} W$ and a maximum clock frequency of 139.47 MHz. [ABSTRACT FROM AUTHOR]

Objective. Fetal ECG (FECG) plays an important role in fetal monitoring. However, the abdominal ECG (AECG) recorded at the maternal abdomen is affected by various noises, making the extraction of FECG a challenging task. The main objective is to present a novel approach to FECG extraction using short time Fourier transform (STFT) and generative adversarial networks (GAN). Methods. Firstly, the AECG signals are transformed from one-dimensional (1D) time domain to two-dimensional (2D) time-frequency domain by using the STFT. Secondly, the 2D-STFT coefficients of FECG are estimated by the GAN model in the time-frequency domain. Finally, after the inverse STFT, the FECG can be reconstructed in the time domain. Main results. Experimental results on two databases demonstrate the effectiveness of the proposed method. Specifically, the SE, PPV and F 1 of the proposed method on PCDB are 92.37 ± 3.78%, 93.69 ± 3.96% and 93.02 ± 3.81%, respectively. And the SE, PPV and F 1 on ADFECGDB are 90.32 ± 10.70%, 89.79 ± 9.26% and 90.05 ± 9.81%, respectively. Significance. Unlike the previous studies based on the elimination of maternal ECG in the 1D time domain, the novelty of the proposed method relies on extracting the FECG directly from the AECG in the 2D time-frequency domain. It sheds some light to the topic of FECG extraction. [ABSTRACT FROM AUTHOR]

Fetal electrocardiogram (fECG) assessment is essential throughout pregnancy to monitor the wellbeing and development of the fetus, and to possibly diagnose potential congenital heart defects. Due to the high noise incorporated in the abdominal ECG (aECG) signals, the extraction of fECG has been challenging. And it is even a lot more difficult for fECG extraction if only one channel of aECG is provided, i.e., in a compact patch device. In this paper, we propose a novel algorithm based on the Ensemble Kalman filter (EnKF) for non-invasive fECG extraction from a single-channel aECG signal. To assess the performance of the proposed algorithm, we used our own clinical data, obtained from a pilot study with 10 subjects each of 20 min recording, and data from the PhysioNet 2013 Challenge bank with labeled QRS complex annotations. The proposed methodology shows the average positive predictive value (PPV) of 97.59%, sensitivity (SE) of 96.91%, and F1-score of 97.25% from the PhysioNet 2013 Challenge bank. Our results also indicate that the proposed algorithm is reliable and effective, and it outperforms the recently proposed extended Kalman filter (EKF) based algorithm.

Fetal ElectroCardioGrams (ECG) carry large amount of information, which may be critical for the monitoring of fetal well-being. Extraction of accurate fECG signal from abdominal electrocardiogram (aECG) is not possible by a basic linear filtration. This paper introduces a new approach to extract fECG signal by using Principal Component Analysis (PCA), which is a frequently used technique to extract multiple components contained in input mixed signals. In our work here the detection of R-peaks based on Pan Tomkins approach is compared with peak detector in LabVIEW Biomedical Toolkit. The paper outlines the importance of the fetal QRS (the combination of three graphical deflections Q, R and S, seen on a typical ECG) detector accuracy and its influence on the evaluation of the extraction system. [ABSTRACT FROM AUTHOR]

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 combined different algorithms together, yielding promising results, with the best performance in the F1 score of 92.61% achieved by an algorithm combining ICA and TS. With the data modified by adding different types of noise, the combination of ICA–TS–ICA showed the highest F1 score of 85.4%. It should be noted that these combined approaches required higher computational complexity, including execution time and allocated memory compared with other methods. Owing to comprehensive examination through various evaluation metrics in different extraction algorithms, this study provides insights into the implementation and operation of state-of-the-art fetal and maternal monitoring systems in the era of mobile health.

Independent component analysis (ICA) is usually used as a preliminary step for maternal electrocardiogram (ECG) QRS detection in fetal ECG extraction. When applying ICA to do this, a troublesome problem arises from how to automatically identify the separated maternal ECG component. In this paper we proposed a method called PRCH (short for Peak to peak entropy, R-R interval entropy, Correlation coefficient and Heart rate) for the automatic identifying. In the method, we defined four kinds of features, including amplitude, instantaneous heart rate, morphology and average heart rate, to characterize a signal, and determined some decision parameters through machine learning. Experiments and comparison with other three existed methods were given. Through taking metric F1 for evaluation, it showed that the proposed PRCH method has the highest identifying accuracy and generalization capability. [ABSTRACT FROM AUTHOR]

The non-invasive fetal electrocardiogram (fECG) technique has recently received considerable interest in monitoring fetal health. The aim of our paper is to propose a novel fECG algorithm based on the combination of the criteria of independent source separation and of a quality index optimization (ICAQIO-based). The algorithm was compared with two methods applying the two different criteria independently--the ICA-based and the QIO-based methods--which were previously developed by our group. All three methods were tested on the recently implemented Fetal ECG Synthetic Database (FECGSYNDB). Moreover, the performance of the algorithm was tested on real data from the PhysioNet fetal ECG Challenge 2013 Database. The proposed combined method outperformed the other two algorithms on the FECGSYNDB (ICAQIO-based: 98.78%, QIO-based: 97.77%, ICA-based: 97.61%). Significant differences were obtained in particular in the conditions when uterine contractions and maternal and fetal ectopic beats occurred. On the real data, all three methods obtained very high performances, with the QIO-based method proving slightly better than the other two (ICAQIO-based: 99.38%, QIO-based: 99.76%, ICA-based: 99.37%). The findings from this study suggest that the proposed method could potentially be applied as a novel algorithm for accurate extraction of fECG, especially in critical recording conditions.q [ABSTRACT FROM AUTHOR]

In this study, in order to verify the effectiveness of the variable-band-filters discrete wavelet transform (VBF-DWT) and construction method of the variable band filter (VBF), a fetal ECG extraction has been carried out and the main results obtained are shown as follows. The approach to configuration variable band filter (VBF) by selecting the frequency band only fetal ECG component presented is effective to configure the optimal base sensible signal. The extraction of the fetal electrocardiogram is successful by applying soft-thresholding to VBF-DWT, which uses the constructed VBF. The information extropy is selected as an evaluation index, and two kinds of ECG signal are used to evaluate the relative performance of the wavelet packet transform (WPT) and the proposed VBF-DWT. The first kind of ECG signal is synthesized using a maternal ECG, fetal ECG, and white noise; the second kind is real measured mixed noisy maternal-fetal ECG obtained using ICA. The results show that the VBF-DWT basis (designed using VBF) outperforms the WPT basis that was elected by the basis algorithm (BBA).

Background: The fundamental assumptions for various kinds of fetal electrocardiogram (fECG) extraction methods are not consistent with each other, which is a very important problem needed to be ascertained. Methods: Based on two public databases, the regularity on ECG wave durations for normal sinus rhythm is investigated statistically. Taking the ascertained regularity as an assumption, a new fECG extraction algorithm is proposed, called Partial R-R interval Resampling (PRR). Results: Both synthetic and real abdominal ECG signals are used to test the algorithm. The results indicate that the PRR algorithm has better performance over the whole R-R interval resampling based comb filtering method (RR) and linear template method (LP), which takes advantages of both LP and RR. Conclusions: The final drawn conclusion is: (1) the proposition should be true that the individual's heart beat span is invariable for normal sinus rhythm; (2) the proposed PRR fetal ECG extraction algorithm can estimate the maternal ECG (mECG) more accurately and stably even in the condition of large HRV, finally resulting in better fetal ECG extraction. [ABSTRACT FROM AUTHOR]

Abstract: This paper addresses the problem of blind source separation for underdetermined mixtures (i.e., more sources than sensors) of event-related sources that include quasi-periodic sources (e.g., electrocardiogram (ECG)), sources with synchronized trials (e.g., event-related potentials (ERP)), and amplitude-variant sources. The proposed method is based on two steps: (i) tensor decomposition for underdetermined source separation and (ii) signal extraction by Kalman filtering to recover the source dynamics. A tensor is constructed for each source by synchronizing on the “event” period of the corresponding signal and stacking different periods along the second dimension of the tensor. To cope with the interference from other sources that impede on the extraction of weak signals, two robust tensor decomposition methods are proposed and compared. Then, the state parameters used within a nonlinear dynamic model for the extraction of event-related sources from noisy mixtures are estimated from the loading matrices provided by the first step. The influence of different parameters on the robustness to outliers of the proposed method is examined by numerical simulations. Applied to clinical electroencephalogram (EEG), ECG and magnetocardiogram (MCG), the proposed method exhibits a significantly higher performance in terms of expected signal shape than classical source separation methods such as π CA and FastICA. [Copyright &y& Elsevier]

Fetal Electrocardiogram (FECG) signal provides reliable information concerning the electrophysiological state of a fetus that could assist clinicians in making appropriate decisions during pregnancy and labor. In this paper an optimal adaptive filter with a dynamic structure was designed to extract a FECG from composite maternal abdominal signal. Positive experimental results were obtained. [ABSTRACT FROM AUTHOR]

In this letter, we propose the application of the generalized eigenvalue decomposition for the decomposition of multichannel electrocardiogram (ECG) recordings. The proposed method uses a modified version of a previously presented measure of periodicity and a phase-wrapping of the RR-interval, for extracting the ldquomost periodicrdquo linear mixtures of a recorded dataset. It is shown that the method is an improved extension of conventional source separation techniques, specifically customized for ECG signals. The method is therefore of special interest for the decomposition and compression of multichannel ECG, and for the removal of maternal ECG artifacts from fetal ECG recordings. [ABSTRACT FROM PUBLISHER]

Objective. The accurate decomposition of a mother's abdominal electrocardiogram (AECG) to extract the fetal ECG (FECG) is a primary step in evaluating the fetus's health. However, the AECG is often affected by different noises and interferences, such as the maternal ECG (MECG), making it hard to evaluate the FECG signal. In this paper, we propose a deep-learning-based framework, namely 'AECG-DecompNet', to efficiently extract both MECG and FECG from a single-channel abdominal electrode recording. Approach. AECG-DecompNet is based on two series networks to decompose AECG, one for MECG estimation and the other to eliminate interference and noise. Both networks are based on an encoder-decoder architecture with internal and external skip connections to reconstruct the signals better. Main results. Experimental results show that the proposed framework performs much better than utilizing one network for direct FECG extraction. In addition, the comparison of the proposed framework with popular single-channel extraction techniques shows superior results in terms of QRS detection while indicating its ability to preserve morphological information. AECG-DecompNet achieves exceptional accuracy in the precision metric (97.4%), higher accuracy in recall and F 1 metrics (93.52% and 95.42% respectively), and outperforms other state-of-the-art approaches. Significance. The proposed method shows a notable performance in preserving the morphological information when the FECG within the AECG signal is weak., (© 2021 Institute of Physics and Engineering in Medicine.)