Maternal Abdominal Signals Research Articles

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.

One-Dimensional W-NETR for Non-Invasive Single Channel Fetal ECG Extraction.

Fetal cardiac monitoring is very helpful in the early detection of the potential risk of fetal cardiac abnormalities, which enables prompt preventative care and ensures safe births. As a result, it is crucial to regularly check on the embryonic heart. Methods of non-invasively fetal ECG extraction from maternal abdominal ECG signal are thoroughly discussed. Although fetal signals are generally obscured by maternal ECG signals and noise, extracting a clean fetal ECG is a significant difficulty. The majority of techniques for fetal ECG extraction include many extraction steps. We describe a unique method for splitting a single-channel maternal abdominal ECG into maternal and fetus ECG employing two parallel U-nets with transformer encoding, which we refer to as W-NEt TRansformers (W-NETR). Due to its enhanced capacity to simulate remote interactions and capture global context, the suggested pipeline utilizes the self-attention mechanism of the transformer. We tested the proposed pipeline on synthetic and real datasets and outperformed the current state-of-the-art deep learning models. The proposed model achieved the best results on both datasets for QRS detection precision, recall, and F1 scores. More specifically, it achieved F1 score of 99.88% and 98.9% on the real ADFECGDB and PCDB datasets, respectively. These encouraging results highlight the suggested W-NETR's effectiveness in precisely extracting the fetal ECG, which was achieved with high SSIM and PSNR values in the results. This provides the bed set for long-term maternal and fetal monitoring via portable devices as the proposed system performs real-time execution.

Extraction of Fetal ECG From Abdominal and Thorax ECG Using a Non-Causal Adaptive Filter Architecture.

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 ( γ), 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 [Formula: see text] and a maximum clock frequency of 139.47 MHz.

A Novel Cluster-Based Method for Single-channel Fetal Electrocardiogram Detection.

Fetal electrocardiography (FECG) is a promising technology for non-invasive fetal monitoring. However, due to the low amplitude and non-stationary characteristics of the FECG signal, it is difficult to extract it from maternal abdominal signals. Moreover, most FECG extraction methods are based on multiple channels, which make it difficult to achieve fetal monitoring outside the clinic. This paper proposes an efficient cluster-based method for accurate FECG extraction and fetal QRS detection only using one channel signal. We designed min-max-min group as the basis for feature extraction. The extracted features are used to distinguish the different components of the abdominal signal, and finally extract the FECG signal. To verify the effectiveness of our algorithm, we conducted experiments on a public dataset and a dataset record from the Tongji Hospital. Experimental results show that our method can achieve an accuracy rate of more than 96% which is better than other algorithms.

Swarm Decomposition of Abdominal Signals for Non-invasive Fetal ECG Extraction.

The non-invasive fetal electrocardiography (fECG) extraction from maternal abdominal signals is one of the most promising modern fetal monitoring techniques. However, the noninvasive fECG signal is heavily contaminated with noise and overlaps with other prominent signals like the maternal ECG. In this work we propose a novel approach in non-invasive fECG extraction using the swarm decomposition (SWD) to isolate the fetal components from the abdominal signal. Accompanied with the use of higher-order statistics (HOS) for R peak detection, the application of the proposed method to the Abdominal and Direct Fetal ECG PhysioNet Database resulted in fetal R peak detection sensitivity of 99.8% and a positive predictability of 99.8%. Our results demonstrate the applicability of SWD and its potentiality in extracting fECG of good morphological quality with more deep decomposition levels, in order to connect the extracted structural characteristics of the fECG with the health status of the fetus.Clinical Relevance- The developed method shows improvement in fetal R peak detection for certain signals.

Optimization of monitoring of contractile uterus activity and fetus status in women with the threat of preterm birth

Research objective: to study the use of uterine activity and variables of acceleration capacity (AC) and deceleration capacity (DC) in uterine contractile activity and fetal well-being monitoring in women at risk of preterm birth.Materials and methods. 292 pregnant women were included in the prospective study. All involved women underwent ultrasound cervicometry at 16 weeks. 124 pregnant women with a “short cervix” of the III (main) group were observed in the dynamics, as well as uterine activity and fetal AC/DC at 26, 32 and 38 weeks of gestation, and during labor were studied. In 112 women of group II the variables of fetal AC/DC were detected at these terms of pregnancy. They were monitored via conventional cardiotocography during labor. 56 pregnant women in group I with normal cervicometry were monitored.Results. Sensitivity and specificity of the diagnosis of the threatened preterm birth in the main group was 97.30% and 94.74% respectively. Diagnostic accuracy in the case of uterine activity according to fetal non-invasive electrocardiography was 96.18%. Sensitivity and specificity of the diagnosis of the threatened preterm birth in the comparison group were significantly lower: 89.29% and 87.80% respectively. The diagnostic accuracy was 88.41%.Patients in all clinical groups at 26 weeks had low AC/DC values. However, later in women of group I was found highest AC/DC level. AC/DC values in patients with risk of preterm birth were reduced compared with controls (p < 0.05). Sensitivity and specificity of the diagnosis of fetal distress if AC/DC was used were 91.67% and 99.12% respectively. Diagnostic accuracy of the test was 98.41%. In the comparison group the sensitivity was 77.78%, and the specificity was 89.22%. Diagnostic accuracy of intranatal cardiotocography was 86.82%.Conclusions. Uterine activity extracted from the maternal abdominal signal can significantly improve the diagnosis of the threatenedpreterm delivery. Patients at risk of preterm birth have a delayed fetal neurological, which leads to fetal distress. AC/DC variables obtained via fetal non-invasive electrocardiography allow increasing the accuracy of fetal distress diagnosis.

LabVIEW-based cardiac risk assessment of foetal ECG signal extracted from maternal abdominal signal

Mathematical computational processing of abdominal foetal ECG has proved to be beneficial in crucial diagnosis of complex cardiac diseases. To arrive at diagnosis, a cardiologist needs to observe the variations critically in the duration and amplitude of different waves of the ECG which may otherwise result in permanent damage to the brain and nervous system. In this paper, the foetal cardiac signal has been extracted from a composite abdominal signal in LabVIEW environment using Independent Component Analysis (ICA) approach, implemented after the application of hybrid filters, with signal taken from PhysioNet Database. Results obtained demonstrate that the denoised signal is approximately 99% accurate and the ICA implementation helps to determine foetal heart rate successfully with low computational complexity. The performance of the proposed algorithm has also been explored in the case of twin pregnancy. The estimated heart rate is comparable to the actual heart rate, which validates the algorithm's accuracy.

LabVIEW-based cardiac risk assessment of foetal ECG signal extracted from maternal abdominal signal

LabVIEW-based cardiac risk assessment of foetal ECG signal extracted from maternal abdominal signal

Non-invasive fetal ECG extraction using discrete wavelet transform recursive inverse adaptive algorithm.

Fetal heart activity adds significant information about the status of the fetus health. Early diagnosis of issues in the heart before delivery allows early intervention and significantly improves the treatment. This paper presents a new adaptive filtering algorithm for fetal electrocardiogram (FECG) extraction from the maternal abdominal signal, known in literature as abdominal electrocardiogram (AECG) signal. Fetal QRS complex waves will be identified and extracted accurately for fetal health care and monitoring purposes. We use discrete wavelet transform recursive inverse (DWT-RI) adaptive filtering algorithm for this objective. Thoracic maternal electrocardiogram (MECG) is used as a reference in the proposed algorithm and FECG components are extracted from AECG signal after suppressing the MECG projections. The proposed algorithm is compared to other typical adaptive filtering algorithms, least mean squares (LMS), recursive least squares (RLS), and recursive inverse (RI). Fetal QRS waveforms successful identification and extraction from AECG signal is evaluated objectively and visually and compared to other algorithms. We validated the proposed algorithm using both synthetic data and real clinical data. The proposed algorithm is capable of extracting fetal QRS waveforms successfully from AECG and outperforms other adaptive filtering algorithms in terms of accuracy and positive predictivity.

Single-lead noninvasive fetal ECG extraction by means of combining clustering and principal components analysis.

Early detection of potential hazards in the fetal physiological state during pregnancy and childbirth is very important. Noninvasive fetal electrocardiogram (FECG) can be extracted from the maternal abdominal signal. However, due to the interference of maternal electrocardiogram and other noises, the task of extraction is challenging. This paper introduces a novel single-lead noninvasive fetal electrocardiogram extraction method based on the technique of clustering and PCA. The method is divided into four steps: (1) pre-preprocessing; (2) fetal QRS complexes and maternal QRS complexes detection based on k-means clustering algorithm with the feature of max-min pairs; (3) FQRS correction step is to improve the performance of step two; (4) template subtraction based on PCA is introduced to extract FECG waveform. To verify the performance of the proposed algorithm, two clinical open-access databases are used to check the performance of FQRS detection. As a result, the method proposed shows the average PPV of 95.35%, Se of 96.23%, and F1-measure of 95.78%. Furthermore, the robustness test is carried out on an artificial database which proves that the algorithm has certain robustness in various noise environments. Therefore, this method is feasible and reliable to detect fetal heart rate and extract FECG. Graphical abstract Early detection of potential hazards in the fetal physiological state during pregnancy and childbirth is very important. Noninvasive fetal electrocardiogram (FECG) can be extracted from maternal abdominal signal. However, due to the interference of maternal electrocardiogram and other noises, the task of extraction is challenging. This paper introduces a novel single-lead noninvasive fetal electrocardiogram extraction method based on the technique of clustering and PCA. The method is divided into four steps: (1) pre-preprocessing; (2) fetal QRS complexes and maternal QRS complexes detection based on k-means clustering algorithm with the feature of max-min pairs; (3) FQRS correction step is to improve the performance of step two; (4) template subtraction based on PCA is introduced to extract FECG waveform. To verify the performance of algorithm, two clinical open-access databases are used to check the performance of FQRS detection. As a result, the method proposed shows the average PPV of 95.35%, Se of 96.23%, and F1-measure of 95.78%. Furthermore, the robustness test is carried out on an artificial database which proves that the algorithm has certain robustness in various noise environments. Therefore, this method is feasible and reliable to detect fetal heart rate and extract FECG.

Noninvasive fetal ECG extraction using doubly constrained block-term decomposition.

Fetal electrocardiogram (fECG) monitoring is a beneficial method for assessing fetal health and diagnosing the fetal cardiac condition during pregnancy. In this study, an algorithm is proposed to extract fECG from maternal abdominal signals based on doubly constrained block-term (DoCoBT) tensor decomposition. This tensor decomposition method is constrained by quasiperiodicity constraints of fetal and maternal ECG signals. Tensor decompositions are more powerful tools than matrix decomposition, due to employing more information for source separation. Tensorizing abdominal signals and using periodicity constraints of fetal and maternal ECG, appropriately separates subspaces of the mother, the fetus(es) and noise. The quantitative and qualitative results of the proposed method show improved performance of DoCoBT decomposition versus other tensor and matrix decomposition methods in noisy conditions.

A Fetal ECG Monitoring System Based on the Android Smartphone.

In this paper, a fetal electrocardiogram (ECG) monitoring system based on the Android smartphone was proposed. We designed a portable low-power fetal ECG collector, which collected maternal abdominal ECG signals in real time. The ECG data were sent to a smartphone client via Bluetooth. Smartphone app software was developed based on the Android system. The app integrated the fast fixed-point algorithm for independent component analysis (FastICA) and the sample entropy algorithm, for the sake of real-time extraction of fetal ECG signals from the maternal abdominal ECG signals. The fetal heart rate was computed using the extracted fetal ECG signals. Experimental results showed that the FastICA algorithm can extract a clear fetal ECG, and the sample entropy can correctly determine the channel where the fetal ECG is located. The proposed fetal ECG monitoring system may be feasible for non-invasive, real-time monitoring of fetal ECGs.

Multiple Sub-Filter Adaptive Noise Canceller for Fetal ECG Extraction

Abstract Non-invasive monitoring of fetal cardiac signs through the signal processing of maternal abdominal signals has been emerging as a promising technique in the field of obstetrics and gynaecology. The underlying challenges in obtaining the Fetal Electrocardiogram (FECG) from maternal abdominal signals include elimination of dominant maternal ECG and other noises. The simple technique based on adaptive noise cancellers uses Single Long Filter (SLF) as adaptive filter for extracting the FECG. In this paper, we propose an algorithm for implementation of adaptive filter in adaptive noise canceller (ANC) as Multiple Sub-Filters (MSF). Each of the adaptive sub-filters are updated using the Least Mean Square (LMS) algorithm. The proposed MSF architecture is compared with traditional SLF ANC by testing using a real data from Daisy database. From the obtained results, it is observed that MSF-ANC performs better than SLF-ANC with 91.66% positive predictive value and 84.61% accuracy.

An Improved FastICA Method for Fetal ECG Extraction.

Objective The fast fixed-point algorithm for independent component analysis (FastICA) has been widely used in fetal electrocardiogram (ECG) extraction. However, the FastICA algorithm is sensitive to the initial weight vector, which affects the convergence of the algorithm. In order to solve this problem, an improved FastICA method was proposed to extract fetal ECG. Methods First, the maternal abdominal mixed signal was centralized and whitened, and the overrelaxation factor was incorporated into Newton's iterative algorithm to process the initial weight vector randomly generated. The improved FastICA algorithm was used to separate the source components, selected the best maternal ECG from the separated source components, and detected the R-wave location of the maternal ECG. Finally, the maternal ECG component in each channel was removed by the singular value decomposition (SVD) method to obtain a clean fetal ECG signal. Results An annotated clinical fetal ECG database was used to evaluate the improved algorithm and the conventional FastICA algorithm. The average number of iterations of the algorithm was reduced from 35 before the improvement to 13. Correspondingly, the average running time was reduced from 1.25 s to 1.04 s when using the improved algorithm. The signal-to-noise ratio (SNR) based on eigenvalues of the improved algorithm was 1.55, as compared to 0.99 of the conventional FastICA algorithm. The SNR based on cross-correlation coefficients of the conventional algorithm was also improved from 0.59 to 2.02. The sensitivity, positive predictive accuracy, and harmonic mean (F1) of the improved method were 99.37%, 99.00%, and 99.19%, respectively, while these metrics of the conventional FastICA method were 99.03%, 98.53%, and 98.78%, respectively. Conclusions The proposed improved FastICA algorithm based on the overrelaxation factor, while maintaining the rate of convergence, relaxes the requirement of initial weight vector, avoids the unbalanced convergence, reduces the number of iterations, and improves the convergence performance.

A novel approach to the extraction of fetal electrocardiogram based on empirical mode decomposition and correlation analysis.

Fetal heart rate monitoring is the process of checking the condition of the fetus during pregnancy and it would allow doctors and nurses to detect early signs of trouble during labor and delivery. The fetal ECG (FECG) signal is so weak and also is corrupted by other signals and noises, mainly by maternal ECG signal. It is so hard to acquire a noise-free, precise and reliable FECG using the conventional methods. In this study, a combination of empirical mode decomposition (EMD) algorithms, correlation and match filtering is used for extracting FECG from maternal abdominal ECG signals. The proposed method benefits from match filtering ability to detect fetal signal and QRS complex to detect weak QRS peaks. The combined method, has been applied successfully on different signal qualities, even for signals that their analysis was hard and complicated for other methods. This method is able to detect R-R intervals with high accuracy. It was proved that the complete ensemble empirical mode decomposition method provides a better frequency resolution of modes and also requires less iterations that leads to a considerably less computational cost than EMD and ensemble empirical mode decomposition and can reconstruct the FECG completely from the calculated modes. We believe that this method opens a new field in non-invasive maternal abdominal signal processing so the FECG signal could be extracted with high speed and accuracy.

Efficient Fetal-Maternal ECG Signal Separation from Two Channel Maternal Abdominal ECG via Diffusion-Based Channel Selection.

There is a need for affordable, widely deployable maternal-fetal ECG monitors to improve maternal and fetal health during pregnancy and delivery. Based on the diffusion-based channel selection, here we present the mathematical formalism and clinical validation of an algorithm capable of accurate separation of maternal and fetal ECG from a two channel signal acquired over maternal abdomen. The proposed algorithm is the first algorithm, to the best of the authors' knowledge, focusing on the fetal ECG analysis based on two channel maternal abdominal ECG signal, and we apply it to two publicly available databases, the PhysioNet non-invasive fECG database (adfecgdb) and the 2013 PhysioNet/Computing in Cardiology Challenge (CinC2013), to validate the algorithm. The state-of-the-art results are achieved when compared with other available algorithms. Particularly, the F1 score for the R peak detection achieves 99.3% for the adfecgdb and 87.93% for the CinC2013, and the mean absolute error for the estimated R peak locations is 4.53 ms for the adfecgdb and 6.21 ms for the CinC2013. The method has the potential to be applied to other fetal cardiogenic signals, including cardiac doppler signals.

Sequential separation of twin pregnancy electrocardiograms

Abstract We propose to tackle the problem of maternal abdominal electric signals decomposition with a combined application of independent component analysis and projective or adaptive filtering. The developed method is employed to process the four-channel abdominal signals recorded during twin pregnancy. These signals are complicated mixtures of the maternal ECG, the ECGs of the fetal twins and noise of various origin. Although the independent component analysis cannot separate the respective signals, the proposed combination of the methods deals with this task successfully. A simulation experiment confirms high efficiency of this approach.

The Use of Fetal Noninvasive Electrocardiography.

Preeclampsia (PE) is one of the severe complications of pregnancy that leads to fetal deterioration. The aim was to survey the validity of fetal distress diagnostics in case of Doppler ultrasonic umbilical vein and arteries blood flow velocity investigation and ECG parameters analysis obtained from maternal abdominal signal before labor in preeclamptic patients. Fetal noninvasive ECG and umbilical arterial and venous Doppler investigation were performed in 120 patients at 34–40 weeks of gestation. And 30 of them had physiological gestation and were involved in Group I. In Group II 52 pregnant women with mild-moderate PE were observed. 38 patients with severe PE were monitored in Group III. The most considerable negative correlation was determined in pair Apgar score 1 versus T/QRS (R = −0.50; p < 0.05). So the increased T/QRS ratio was the most evident marker of fetal distress. Fetal noninvasive ECG showed sensitivity of 96.6% and specificity of 98.4% and, therefore, was determined as more accurate method for fetal monitoring.

Novel Possibilities for the Diagnosis of Fetal Distress in Pre-eclamptic Women

ABSTRACTObjectiveFetal deterioration has been known as a result of acute or chronic disturbances in the system of ‘motherplacenta- fetus’. Pre-eclampsia (PE) is a maternal gestational disease caused by abnormal placentation that could provoke fetal distress.AimThe aim of the investigation was to survey the accuracy of fetal distress diagnostics in case of traditional cardiotocography (CTG) waveform evaluation and electrocardiography (ECG) parameters analysis obtained from maternal abdominal signal before and during labor in pre-eclamptic patients.Materials and methodsOne hundred and thirty-nine pregnant ladies at 32 to 40 weeks of gestation were enrolled in the study. There were 30 women with physiological pregnancy and normal fetal condition according to hemodynamic Doppler values and fetal ECG parameters in group I. In group II, 64 patients with mild and moderate PE were involved whom were performed fetal ECG noninvasive monitoring and hemodynamic Doppler. In group III, 45 patients with severe PE were monitored with fetal noninvasive ECG and hemodynamic Doppler.ResultsIt was determined that fetal autonomic tone was suppressed with the relative increase of low frequency branch in pre-eclamptic patients. The increased value of the amplitude of mode and stress index was associated with abnormal myocardial adrenergic stimulation. It has induced pQ and QT shortening, increased T/QRS ratio. Fetal cardiac response was determined as gradual reduce of autonomic regulation.The most considerable negative correlation was determined in pair Apgar score 1 vs T/QRS (R = – 0.48; p &lt; 0.05). The QT interval duration has revealed positive correlation with Apgar score 1 (R = 0.44; p &lt; 0.05). Additional to the conventional CTG analysis evaluation of noninvasive fetal ECG parameters has contributed to better diagnostic of distress.ConclusionFetal noninvasive ECG has showed sensitivity 100.0% and specificity 93.8% and, therefore, was more accurate and convenient method for fetal monitoring than conventional CTG.How to cite this articleLakhno IV. Novel Possibilities for the Diagnosis of Fetal Distress in Pre-eclamptic Women. Int J Infertil Fetal Med 2015;6(2):62-66.

Spatio-Temporal FIR Filter for Fetal ECG Extraction

In this paper we propose to apply spatio-temporal filtering to fetal ECG extraction from the multichannel maternal abdominal bioelectric signals. The method performs weighted addition of the assumed number of time samples of the respective measured signal channels. The filter weights are calculated during the learning phase of the method. After this learning phase, the filter operation is very simple, similar to usual filtering, nevertheless extraordinarily effective. Our experiments show that it can simultaneously accomplish maternal ECG suppression and fetal QRS complexes enhancement for their detection. The detection tests are performed to show the method influence on this crucial operation.