Mother ECG Research Articles

A novel deep learning technique for morphology preserved fetal ECG extraction from mother ECG using 1D-CycleGAN

The non-invasive fetal electrocardiogram (fECG) enables easy detection of developing heart abnormalities, leading to a significant reduction in infant mortality rate and post-natal complications. Due to the overlapping of maternal and fetal R-peaks, the low amplitude of the fECG, systematic and ambient noises, typical signal extraction methods, such as adaptive filters, independent component analysis, empirical mode decomposition, etc., are unable to produce satisfactory fECG. While some techniques can produce accurate QRS waves, they often ignore other important aspects of the ECG. Utilizing extensive preprocessing and an appropriate framework, our approach, built upon 1D CycleGAN, achieves fECG signal reconstruction from the mECG signal while preserving its morphology. The performance of our solution was evaluated by combining two available datasets from Physionet, “Abdominal and Direct Fetal ECG Database” and “Fetal electrocardiograms, direct and abdominal with reference heartbeat annotations”, where it achieved an average PCC and Spectral-Correlation score of 88.4% and 89.4%, respectively. It detects the fQRS of the signal with accuracy, precision, recall and F1 score of 92.6%, 97.6%, 94.8% and 96.4%, respectively. It can also accurately produce the estimation of fetal heart rate and R-R interval with an error of 0.25% and 0.27%, respectively. The main contribution of our work is that, unlike similar studies, it can retain the morphology of the ECG signal with high fidelity. The accuracy of our solution for fetal heart rate and R-R interval length is comparable to existing state-of-the-art techniques. This makes it a highly effective tool for early diagnosis of fetal heart diseases and regular health checkups of the fetus.

Fetal ECG denoising using dynamic time warping template subtraction.

The analysis of the fetal electrocardiogram (ECG) requires to remove the mother ECG (mECG) from the abdominal ECG signals. Template subtraction is a method that consists in modeling and removing the mECG's mean period i.e. the signal waveform defined as the Euclidean mean of all periods. This mean period is then subtracted to all periods to extract the fetal ECG (fECG). Such a method is not accurate because each mECG's period is not correctly aligned with the mean period. We propose to take account of the diffeomorphism of each period to improve the precision of the model and remove the mECG more efficiently. The soft-dynamic time warping (DTW) algorithm is used to compute the mean mECG period and the alignment between the mean period and all periods. Our approach is compared to a classic template subtraction on synthetic and real databases. Results show that considering the dynamic time warping allows a better removal of the mECG. Clinical relevance - The template subtraction is modified in this paper to consider the time warping for each mother ECG's period in order to improve the fetal ECG extraction from the abdominal ECG.

Clifford Wavelet Entropy for Fetal ECG Extraction.

Analysis of the fetal heart rate during pregnancy is essential for monitoring the proper development of the fetus. Current fetal heart monitoring techniques lack the accuracy in fetal heart rate monitoring and features acquisition, resulting in diagnostic medical issues. The challenge lies in the extraction of the fetal ECG from the mother ECG during pregnancy. This approach has the advantage of being a reliable and non-invasive technique. In the present paper, a wavelet/multiwavelet method is proposed to perfectly extract the fetal ECG parameters from the abdominal mother ECG. In a first step, due to the wavelet/mutiwavelet processing, a denoising procedure is applied to separate the noised parts from the denoised ones. The denoised signal is assumed to be a mixture of both the MECG and the FECG. One of the well-known measures of accuracy in information processing is the concept of entropy. In the present work, a wavelet/multiwavelet Shannon-type entropy is constructed and applied to evaluate the order/disorder of the extracted FECG signal. The experimental results apply to a recent class of Clifford wavelets constructed in Arfaoui, et al. J. Math. Imaging Vis. 2020, 62, 73–97, and Arfaoui, et al. Acta Appl. Math. 2020, 170, 1–35. Additionally, classical Haar–Faber–Schauder wavelets are applied for the purpose of comparison. Two main well-known databases have been applied, the DAISY database and the CinC Challenge 2013 database. The achieved accuracy over the test databases resulted in Se = 100%, PPV = 100% for FECG extraction and peak detection.

Modified Block Compressed Sensing for Extraction of Fetal Electrocardiogram from Mother Electrocardiogram Using Block Compressed Sensing Based Guided FOCUSS and FAST-Independent Component

Fetal ECG extraction from abdominal ECG is critical task for telemonitoring of fetus which require lot of understanding to the subject. Conventional source separation methods are not efficient enough to separate FECG from huge multichannel ECG. Thus use of compression technique is needed to compress and reconstruct ECG signal without any significant losses in quality of signal. Compressed sensing shows promising results for such tasks. However, current compressed sensing theory is not so far that successful due to the non-sparsity and strong noise contamination present in ECG signal. The proposed work explores the concept of block compressed sensing to reconstruct non-sparse FECG signal using GFOCUSS algorithm. The main objective of this paper is not only to successfully reconstruct the ECG signal but to efficiently separate FECG from abdominal ECG. The proposed algorithm is explained in very extensive manner for all experiments. The key feature of proposed method is, that it doesn’t affect the interdependence relation between multichannel ECG. The useof walsh sensing matrix made it possible to achieve high compression ratio. Experimental results shows that even at very high compression ratio, successful FECG reconstruction from raw ECG is possible. These results are validated using PSNR, SINR, and MSE. This shows the framework, compared to other algorithms such as current blocking CS algorithms, rackness CS algorithm and wavelet algorithms, can greatly reduce code execution time during data compression stage and achieve better reconstruction in terms of MSE, PSNR and SINR.

TWIN FETUS ECG SIGNAL EXTRACTION BASED ON TEMPORAL PREDICTABILITY

The most popular defects that infect new born babies have close connection with heart. Approximately 1% percent of new born babies suffering from defects that are caused by heart. By observing Electrocardiogram "ECG" during gestation doctors can study the fetal heart activity that collected from mother abdominal and rectify number of defects which has been diagnosed in fetal heart. Many techniques like filters, BSS and even artificial intelligence used to extract and process fetal ECG signal. In case of twins gestation the problem would be more complicated because the recorded signal is a mixture of multi signals which are mother ECG, fetuses ECG and noises where every signal comes from different source in addition fetus have the same ECG signal features as well. In this study the concentration would be on BSS techniques specifically on Stone BSS method. A comparison has been made between Stone method and two other methods "EFICA and JADE". The results proved that Stone method has better performance comparing to the other BSS techniques

An empirical sequence to extract fetal electrocardiogram using the Kernel and wavelet optimization

Fetal electrocardiogram (ECG) monitoring from the mother’s abdomen during pregnancy provides electrophysiological statistics of fetus heart. To separate fetal ECG from mother ECG is a signal separation technique that has the potential to give blind source information. In this paper, an empirical sequence of Kernel Independent Component Analysis (KICA) and Dual-Tree Complex Wavelet Transform (DTCWT) is implemented for efficient ECGs separation and post enhancement of extracted fetal ECG. The kernel approach provides a non-linear transformation and works in building contrast function based on mutual information. Further to remove unwanted disturbances, shift-invariant DTCWT is used to reconstruct filtered extracted fetal ECG. The proposed work is validated on Abdominal and Direct Fetal ECG Database (ADFECGDB) from the physionet. The proposed method also compares the accuracy and running time by implementing the FastICA method on the same dataset simultaneously.

Fetal ECG extraction exploiting joint sparse supports in a dual dictionary framework

Electrocardiogram (ECG) signals are vital tools in assessing the health of the mother and the fetus during pregnancy. Extraction of fetal ECG (FECG) signal from the mother's abdominal recordings requires challenging signal processing tasks to eliminate the effects of the mother's ECG (MECG) signal, noise and other distortion sources. The availability of ECG data from multiple electrodes provides an opportunity to leverage the collective information in a collaborative manner. We propose a new scheme for extracting the fetal ECG signals from the abdominal ECG recordings of the mother using the multiple measurement vectors approach. The scheme proposes a dual dictionary framework that employs a learned dictionary for eliminating the MECG signals through sparse domain representation and a wavelet dictionary for the noise reduced sparse estimation of the fetal ECG signals. We also propose a novel methodology for inferring a single estimate of the fetal ECG source signal from the individual sensor estimates. Simulation results with real ECG recordings demonstrate that the proposed scheme provides a comprehensive framework for eliminating the mother's ECG component in the abdominal recordings, effectively filters out noise and distortions, and leads to more accurate recovery of the fetal ECG source signal compared to other state-of-the-art algorithms.

Fetal Electrocardiogram Extraction and Analysis Using Adaptive Noise Cancellation and Wavelet Transformation Techniques.

Birth defect-related demise is mainly due to congenital heart defects. In the earlier stage of pregnancy, fetus problem can be identified by finding information about the fetus to avoid stillbirths. The gold standard used to monitor the health status of the fetus is by Cardiotachography(CTG), cannot be used for long durations and continuous monitoring. There is a need for continuous and long duration monitoring of fetal ECG signals to study the progressive health status of the fetus using portable devices. The non-invasive method of electrocardiogram recording is one of the best method used to diagnose fetal cardiac problem rather than the invasive methods.The monitoring of the fECG requires development of a miniaturized hardware and a efficient signal processing algorithms to extract the fECG embedded in the mother ECG. The paper discusses a prototype hardware developed to monitor and record the raw mother ECG signal containing the fECG and a signal processing algorithm to extract the fetal Electro Cardiogram signal. We have proposed two methods of signal processing, first is based on the Least Mean Square (LMS) Adaptive Noise Cancellation technique and the other method is based on the Wavelet Transformation technique. A prototype hardware was designed and developed to acquire the raw ECG signal containing the mother and fetal ECG and the signal processing techniques were used to eliminate the noises and extract the fetal ECG and the fetal Heart Rate Variability was studied. Both the methods were evaluated with the signal acquired from a fetal ECG simulator, from the Physionet database and that acquired from the subject. Both the methods are evaluated by finding heart rate and its variability, amplitude spectrum and mean value of extracted fetal ECG. Also the accuracy, sensitivity and positive predictive value are also determined for fetal QRS detection technique. In this paper adaptive filtering technique uses Sign-sign LMS algorithm and wavelet techniques with Daubechies wavelet, employed along with de noising techniques for the extraction of fetal Electrocardiogram.Both the methods are having good sensitivity and accuracy. In adaptive method the sensitivity is 96.83, accuracy 89.87, wavelet sensitivity is 95.97 and accuracy is 88.5. Additionally, time domain parameters from the plot of heart rate variability of mother and fetus are analyzed.

Virtual Instrumentation Based Fetal ECG Extraction

ECG, the Electro Cardio Gram is a very important tool for the clinician to decide the cardiac health of a patient1. Fetal ECG is similarly a parameter to decide the child's health throughout the gestation period of the child and during labour. Fetal ECG is contaminated with noise and the mother's ECG. The main objective of this study is to isolate and extract only the fetal ECG from the mixed signals. The present study proposes a method to effectively and efficiently isolate only the fetal ECG. The database used is available from PhysioNet which provides benchmark signals of ECGs acquired from the mother's abdomen. The algorithm used in this study is based on Fast ICA. Graphical representation software such as NI LabVIEW is preferred over Matlab due to the preciseness, cost effectiveness and the ease at which the algorithm can be understood, constructed and implemented. The present study depends on the beats/minute, amplitude and the correlation coefficient parameters to conclude the accuracy of the algorithm.

Extracting Fetal Electro Cardiogram Signal to Monitor Congenital Heart Problems Using Framelet

Gynecologists are interested in measuring the Fetal Electro Cardiogram (FECG) signal since it provides reliable information about the fetal status, the detection of abnormalities and to detect whether the fetus is alive or dead. Non-invasive technique is preferred for this to avoid the breaking up of the membrane which protects the child. The problems associated with the non-invasive interaction are mainly due to the low power of FECG signal which is contaminated by various sources of interference. The proper checking of fetal heart and the prior recognition of cardiac problems make heart specialist to recommend proper medication in that moment or to take the essential safety measures during delivery or after labor. The enduring look of mother's ECG signal in which the amplitude is 5-20 times more than that of FECG is considered to be a maddening one. A new method for filtering FECG from Abdominal ECG (AECG) is proposed. In the midst of the several noises that taint FECG, the noise which is needed to be eliminated is the mother's noise generated in the abdomen. The current work aims to get rid of the mother's ECG signal (MECG) and to extract a perfect FECG. The performance of the proposed method is evaluated by Mean Square Error (MSE) and Peak Signal to noise ratio (PSNR). The result shows that the Framelet Transform (FT) produces minimum MSE and high Peak Signal to Noise Ratio (PSNR) than Discrete Wavelet Transform (DWT).

Extracting Fetal Electro Cardiogram Signal to Monitor Congenital Heart Problems Using Framelet

Gynecologists are interested in measuring the Fetal Electro Cardiogram (FECG) signal since it provides reliable information about the fetal status, the detection of abnormalities and to detect whether the fetus is alive or dead. Non-invasive technique is preferred for this to avoid the breaking up of the membrane which protects the child. The problems associated with the non-invasive interaction are mainly due to the low power of FECG signal which is contaminated by various sources of interference. The proper checking of fetal heart and the prior recognition of cardiac problems make heart specialist to recommend proper medication in that moment or to take the essential safety measures during delivery or after labor. The enduring look of mother's ECG signal in which the amplitude is 5-20 times more than that of FECG is considered to be a maddening one. A new method for filtering FECG from Abdominal ECG (AECG) is proposed. In the midst of the several noises that taint FECG, the noise which is needed to be eliminated is the mother's noise generated in the abdomen. The current work aims to get rid of the mother's ECG signal (MECG) and to extract a perfect FECG. The performance of the proposed method is evaluated by Mean Square Error (MSE) and Peak Signal to noise ratio (PSNR). The result shows that the Framelet Transform (FT) produces minimum MSE and high Peak Signal to Noise Ratio (PSNR) than Discrete Wavelet Transform (DWT).

A Novel Extraction Method of Fetal Electrocardiogram From the Composite Abdominal Signal

In contrast to the ultrasonic measurement of fetal heart motion, the fetal electrocardiogram (ECG) provides clinically significant information concerning the electrophysiological state of a fetus. In this paper, a novel method for extracting the fetal ECG from abdominal composite signals is proposed. This method consists of the cancellation of the mother's ECG and blind source separation with the reference signal (BSSR). The cancellation of the mother's ECG component was performed by subtracting the linear combination of mutually orthogonal projections of the heart vector. The BSSR is a fixed-point algorithm, the Lagrange function of which includes the higher order cross-correlation between the extracted signal and the reference signal as the cost term rather than a constraint. This realizes the convexity of the Lagrange function in a simple form, which guarantees the convergence of the algorithm. By practical application, the proposed method has been shown to be able to extract the P and T waves in addition to the R wave. The reliability and accuracy of the proposed method was confirmed by comparing the extracted signals with the directly recorded ECG at the second stage of labor. The gestational age-dependency of the physiological parameters of the extracted fetal ECG also coincided well with that of the magnetocardiogram, which proves the clinical applicability of the proposed method.

Non‐invasive intrapartum fetal ECG: preliminary report

To obtain fetal heart rate, detailed fetal electrocardiography (fECG) signals and uterine contractions during labour using a single device. Prospective observational study. Delivery suite at a tertiary referral hospital, London, UK. Fifteen patients at median gestation of 39 weeks (range 24-41) were recruited at median cervical dilatation of 4.0 cm (range 0-10) of whom 8/15 (53%) had intact amniotic membranes. Using 12 abdominally sited electrodes, we recorded the composite abdominal signal in pregnancies intrapartum. The recorded data were analysed off-line using a blind signal separation technique. Success of signal separation and fECG time intervals. Successful fECG signal acquisition was achieved in 12/15 (80%) patients and an averaged fECG waveform acquired. In these patients, P and QRS waves were seen in all cases, and T waves in 11/12 (92%). True beat-to-beat heart rate (HR) was displayed and measures of its variability obtained. The mother's ECG and uterine electrical activity, shown to match tocographically recorded uterine contractions, were also separated and displayed. Failure to acquire fECG in three cases was attributed to excessive abdominal muscular activity and electrical interference. This study demonstrates a non-invasive technique that displays detailed intrapartum fECG waveforms, HR variability, maternal ECG and uterine contractions simultaneously, all in a single device and which avoids the potential risks of invasive monitoring with a fetal scalp electrode.

Rejection of the maternal electrocardiogram in the electrohysterogram signal.

The electrohysterogram (EHG) signal is mainly corrupted by the mother's electrocardiogram (ECG), which remains present despite analog filtering during acquisition. Wavelets are a powerful denoising tool and have already proved their efficiency on the EHG. In this paper, we propose a new method that employs the redundant wavelet packet transform. We first study wavelet packet coefficient histograms and propose an algorithm to automatically detect the histogram mode number. Using a new criterion, we compute a best basis adapted to the denoising. After EHG wavelet packet coefficient thresholding in the selected basis, the inverse transform is applied. The ECG seems to be very efficiently removed.