An Adaptive FECG Extraction and Analysis Method Using ICA, ICEEMDAN and Wavelet Shrinkage

Fetal Electrocardiogram (ECG) signal extraction from non-invasive abdominal ECG signal is one of the important clinical practices followed to observe the fetal health state. Information about heart growth and health conditions of a fetus can be observed from fetal ECG signals. However, acquiring fetal ECG from abdominal ECG signals is still considered as a challenging task in biomedical analysis. This is mainly due to corrupted high amplitude maternal ECG signals, low signal to noise ratio of fetal ECG signal, difficulties in reduction of QRS (Q wave, R wave, S wave) complexities, fetal ECG signal superimposed characteristics, other motion, and electromyography artifacts. To reduce these conventional challenges, in fetal ECG analysis of a novel Conditional Generative adversarial network (CGAN) is introduced in this research work to extract the fetal ECG signal. The proposed classification model was classified efficiently in fetal ECG signals from non-invasive abdominal ECG signals. The experimental analysis demonstrates that the proposed network model provides better results in terms of sensitivity, specificity, and accuracy compared to the conventional fetal ECG extraction models like singular value decomposition, periodic component analysis, and Adaptive neuro-fuzzy inference system.

Electroencephalograph (EEG) is brain activity measurement technique that it is widely used by placing electrode on the scalp. The EEG recording signals are vulnerably contaminated by noise. Thus, it can lead to misperception in analyzing EEG signals. One of the noise that has major influence on the recorded EEG signals is ocular artifacts (OAs). In this paper, a new method to remove OAs automatically is proposed by combining between Complete Ensemble Empirical Mode Decomposition (CEEMD) and Independent Component Analysis (ICA). CEEMD-ICA-OD removes OAs based on the concentration of noise that is calculated by calculating entropy for every CEEMD decomposition. Every selected decomposition of CEEMD will be used as input to the ICA method. Then, ICA will decompose selected CEEMD decomposition into independent components (ICs). Then, Chauvenet Criterion (CC) as Outlier Detection (OD) method is used to detect OAs pattern. Every detected OAs pattern will be eliminated by zeroing process on OAs pattern so that the EEG signal information remains secure. The proposed method were compared with Empirical Mode Decomposition (EMD), Ensemble EMD (EEMD) and Complete EEMD (CEEMD) with zeroing ICA combination for every comparison method. The performance measure of the proposed method and the benchmark method is calculated by using Root Mean Square Error (RMSE) and Signal to Noise Ratio (SNR) to show the performance quality in eliminating OAs. The experiment results show that the proposed method is successfully removing OAs better than the other methods with RMSE value and SNR value are 0.2491 and -0.3449, respectively.

Elicitation of fetal ECG from abdominal recordings using Blind Source Separation techniques and Robust Set Membership Affine Projection algorithm for signal quality enhancement

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.

Fetal electrocardiogram (FECG) signals directly reflect the electrical activity of the fetal heart, enabling the assessment of fetal cardiac health. To effectively separate and extract FECG signals from maternal abdominal electrocardiogram (ECG) signals, this study proposed a W-shaped parallel network, termed Attention R2W-Net, which consisted of two Attention R2U-Nets. In the encoder and decoder, recurrent residual modules were used to replace feedforward convolutional layers, significantly enhancing feature representation and improving noise suppression. Additionally, attention gates were used to replace skip connections, enabling precise correction of low-resolution features using deep features and further improving model performance. The decoders at both ends of the network were utilized to reconstruct FECG and MECG signals, respectively. The algorithm was validated using simulated and real datasets, achieving F1 scores of 99.17%, 98.03%, and 97.08% on the ADFECGDB, PCDB, and NIFECGDB datasets, respectively, demonstrating superior performance in both subjective visual effects and objective evaluation metrics. Attention R2W-Net’s ability to extract robustly and accurately FECG signals, even in complex noisy environments, make it a reliable tool for FECG extraction. The proposed method’s efficiency and accuracy highlight its potential for widespread clinical application, contributing to improved early diagnosis of fetal cardiac abnormalities.

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 estimation using fractional Fourier transform and wavelet analysis

The extraction of Fetal Electrocardiogram (FECG) during labor or prenatal phases of pregnancy holds significant importance for early prediction of heart abnormalities. The accuracy of the extracted FECG is crucial for effective diagnosis, but the presence of external noises, particularly from the Maternal ECG (MECG), poses a major challenge in obtaining precise information. To address this issue in biomedical data processing, the study employs Finite Impulse Response (FIR) filters using an array multiplier. One notable challenge encountered in this process is the interference caused by external noises, leading to higher delay and power dissipation. In response, a modified High-Performance Multiplier (HPM) based modified booth multiplier is thoroughly reviewed and validated. This modification aims to enhance overall performance and enable high-speed operations in filtering the FECG signals. The effectiveness of these modified multipliers is assessed using ECG signal information collected from the MIT-BIH Arrhythmia Database, comprising 120 samples. In addition to noise filtering, the study explores the validation of Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) transceivers. These transceivers play a crucial role in ensuring the effective transmission of the extracted FECG signals. The research reveals a significant reduction, up to 80.4%, in both area and power dissipation during simulations conducted in Xilinx ISE 9.1 and Cadence Virtuoso. This achievement highlights the potential for improved efficiency and reliability in the processing and transmission of FECG signals, paving the way for advancements in early detection of fetal heart abnormalities.

Sudden cardiac death prediction based on the complete ensemble empirical mode decomposition method and a machine learning strategy by using ECG signals

A three-dimensional dynamic model of the electrical activity of the heart is presented. The model is based on the single dipole model of the heart and is later related to the body surface potentials through a linear model which accounts for the temporal movements and rotations of the cardiac dipole, together with a realistic ECG noise model. The proposed model is also generalized to maternal and fetal ECG mixtures recorded from the abdomen of pregnant women in single and multiple pregnancies. The applicability of the model for the evaluation of signal processing algorithms is illustrated using independent component analysis. Considering the difficulties and limitations of recording long-term ECG data, especially from pregnant women, the model described in this paper may serve as an effective means of simulation and analysis of a wide range of ECGs, including adults and fetuses.

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.

Objective: The utility of fetal heart rate (FHR) monitoring can only be achieved with an acquisition sampling rate that preserves the underlying physiological information on the millisecond time scale (1000 Hz rather than 4 Hz). For such acquisition, fetal ECG (fECG) is required, rather than the ultrasound to derive FHR. We tested one recently developed algorithm, SAVER, and two widely applied algorithms to extract fECG from a single-channel maternal ECG signal recorded over the xyphoid process rather than the routine abdominal signal. Approach: At 126dG, ECG was attached to near-term ewe and fetal shoulders, manubrium and xyphoid processes (n = 12). fECG served as the ground-truth to which the fetal ECG signal extracted from the simultaneously-acquired maternal ECG was compared. All fetuses were in good health during surgery (pH 7.29 ± 0.03, pO2 33.2 ± 8.4, pCO2 56.0 ± 7.8, O2Sat 78.3 ± 7.6, lactate 2.8 ± 0.6, BE −0.3 ± 2.4). Main result: In all animals, single lead fECG extraction algorithm could not extract fECG from the maternal ECG signal over the xyphoid process with the F1 less than 50%. Significance: The applied fECG extraction algorithms might be unsuitable for the maternal ECG signal over the xyphoid process, or the latter does not contain strong enough fECG signal, although the lead is near the mother’s abdomen. Fetal sheep model is widely used to mimic various fetal conditions, yet ECG recordings in a public data set form are not available to test the predictive ability of fECG and FHR. We are making this data set openly available to other researchers to foster non-invasive fECG acquisition in this animal model.

Foetal Electrocardiogram (FECG) monitoring is a technique for obtaining important information about the condition of the foetus during pregnancy and labour. In this study, we propose a new method of using combination of hybrid soft-computing technique called Adaptive Neuro Fuzzy Inference System (ANFIS) along with wavelets to estimate the Maternal Electrocardiogram (MECG) and to extract the FECG signal from the mother's Abdominal Electrocardiogram (AECG). Two methods have been developed wherein: using wavelets as a pre-processing tool combined with ANFIS; using wavelet as a post-processing tool combined with ANFIS. The results obtained by both the methods were analysed in terms of Signal to Noise Ratio (SNR), correlation coefficients and with performance indices. These proposed methods successfully removed the artefacts and extracted the desired FECG signal.

Accurate fetal R-peak detection from low-SNR fetal electrocardiogram (FECG) signals remains a critical challenge as current NI-FECG methods struggle to extract high SNR FECG signals and conventional algorithms fail when signal quality deteriorates. We proposed a U-Net-based method that enables robust R-peak detection directly from low-SNR FECG signals (0–12 dB), bypassing the need for high-SNR inputs that are clinically difficult to acquire. The method was evaluated on both real (A&D FECG) and synthetic (FECGSYN) databases, comparing against ten state-of-the-art detectors. The proposed method significantly reduces false predictions compared to commonly used detection algorithms, achieving a PPV of 99.81%, an SEN of 100.00%, and an F1-score of 99.91% on the A&D FECG database and a PPV of 99.96%, an SEN of 99.93%, and an F1-score of 99.94% on the FECGSYN database. Further investigation of robustness in low-SNR conditions (0 dB, 5 dB, and 10 dB) achieved 87.38% F1-score at 0 dB SNR on real signals, surpassing the best-performing algorithm implemented in Neurokit by 13.58%. In addition, the algorithm showed ≤2.65% performance variation across tolerance windows (50 reduced to 20 ms), further underscoring its detection accuracy. Overall, this work reduces the reliance on high-SNR FECG signals by reliably extracting R-peaks from suboptimal signals, providing implications for the reliability of fetal heart rate variability analysis in real-world noisy environments.

This paper addresses the enhancements achievable by the application of wavelet transform to fetal ECG (FECG) signals extracted by polynomial networks. The polynomial networks technique has been exploited to isolate fetal electrocardiogram (FECG) from the undesired mapped maternal electrocardiogram (mapped MECG). In this paper wavelet transform is used to enhance the extracted FECG. Processing of both real and synthetic ECG data are examined with proposed pre and post wavelet denoising algorithms. Results show improved extraction performance and successful removal of baseline wandering. Numerical results of signal-to-noise ratio for synthetic data attest considerable enhancement. The characteristics of the FECG signal were shown to be preserved and a relatively clean FECG signal is obtained.