Extraction Of Fetal Electrocardiogram Signal Research Articles

Integrating Contrastive Learning and Cycle Generative Adversarial Networks for Non-invasive Fetal ECG Extraction.

Fetal electrocardiogram (FECG) contains crucial information about the fetus during pregnancy, making the extraction of FECG signal essential for monitoring fetal health. However, extracting FECG signal from abdominal electrocardiogram (AECG) poses several challenges: (1) FECG signal is often contaminated by noise, and (2) FECG signal is frequently overshadowed by high-amplitude maternal electrocardiogram (MECG). To address these issues and enhance the accuracy of signal extraction, this paper proposes an improved Cycle Generative Adversarial Networks (CycleGAN) with integrated contrastive learning for FECG signal extraction. The model introduces a dual-attention mechanism in the generator of the generative adversarial network, incorporating amulti-head self-attention(MSA) module and achannel-wise self-attention (CSA) module to enhance the quality of generated signals. Additionally, a contrastive triplet loss is integrated into the CycleGAN loss function, optimizing training to increase the similarity between the extracted FECG signal and the scalp fetal electrocardiogram. The proposed method is evaluated using the ADFECG dataset and the PCDB dataset both from the Physionet. In terms of signal extraction quality, Mean Squared Error is reduced to 0.036, Mean Absolute Error (MAE) to 0.009, and Pearson Correlation Coefficient reaches 0.924. When validating the model performance, Structural Similarity Index achieves 95.54%, Peak Signal-to-Noise Ratio (PSNR) reaches 38.87dB, and R-squared (R2) attains 95.12%. Furthermore, the positive predictive value (PPV), sensitivity (SEN) and F1-score for QRS wave cluster detection on the ADFECG dataset also reached 99.56%, 99.43% and 99.50%, respectively. On the PCDB dataset, the positive predictive value (PPV), sensitivity (SEN) and F1-score for QRS wave cluster detection also reached 98.24%, 98.60% and 98.42%, respectively. All of them are higher than other methods. Therefore, the proposed model has important applications in effective monitoring of fetal health during pregnancy.

Fetal electrocardiogram signal extraction and analysis method combining fast independent component analysis algorithm and convolutional neural network

Fetal electrocardiogram (ECG) signals provide important clinical information for early diagnosis and intervention of fetal abnormalities. In this paper, we propose a new method for fetal ECG signal extraction and analysis. Firstly, an improved fast independent component analysis method and singular value decomposition algorithm are combined to extract high-quality fetal ECG signals and solve the waveform missing problem. Secondly, a novel convolutional neural network model is applied to identify the QRS complex waves of fetal ECG signals and effectively solve the waveform overlap problem. Finally, high quality extraction of fetal ECG signals and intelligent recognition of fetal QRS complex waves are achieved. The method proposed in this paper was validated with the data from the PhysioNet computing in cardiology challenge 2013 database of the Complex Physiological Signals Research Resource Network. The results show that the average sensitivity and positive prediction values of the extraction algorithm are 98.21% and 99.52%, respectively, and the average sensitivity and positive prediction values of the QRS complex waves recognition algorithm are 94.14% and 95.80%, respectively, which are better than those of other research results. In conclusion, the algorithm and model proposed in this paper have some practical significance and may provide a theoretical basis for clinical medical decision making in the future.

Implementation of an Optimised Hybrid Algorithm Based on Independent Component Analysis and Empirical Mode Decomposition for Non-Invasive Fetal Electrocardiogram Signal Extraction and Processing

Implementation of an Optimised Hybrid Algorithm Based on Independent Component Analysis and Empirical Mode Decomposition for Non-Invasive Fetal Electrocardiogram Signal Extraction and Processing

Analysis and Extraction of Fetal Electrocardiogram Signal with Adaptive Filtering Using Differential Evolution Algorithm

Recent studies have demonstrated the advantages
 of fetal electrocardiogram (ECG) extraction and analysis. In this study, a new
 approach is proposed for fetal ECG signal extraction using differential evolution
 algorithm and adaptive filtering. The results obtained by the proposed approach
 are compared with the result of the adaptive method based on the least mean
 square method in the literature. According to the obtained results, the
 proposed approach gives better results in terms of fetal ECG signal extraction.

A Fetal Electrocardiogram Signal Extraction Algorithm Based on Fast One-Unit Independent Component Analysis with Reference.

Fetal electrocardiogram (FECG) extraction is very important procedure for fetal health assessment. In this article, we propose a fast one-unit independent component analysis with reference (ICA-R) that is suitable to extract the FECG. Most previous ICA-R algorithms only focused on how to optimize the cost function of the ICA-R and payed little attention to the improvement of cost function. They did not fully take advantage of the prior information about the desired signal to improve the ICA-R. In this paper, we first use the kurtosis information of the desired FECG signal to simplify the non-Gaussian measurement function and then construct a new cost function by directly using a nonquadratic function of the extracted signal to measure its non-Gaussianity. The new cost function does not involve the computation of the difference between the function of the Gaussian random vector and that of the extracted signal, which is time consuming. Centering and whitening are also used to preprocess the observed signal to further reduce the computation complexity. While the proposed method has the same error performance as other improved one-unit ICA-R methods, it actually has lower computation complexity than those other methods. Simulations are performed separately on artificial and real-world electrocardiogram signals.

Extraction of fetal electrocardiogram signal utilizing blind source separation based on time-frequency distributions and wavelet packet denoising

A new method utilizing Blind Source Separation based on Time-Frequency distributions(TFBSS) and wavelet packet denosing was proposed to extract the Fetal ElectroCardioGram(FECG) signal.The original eight ElectroCardioGram(ECG) signals obtained from the thoracic and abdominal area of the pregnant woman were firstly processed to eight components by utilizing rearrangement TFBSS.Then the Maternal ECG(MECG) signal and noise components in eight components were set by zero and the rest components were reconstructed by using the mixing matrix.The FECG with noise could be extracted by separating the reconstructed result by using rearrangement TFBSS.Finally,the baseline shift and noise in FECG were suppressed by wavelet packet denoising technique.The FECG could be extracted even under the condition of the fetal QRS wave being partly and entirely overlapped with the maternal QRS wave in the abdominal composite signal.The experimental results show that the clear FECG can be extracted by utilizing the proposed method.

Fetal Electrocardiogram Signal Extraction by ANFIS Trained with PSO Method

Studies indicate that the primary source of distress in pregnent mothers is their concerns about fetus’s condition and health. One way to know about condition of fetus is non-invasive fetal electrocardiogram signal extraction through which the components of fetal electrocardiogram signal are extracted from a signal recorded at abdominal area of mother which is a combination of fetal and maternal electrocardiogram signal and noise source components. The purpose of this study is to propose an algorithm to boost this extraction. To this end, we decomposed electrocardiogram signal to its Intrinsic Mode Functions (IMFs) thruogh Empirical Mode Decomposition algorithm; then, we removed the last and collected the other IMFs to reconstruct electrocardiogram signal without Baseline. Afterwards, we used Particle Swarm Optimization to train and adjust the parameters of Adaptive Neuro-Fuzzy Inference System to model the path that maternal electrocardiogram signal travel to reach abdominal area. Accordingly, we were able to distinguish and remove maternal electrocardiogram signal components from the recorded signal and hence we obtained a good approximation of fetal electrocardiogram signal. We implemented our algorithm and other algorithms on simulated and real signals and found out that, in most cases, the proposed algorithm improved the extraction of fetal electrocardiogram signal. DOI: http://dx.doi.org/10.11591/ijece.v2i2.231