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Fetal heart monitoring and early disease detection using non-invasive fetal electrocardiograms (fECG) can help substantially to reduce infant death through improved diagnosis of Coronary Heart Disease (CHD) in the fetus. Despite the potential benefits, non-invasive fECG extraction from maternal abdominal ECG (mECG) is a challenging problem due to multiple factors such as the overlap of maternal and fetal R-peaks, low amplitude of fECG, and various systematic and environmental noises. Conventional fECG extraction techniques, such as adaptive filters, independent component analysis (ICA), empirical mode decomposition (EMD), etc., face various performance issues due to the fECG extraction challenges. In this paper, we proposed a novel deep learning architecture, LinkNet++ (motivated by the original LinkNet) to extract fECG from abdominal mECG automatically and efficiently using two different publicly available datasets. LinkNet++ is equipped with a feature-addition method to combine deep and shallow levels with residual blocks to overcome the limitations of U-Net and UNet++ models. It also has deep supervised and densely connected convolution blocks to overcome the limitations of the original LinkNet. The proposed LinkNet++ model was evaluated using fECG signal reconstruction and fetal QRS (fQRS) detection. As a signal-to-signal synthesis model, LinkNet++ performed very well in two real-life datasets and achieved 85.58% and 87.60% Pearson correlation coefficients (PCC) between the ground truth and predicted fECG on two datasets, respectively. In terms of fQRS detection, it also outperformed most of the previous works and showed excellent performance with more than 99% of F1 scores on both datasets. Our results indicate that the proposed model can potentially extract fECG non-invasively with excellent signal quality, thereby providing an excellent diagnostic tool for various fetal heart diseases. • A novel LinkNet++ with deep supervision and dense blocks is proposed for automatic extraction of fECG from mECG. • A rigorous leave one subject out cross-validation study is conducted using two datasets to build a generalizable model. • The innovative LinkNet++ model outperforms all previous models and standard signal processing methodologies. [ABSTRACT FROM AUTHOR]

[Display omitted] • Unlike traditional BP prediction schemes, we aim at estimating ABP waveforms from PPG and ECG signals from which all BP information is extracted. • We propose a hybrid pipeline that separates the ABP estimation process into two parts viz. BP prediction and ABP estimation. • The predicted normalized ABP waveforms are linearly transformed into ABP signals using their respective BP metrics. • We propose the NABNet architecture which utilizes Convolutional LSTM and Attention Guidance concepts for improving construction error accumulating due to ABP phase lag during segmentation. • This study performs multiple sets of experiments to determine the best segmentation model for ABP estimation. • The model is trained on a large, variable dataset with highly varying PPG and ECG waveforms to enhance the robustness and generalizability of the model. and Motivations: Continuous Blood Pressure (BP) monitoring is crucial for real-time health tracking, especially for people with hypertension and cardiovascular diseases (CVDs). The current cuff-based BP monitoring methods are non-invasive but discontinuous while continuous BP monitoring methods are mostly invasive and can only be applied in a clinical setup to patients being monitored by advanced equipment and medical experts. Several studies have reported different techniques for predicting BP values from non-invasive Photoplethysmogram (PPG) and Electrocardiogram (ECG) signals. Apart from BP readings, estimating ABP waveforms from non-invasive signals can provide vital body parameters such as Mean Arterial Pressure (MAP) which can be used to determine poor organ perfusion, nutrient supply to organs, and cardiovascular diseases (CVDs), etc. It is challenging to estimate ABP waveforms while maintaining a high BP prediction performance and ABP waveform pattern. In this work, we propose a novel approach for ABP waveform estimation by separating the task into BP prediction and a normalized ABP waveform estimation through segmentation from PPG, PPG derivatives, and ECG signals, and combining afterward. We propose the Nested Attention-guided BiConvLSTM Network or NABNet which uses LSTM blocks during segmentation for better handling of the existing phase shifts between PPG, ECG, and ABP signals. Several experiments were performed to improve the ABP reconstruction performance, which was combined with an existing BP prediction pipeline for the non-invasive estimation of ABP waveforms. The proposed framework can robustly estimate ABP waveforms from PPG and ECG signals by reaching a high MAP performance and low construction error while maintaining the overall Grade A performance of the BP prediction pipeline. Linearly translating the range-normalized, synthesized ABP segments by corresponding SBP and DBP predictions from the BP prediction pipeline managed to robustly estimate ABP waveforms from PPG and ECG signals. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]