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The Magnetohydrodynamic (MHD) effect on the bloodstream, induced by the static magnetic field of Magnetic Resonance Imaging (MRI) devices, distorts Electrocardiogram (ECG) components, and poses challenges to cardiac gating and monitoring during MRI examinations. Restoring ECG components from MHD-induced artifacts has always been a challenge, primarily due to the influence of numerous device-induced and physiological variables and the absence of paired ground truth clean ECG for validation. In this research, at first, we employ unpaired restoration of MHD-corrupted 12-lead ECG signals using one dimensional (1D) Cycle Generative Adversarial Networks (CycleGANs) for channel and patient orientation-specific restoration. Subsequently, a 1D-segmentation-based comprehensive restoration scheme is proposed based on the phase 1 outputs to simultaneously restore 12-channel ECG signals, irrespective of internal and external factors affecting ECG. By employing the proposed Magnetohydrodynamic Generative Adversarial Network (MHDGAN) and MHD-to-ECG Network (MHD2ECGNet) for unpaired and comprehensive restoration, we achieved an overall spectral correlation improvement of 92.56% and 77.64% , respectively. In R-peak detection, MHDGAN demonstrated an overall precision, recall, and F1-score of 98.14% , 94.12% , and 96.01% , respectively. The extracted heart rate (HR) and heart rate variability (HRV) from the restored waveforms closely match the ground truth, as confirmed through quantitative and qualitative assessments. The proposed framework has proven effective in restoring MHD-corrupted ECG waveforms, even in the absence of paired ground truth labels and amidst compounding distortions from multiple sources. • Magnetohydrodynamic (MHD) corrupted ECG signals can be restored using Deep Learning. • 1D-CycleGANs can be used for unpaired and unaligned MHD data for restoration. • Multichannel 1D-Segmentation models can be used to generalize the restoration process. • Temporal, Spectral and Clinical evaluations confirmed the efficacy of the proposed framework. • The proposed approach can enhance cardiac monitoring during MRI examinations. [ABSTRACT FROM AUTHOR]

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]

The world is slowly recovering from the Coronavirus disease 2019 (COVID-19) pandemic; however, humanity has experienced one of its According to work by Mishra et al. (2020), the study's first phase included a cohort of 5,262 subjects, with 3,325 Fitbit users constituting the majority. However, among this large cohort of 5,262 subjects, most significant trials in modern times only to learn about its lack of preparedness in the face of a highly contagious pathogen. To better prepare the world for any new mutation of the same pathogen or the newer ones, technological development in the healthcare system is a must. Hence, in this work, PCovNet+, a deep learning framework, was proposed for smartwatches and fitness trackers to monitor the user's Resting Heart Rate (RHR) for the infection-induced anomaly. A convolutional neural network (CNN)-based variational autoencoder (VAE) architecture was used as the primary model along with a long short-term memory (LSTM) network to create latent space embeddings for the VAE. Moreover, the framework employed pre-training using normal data from healthy subjects to circumvent the data shortage problem in the personalized models. This framework was validated on a dataset of 68 COVID-19-infected subjects, resulting in anomalous RHR detection with precision, recall, F-beta, and F-1 score of 0.993, 0.534, 0.9849, and 0.6932, respectively, which is a significant improvement compared to the literature. Furthermore, the PCovNet+ framework successfully detected COVID-19 infection for 74% of the subjects (47% presymptomatic and 27% post-symptomatic detection). The results prove the usability of such a system as a secondary diagnostic tool enabling continuous health monitoring and contact tracing. • A CNN-VAE-based anomaly detection model and an LSTM network to generate temporal-aware embeddings of the latent vector of the primary model is used. • Healthy patient data is used to pretrain the base model and fine-tuned using each subject's baseline data to achieve a personalized version. • The proposed model is validated on 68 COVID-19-infected individuals' data. [ABSTRACT FROM AUTHOR]