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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]

• A deep learning-based classification system proposed in this study, where the maximum flow velocity waveform from TCD signals is used for classifying intensive care unit (ICU) patient signals. • A novel CycleGAN based maximum flow velocity waveform restoration model proposed to restore low quality maximum flow velocity signal to high quality maximum flow velocity signal. • A novel two-stream method based Self-organized Operational Neural Network (Self-ONN) architecture, DopplerNet2+ is proposed in this study for classifying healthy and ICU patient signals. Since the brain is unlike any other organ in that it cannot store energy and has a high metabolic demand, constant blood flow is essential for healthy brain function. The maximum flow velocity waveform that is produced by transcranial doppler echo ultrasonography has different qualities for a healthy subject and a critically ill patient with conditions such as intraparenchymal or subarachnoid hemorrhage, hydrocephalus, or traumatic brain injury. Depending on the degree of the injury, the symptoms of traumatic brain damage can present themselves right away or not until days or weeks later. To aid in the early and accurate detection of patients with severe brain conditions, a classification system is proposed that can distinguish between healthy control and patient utilizing the maximum flow velocity waveform derived from Transcranial doppler ultrasound. In this research, we manually labelled the data to remove mediocre and corrupted signals and pre-processed low-quality signals into high-quality ones using a Cycle Generative Adversarial Network (CycleGAN). This study proposes a two-stream deep learning model, DopplerNet2+, based on a Self-organized Operational Neural Network (Self-ONN), which achieves an overall accuracy, precision, recall, sensitivity, f1 score, and specificity of 99.45%, 99.45%, 99.45%, and 99.37% for the classification issue. DopplerNet2+ has a better area under the curve (AUC) of 1.00 and a better Kolmogorov-Smirnov (KS) statistic of 0.996 at the 0.812 thresholds than 11 other Self-ONN models trained with different inputs. The results show that the proposed models can successfully carry out the targeted classification task. [ABSTRACT FROM AUTHOR]

• Automated multi-stage method employed to classify urban wastes. • A novel model named GCDN-Net is proposed as the garbage classifier. • Four different types of waste have been classified using the garbage classifier. • Saliency map-based visualization with ScoreCAM is shown as a qualitative evaluation. The escalating waste volume due to urbanization and population growth has underscored the need for advanced waste sorting and recycling methods to ensure sustainable waste management. Deep learning models, adept at image recognition tasks, offer potential solutions for waste sorting applications. These models, trained on extensive waste image datasets, possess the ability to discern unique features of diverse waste types. Automating waste sorting hinges on robust deep learning models capable of accurately categorizing a wide range of waste types. In this study, a multi-stage machine learning approach is proposed to classify different waste categories using the "Garbage In, Garbage Out" (GIGO) dataset of 25,000 images. The novel Garbage Classifier Deep Neural Network (GCDN-Net) is introduced as a comprehensive solution, adept in both single-label and multi-label classification tasks. Single-label classification distinguishes between garbage and non-garbage images, while multi-label classification identifies distinct garbage categories within single or multiple images. The performance of GCDN-Net is rigorously evaluated and compared against state-of-the-art waste classification methods. Results demonstrate GCDN-Net's excellence, achieving 95.77% accuracy, 95.78% precision, 95.77% recall, 95.77% F1-score, and 95.54% specificity when classifying waste images, outperforming existing models in single-label classification. In multi-label classification, GCDN-Net attains an overall Mean Average Precision (mAP) of 0.69 and an F1-score of 75.01%. The reliability of network performance is affirmed through saliency map-based visualization generated by Score-CAM (class activation mapping). In conclusion, deep learning-based models exhibit efficacy in categorizing diverse waste types, paving the way for automated waste sorting and recycling systems that can mitigate costs and processing times. [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]

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]