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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]

Physiological signals, such as the Photoplethysmogram (PPG) collected through wearable devices, consistently encounter significant motion artifacts. Current signal processing techniques, and even state-of-the-art machine learning algorithms, frequently struggle to effectively restore the inherent bodily signals amidst the array of randomly generated distortions. This often leads to the modification or even the degradation of the underlying physiological information. To enhance heart rate estimation from wrist PPG (wPPG) signals, this study introduces the Translation Through Restoration GAN (TTR-GAN). TTR-GAN comprises cascaded dual-stage 1D Cycle Generative Adversarial Networks (1D-CycleGANs) constructed using Super-ONNs. In the first phase, corrupted wPPG waveforms are blindly restored using a 1D-CycleGAN-based restoration framework. Subsequently, in the second phase, the restored wPPG waveforms are translated into clean finger PPG (fPPG) signals through a 1D-CycleGAN-based signal-to-signal translation or synthesis framework. Both the restorer and translator GANs undergo independent evaluation using robust temporal, spectral, and clinical metrics. The application of the multipass restoration scheme to the wPPG signals resulted in significantly lower entropy compared to the raw wPPGs, indicating reduced irregularity. Using the proposed PRTX metric to evaluate the translational ability of the multichannel translator CycleGAN, we achieved a substantial improvement of 35.88% in wrist-to-finger PPG translation. The correlation between the pulse rate and pulse rate variations estimated from the generated fPPG signals and the heart rate and heart rate variability readings from the ground truth ECG improved by approximately 10.4% and 14.7% , respectively, when compared to the raw wPPG signals. The proposed TTR-GAN can be implemented in wearable devices to obtain reliable real-time cardiovascular data during daily activities. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) signals suffer substantially from motion artifacts even in ambulatory settings. Signal processing techniques for removing motion artifacts from EEG signals have limitations, and the potential of classical or deep machine-learning algorithms for this task remains largely unexplored. We propose Attention-Guided Operational CycleGAN (AGO-CycleGAN), a novel CycleGAN-based framework to remove motion artifacts and enhance the quality of corrupted EEG signals. It incorporates self-generative operational neurons and an attention-guided Feature Pyramid Network with modified bottlenecks as generators and PatchGAN-based discriminators. AGO-CycleGAN was trained and tested on a single-channel EEG dataset from 23 subjects, using a subject-independent Jackknife cross-validation approach. It outperformed other methods and was evaluated through qualitative and quantitative analysis, employing robust metrics in both temporal and frequency domains. The results indicate its effectiveness in restoring EEG signals affected by severe motion artifacts. AGO-CycleGAN achieves state-of-the-art EEG restoration performance in the temporal domain, gaining improvements in signal-to-noise ratio (ΔSNR) and temporal correlation (η temp) by 26.497 dB and 87.2% , respectively. It also showed excellent performance in preserving the spectral EEG components (delta, theta, alpha, beta, and gamma), evaluated through band power ratio before and after restoration. Spectral correlation (η spec) improved by 93.5% after cleaning the motion artifacts. Qualitative evaluations showed excellently reconstructed clean EEG waveforms upon restoration. Spectral restoration visualized through Power Spectral Density (PSD) plots and per-band topographic maps showed a uniform removal of high-power motion artifact components throughout the spectrum. AGO-CycleGAN significantly outperformed existing techniques in EEG artifact removal and can be extended to multi-channel EEG systems. • This study aims to restore EEG signals from motion artifacts using 1D-CycleGAN. • We use Self-Organized Operational Neural Networks to build the proposed Attention-Guided Operational ‎CycleGAN.‎ • The proposed approach outperforms all competing methods in the relevant literature by a significant margin.‎ • The proposed framework can be modified and reused to restore other physiological waveforms.‎ [ABSTRACT FROM AUTHOR]