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From 2022 March 18 to 21, NOAA Active Region (AR) 12967 was tracked simultaneously by Solar Orbiter at 0.35 au and Hinode/EIS at Earth. During this period, strong blueshifted plasma upflows were observed along a thin, dark corridor of open magnetic field originating at the AR's leading polarity and continuing toward the southern extension of the northern polar coronal hole. A potential field source surface model shows large lateral expansion of the open magnetic field along the corridor. Squashing factor Q -maps of the large-scale topology further confirm super-radial expansion in support of the S-web theory for the slow wind. The thin corridor of upflows is identified as the source region of a slow solar wind stream characterized by ∼300 km s−1 velocities, low proton temperatures of ∼5 eV, extremely high density >100 cm−3, and a short interval of moderate Alfvénicity accompanied by switchback events. When the connectivity changes from the corridor to the eastern side of the AR, the in situ plasma parameters of the slow solar wind indicate a distinctly different source region. These observations provide strong evidence that the narrow open-field corridors, forming part of the S-web, produce some extreme properties in their associated solar wind streams. [ABSTRACT FROM AUTHOR]

Objective. This study aims to assess the sensitivity of epicardial potential-based electrocardiographic imaging (ECGI) to the removal or interpolation of bad leads. Approach. We utilized experimental data from two distinct centers. Langendorff-perfused pig ( n = 2) and dog ( n = 2) hearts were suspended in a human torso-shaped tank and paced from the ventricles. Six different bad lead configurations were designed based on clinical experience. Five interpolation methods were applied to estimate the missing data. Zero-order Tikhonov regularization was used to solve the inverse problem for complete data, data with removed bad leads, and interpolated data. We assessed the quality of interpolated ECG signals and ECGI reconstructions using several metrics, comparing the performance of interpolation methods and the impact of bad lead removal versus interpolation on ECGI. Main results. The performance of ECG interpolation strongly correlated with ECGI reconstruction. The hybrid method exhibited the best performance among interpolation techniques, followed closely by the inverse-forward and Kriging methods. Bad leads located over high amplitude/high gradient areas on the torso significantly impacted ECGI reconstructions, even with minor interpolation errors. The choice between removing or interpolating bad leads depends on the location of missing leads and confidence in interpolation performance. If uncertainty exists, removing bad leads is the safer option, particularly when they are positioned in high amplitude/high gradient regions. In instances where interpolation is necessary, the inverse-forward and Kriging methods, which do not require training, are recommended. Significance. This study represents the first comprehensive evaluation of the advantages and drawbacks of interpolating versus removing bad leads in the context of ECGI, providing valuable insights into ECGI performance., (Creative Commons Attribution license.)

Objective: Despite a long history of ECG-based monitoring of acute ischemia quantified by several widely used clinical markers, the diagnostic performance of these metrics is not yet satisfactory, motivating a data-driven approach to leverage underutilized information in the electrograms. This study introduces a novel metric for acute ischemia, created using a machine learning technique known as Laplacian eigenmaps (LE), and compares the diagnostic and temporal performance of the LE metric against traditional metrics., Methods: The LE technique uses dimensionality reduction of simultaneously recorded time signals to map them into an abstract space in a manner that highlights the underlying signal behavior. To evaluate the performance of an electrogram-based LE metric compared to current standard approaches, we induced episodes of transient, acute ischemia in large animals and captured the electrocardiographic response using up to 600 electrodes within the intramural and epicardial domains., Results: The LE metric generally detected ischemia earlier than all other approaches and with greater accuracy. Unlike other metrics derived from specific features of parts of the signals, the LE approach uses the entire signal and provides a data-driven strategy to identify features that reflect ischemia., Conclusion: The superior performance of the LE metric suggests there are underutilized features of electrograms that can be leveraged to detect the presence of acute myocardial ischemia earlier and more robustly than current methods., Significance: The earlier detection capabilities of the LE metric on the epicardial surface provide compelling motivation to apply the same approach to ECGs recorded from the body surface., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Inverse methods for localization and characterization of cardiac and brain sources from ECG and EEG signals are notoriously ill-conditioned and thus sensitive to SNR in the measurements. Multiple recordings of the same underlying phenomenon are often available, but are contaminated by unmodeled correlated noise such as heart motion from respiration or superposition of atrial activation or on-going EEG in the case of inter-ictal spikes or evoked response in EEG. We address here the open question of how best to incorporate these multiple recordings, comparing standard ensemble averaging, a multichannel non-linear spline-based average designed to be less sensitive to timing variations from motion or modulation, and a probalistic inverse incorporating a data-driven model of the noise correlation and using all recordings jointly. Results are tested on localizations of clincally recorded 120 lead ECGs during ventricular pacing.

The dynamical structure of the brain's electrical signals contains valuable information about its physiology. Here we combine techniques for nonlinear dynamical analysis and manifold identification to reveal complex and recurrent dynamics in interictal epileptiform discharges (IEDs). Our results suggest that recurrent IEDs exhibit some consistent dynamics, which may only last briefly, and so individual IED dynamics may need to be considered in order to understand their genesis. This could potentially serve to constrain the dynamics of the inverse source localization problem.