Classification of Event-Related Potentials with Regularized Spatiotemporal LCMV Beamforming

The usability of EEG-based visual brain–computer interfaces (BCIs) based on event-related potentials (ERPs) benefits from reducing the calibration time before BCI operation. Linear decoding models, such as the spatiotemporal beamformer model, yield state-of-the-art accuracy. Although the training time of this model is generally low, it can require a substantial amount of training data to reach functional performance. Hence, BCI calibration sessions should be sufficiently long to provide enough training data. This work introduces two regularized estimators for the beamformer weights. The first estimator uses cross-validated L2-regularization. The second estimator exploits prior information about the structure of the EEG by assuming Kronecker–Toeplitz-structured covariance. The performances of these estimators are validated and compared with the original spatiotemporal beamformer and a Riemannian-geometry-based decoder using a BCI dataset with P300-paradigm recordings for 21 subjects. Our results show that the introduced estimators are well-conditioned in the presence of limited training data and improve ERP classification accuracy for unseen data. Additionally, we show that structured regularization results in lower training times and memory usage, and a more interpretable classification model

Classification of Event-Related Potentials with Regularized Spatiotemporal LCMV Beamforming

The usability of EEG-based visual brain–computer interfaces (BCIs) based on event-related potentials (ERPs) benefits from reducing the calibration time before BCI operation. Linear decoding models, such as the spatiotemporal beamformer model, yield state-of-the-art accuracy. Although the training time of this model is generally low, it can require a substantial amount of training data to reach functional performance. Hence, BCI calibration sessions should be sufficiently long to provide enough training data. This work introduces two regularized estimators for the beamformer weights. The first estimator uses cross-validated L2-regularization. The second estimator exploits prior information about the structure of the EEG by assuming Kronecker–Toeplitz-structured covariance. The performances of these estimators are validated and compared with the original spatiotemporal beamformer and a Riemannian-geometry-based decoder using a BCI dataset with P300-paradigm recordings for 21 subjects. Our results show that the introduced estimators are well-conditioned in the presence of limited training data and improve ERP classification accuracy for unseen data. Additionally, we show that structured regularization results in lower training times and memory usage, and a more interpretable classification model

Aortic Valve Surgery in Nonelderly Patients: Insights Gained From AVIATOR

Aortic valve surgery in non-elderly patients represents a very challenging patient population. The younger the patient is at the point of aortic valve intervention, the longer their anticipated life expectancy will be, with longer exposure to valve-related complications and risk for re-operation. Although the latest international guidelines recommend aortic valve repair in patients with aortic valve insufficiency, what we see in the real world is that the vast majority of these aortic valves are replaced. However, current prosthetic valves has now been shown to lead to significant loss of life expectancy for non-elderly patients up to 50% for patients in their 40s undergoing mechanical aortic valve replacement. Bioprostheses carry an even worse long-term survival, with higher rates of re-intervention. The promise of trans-catheter valve-in-valve technology is accentuating the trend of bioprosthetic implantation in younger patients, without yet the appropriate evidence. In contrast, aortic valve repair has shown excellent outcomes in terms of quality of life, freedom from re-operation and freedom from major adverse valve-related events with similar life expectancy to general population as it is also found for the Ross procedure, the only available living valve substitute. We are at a time when the paradigm of aortic valve surgery needs to change for the better. To better serve our patients, we must acquire high quality real-world evidence from multiple centers globally – this is the vision of the AVIATOR registry and our common responsibility

AVIATOR: An open international registry to evaluate medical and surgical outcomes of aortic valve insufficiency and ascending aorta aneurysm

Objectives: Current national registries are lacking detailed pathology-driven analysis and long-term patients outcomes. The Heart Valve Society (HVS) aortic valve (AV) repair research network started the Aortic Valve Insufficiency and ascending aorta Aneurysm InternATiOnal Registry (AVIATOR) to evaluate long-term patient outcomes of AV repair and replacement. The purpose of the current report is to describe the AVIATOR initiative and report in a descriptive manner the patients included. Methods: The AV repair research network includes surgeons, cardiologists, and scientists and established an online database compliant with the guidelines for reporting valve-related events. Prospective inclusion started from January 2013. Adult patients (18 years or older) who were operated on between 1995 and 2017 with complete procedural specification of the type of repair/replacement were selected for descriptive analysis. Results: Currently 58 centers from 17 countries include 4896 patients with 89% AV repair (n = 4379) versus 11% AV replacement (n = 517). AV repair was either isolated (28%), or associated with tubular/partial root replacement (22%) or valve-sparing root replacement (49%) with an in-hospital mortality of 0.5%, 1.7%, and 1.2%, respectively. AV replacement was either isolated (24%), associated with tubular/partial root replacement (17%) or root replacement (59%) with an in-hospital mortality of 1%, 2.6%, and 2.0%, respectively. Conclusions: The multicenter surgical AVIATOR registry, by applying uniform definitions, should provide a solid evidence base to evaluate the place of repair versus replacement on the basis of long-term patient outcomes. Obtaining data completeness and adequate representation of all surgery types remain challenging. Toward the near future AVIATOR-medical will start to study natural history, as will AVIATOR-kids, with a focus on pediatric disease