SzCORE: A Seizure Community Open-source Research Evaluation framework for the validation of EEG-based automated seizure detection algorithms

The need for high-quality automated seizure detection algorithms based on electroencephalography (EEG) becomes ever more pressing with the increasing use of ambulatory and long-term EEG monitoring. Heterogeneity in validation methods of these algorithms influences the reported results and makes comprehensive evaluation and comparison challenging. This heterogeneity concerns in particular the choice of datasets, evaluation methodologies, and performance metrics. In this paper, we propose a unified framework designed to establish standardization in the validation of EEG-based seizure detection algorithms. Based on existing guidelines and recommendations, the framework introduces a set of recommendations and standards related to datasets, file formats, EEG data input content, seizure annotation input and output, cross-validation strategies, and performance metrics. We also propose the 10-20 seizure detection benchmark, a machine-learning benchmark based on public datasets converted to a standardized format. This benchmark defines the machine-learning task as well as reporting metrics. We illustrate the use of the benchmark by evaluating a set of existing seizure detection algorithms. The SzCORE (Seizure Community Open-source Research Evaluation) framework and benchmark are made publicly available along with an open-source software library to facilitate research use, while enabling rigorous evaluation of the clinical significance of the algorithms, fostering a collective effort to more optimally detect seizures to improve the lives of people with epilepsy.

Grouped Variable Selection for Generalized Eigenvalue Problems

Many problems require the selection of a subset of variables from a full set of optimization variables. The computational complexity of an exhaustive search over all possible subsets of variables is, however, prohibitively expensive, necessitating more efficient but potentially suboptimal search strategies. We focus on sparse variable selection for generalized Rayleigh quotient optimization and generalized eigenvalue problems. Such problems often arise in the signal processing field, e.g., in the design of optimal data-dependent filters. We extend and generalize existing work on convex optimization-based variable selection using semi-definite relaxations toward group-sparse variable selection using the $\ell_{1,\infty}$-norm. This group-sparsity allows, for instance, to perform sensor selection for spatio-temporal (instead of purely spatial) filters, and to select variables based on multiple generalized eigenvectors instead of only the dominant one. Furthermore, we extensively compare our method to state-of-the-art methods for sensor selection for spatio-temporal filter design in a simulated sensor network setting. The results show both the proposed algorithm and backward greedy selection method best approximate the exhaustive solution. However, the backward greedy selection has more specific failure cases, in particular for ill-conditioned covariance matrices. As such, the proposed algorithm is the most robust available method for group-sparse variable selection in generalized eigenvalue problems.