arXiv Machine Learning
Oct 12

Expert-level detection of low-voltage intervals in EEG as a robust biomarker of neonatal encephalopathy

The study introduces low‑voltage intervals (LVIs) as an amplitude‑ and duration‑based marker of neonatal EEG discontinuity and develops a ConvNeXt AI model that detects LVIs with expert‑level accuracy (AUC = 0.996, MCC = 0.926). In a cohort of 444 neonates, LVI percentage strongly correlates with EEG background grades, moderately with hypoxic‑ischaemic encephalopathy severity, and predicts seizures (AUC = 0.872). The results demonstrate that automated LVI quantification can serve as a robust biomarker for neonatal encephalopathy and seizure risk.

By John M. O'Toole, Sean R. Mathieson, Aurel Luca, Soraia Ventura, Sean Griffin, Geraldine B. Boylan, Robert Hogan
arXiv Machine Learning
Sep 4

RobustSeiz: An Open-Source Framework for Benchmarking the Robustness of EEG Seizure Detection Models

RobustSeiz is an open‑source, model‑agnostic framework designed to benchmark the robustness of EEG seizure detection models under realistic clinical stressors. It standardizes four public scalp‑EEG corpora into BIDS‑EEG trees, applies controlled distribution shifts—including environmental, noise, and adversarial transforms—across predefined hyperparameter grids, and reports comprehensive performance metrics such as sensitivity, precision, F1, false positives per 24 h, onset timing, and predictive agreement. The framework offers a Dockerized GPU pipeline, experiment registry, and both full‑evaluation and research‑subset modes, and demonstrates its utility by evaluating a contemporary detector on TUSZ across the full shift grid.

By Mohammad Mohammadi, Alireza Zarei