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Life sciences · Preprint
arXiv · September 3, 2026
Early or partial results. Treat as a signal, not a conclusion.
RobustSeiz is an open-source, model-agnostic benchmarking framework designed to stress-test seizure detection algorithms under controlled, clinically motivated distribution shifts before deployment. The work demonstrates the framework with one contemporary detector on TUSZ data, reporting multiple robustness metrics including sensitivity, precision, F1, and false positive rate; it establishes a standardized protocol but does not yet evaluate clinical utility or compare multiple detectors systematically.
Framework development study with proof-of-concept demonstration. Scalp EEG signals from four public research corpora (CHB-MIT, TUSZ, Siena, SeizeIT1); subject-independent detector evaluation.. Intervention: RobustSeiz open-source framework for standardized stress-testing of seizure detectors under controlled distribution shifts.
Framework standardizes four public scalp-EEG corpora (CHB-MIT, TUSZ, Siena, SeizeIT1) into BIDS-EEG trees for subject-independent evaluation Reports sample- and event-level sensitivity, precision, F1, false positives per 24 h, Lead and Lag onset timing, and Monte Carlo dropout predictive agreement across environmental, noise, and adversarial transforms Demonstration with contemporary seizure detector on TUSZ shows how perturbation severity changes detection quality, onset timing, and predictive agreement
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Clinicians and researchers deploying seizure detection systems should view this as a pre-deployment benchmarking tool to identify failure modes under realistic clinical stressors; it does not yet provide evidence that use of this framework improves clinical outcomes or detector performance in practice.
This is a methodological framework paper describing a standardized benchmarking tool for seizure detection models, not a clinical trial or efficacy study; it demonstrates proof-of-concept with one detector but does not establish clinical performance or practice guidance.
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Quoted from the source exactly as published.
Clinicians and researchers deploying seizure detection systems should view this as a pre-deployment benchmarking tool to identify failure modes under realistic clinical stressors; it does not yet provide evidence that use of this framework improves clinical outcomes or detector performance in practice.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Despite strong performance on held-out electroencephalography (EEG) data, seizure detectors may fail under real-world acquisition variability, artifacts, and adversarial inputs. We introduce RobustSeiz, an open-source, model-agnostic framework that provides a standardized, reproducible protocol for stress-testing and comparing seizure detectors under controlled, clinically motivated distribution shifts before deployment. We standardize four public scalp-EEG corpora (CHB-MIT, TUSZ, Siena, and SeizeIT1) into BIDS-EEG trees and evaluate subject-independent detectors on held-out splits. Environment, noise, and adversarial transforms are swept over predefined hyperparameter grids. Each run reports sample- and event-level sensitivity, precision, F1, false positives per 24 h, Lead and Lag onset timing, and Monte Carlo dropout predictive agreement. RobustSeiz includes a Dockerized GPU pipeline, experiment registry, and full-evaluation and research-subset modes. We demonstrate the framework with a contemporary seizure detector on TUSZ across the complete implemented shift grid; an AWGN analysis illustrates how perturbation severity changes detection quality, onset timing, and predictive agreement. RobustSeiz provides a shared benchmarking standard for evaluating seizure-detector robustness under realistic clinical stressors, extending pre-deployment assessment beyond clean-data accuracy.
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