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

2026-09-03

AI Summary

本文提出RobustSeiz框架，通过标准化协议评估EEG癫痫检测模型在现实临床干扰下的鲁棒性，使用环境、噪声和对抗变换测试模型性能。

Abstract

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.

Problem

• seizure detection
• robustness
• EEG
• adversarial inputs
• clinical stressors

Innovation

• RobustSeiz
• EEG Seizure Detection
• Model Robustness
• Benchmarking Framework
• Clinical Stressors