RobustSeiz：用于基准测试脑电图癫痫检测模型鲁棒性的开源框架
RobustSeiz: An Open-Source Framework for Benchmarking the Robustness of EEG Seizure Detection Models
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中文总结 AI 辅助
该研究推出开源框架RobustSeiz，标准化4个EEG语料库，可对癫痫检测模型在环境、噪声等偏移下的鲁棒性进行基准测试，扩展了部署前评估范围。
中文摘要 AI 辅助
尽管癫痫检测器在保留的脑电图（EEG）数据上表现出强劲性能，但在真实世界的采集变异、伪影和对抗性输入下可能失效。我们推出RobustSeiz，这是一个开源、与模型无关的框架，提供标准化、可复现的协议，用于在部署前针对受临床驱动的受控分布偏移对癫痫检测器进行压力测试和比较。我们将四个公开头皮EEG语料库（CHB-MIT、TUSZ、Siena和SeizeIT1）标准化为BIDS-EEG树，并在保留的划分上评估与受试者无关的检测器。环境、噪声和对抗性变换在预定义的超参数网格上进行遍历，每次运行报告样本级和事件级的灵敏度、精确率、F1值、每24小时假阳性、Lead和Lag发作时间，以及蒙特卡洛dropout预测一致性。RobustSeiz包含Docker化的GPU流水线、实验注册表，以及全评估和研究子集模式。我们使用TUSZ上的当代癫痫检测器在完整的已实现偏移网格上演示该框架；AWGN分析展示了扰动严重程度如何改变检测质量、发作时间和预测一致性。RobustSeiz提供了一个共享基准标准，用于在现实临床压力源下评估癫痫检测器的鲁棒性，将部署前评估从干净数据准确率扩展。
英文摘要
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.
发表机构
- Sharif University of Technology（谢里夫理工大学）
机构由 AI 辅助整理，请以论文原文为准。
