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<p>Reliable evaluation is essential for clinically usable automated electroencephalography (EEG) seizure detection. Although the TUH EEG Seizure Corpus (TUSZ) is the de facto benchmark, studies often use non-canonical splits, mix patients across partitions, or evaluate on seizure-only segments, making results hard to compare. We release an open-source, transparent benchmark for offline binary seizure detection on TUSZ v2.0.3 that strictly follows the official Train/Dev/Eval partition and evaluates in continuous EEG. Each 60-s window (15-s stride) is mapped to expert features and classified by three CatBoost ensembles: a within-window base model and two temporal-context models (full and high-sensitivity). We cast operating-point selection as an alarm-budget decision problem: on the Development set, we jointly choose the score threshold and event post-processing to maximize event sensitivity while enforcing specificity ≥ 0.93 and false alarms ≤ 0.69 per hour, then freeze this decision policy and apply it once to the held-out Eval set. On Eval, we obtain AUROC 0.92 and balanced accuracy 0.83 at the window level and event sensitivity 0.75 at 0.68 false alarms/hour (time-based positive predictive value [PPV] 0.57; recall 0.76). We provide the protocol and results as a reproducible baseline and clinically oriented benchmark for comparing past and future models.</p>

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A transparent AI assurance and benchmarking framework for EEG seizure detection on TUSZ seeded with a reproducible gradient-boosting ensemble

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月27日

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  • Morteza Zabihi,
  • Emily J. Gilmore,
  • Kan Ding,
  • Eric S. Rosenthal

摘要

Reliable evaluation is essential for clinically usable automated electroencephalography (EEG) seizure detection. Although the TUH EEG Seizure Corpus (TUSZ) is the de facto benchmark, studies often use non-canonical splits, mix patients across partitions, or evaluate on seizure-only segments, making results hard to compare. We release an open-source, transparent benchmark for offline binary seizure detection on TUSZ v2.0.3 that strictly follows the official Train/Dev/Eval partition and evaluates in continuous EEG. Each 60-s window (15-s stride) is mapped to expert features and classified by three CatBoost ensembles: a within-window base model and two temporal-context models (full and high-sensitivity). We cast operating-point selection as an alarm-budget decision problem: on the Development set, we jointly choose the score threshold and event post-processing to maximize event sensitivity while enforcing specificity ≥ 0.93 and false alarms ≤ 0.69 per hour, then freeze this decision policy and apply it once to the held-out Eval set. On Eval, we obtain AUROC 0.92 and balanced accuracy 0.83 at the window level and event sensitivity 0.75 at 0.68 false alarms/hour (time-based positive predictive value [PPV] 0.57; recall 0.76). We provide the protocol and results as a reproducible baseline and clinically oriented benchmark for comparing past and future models.