This study presents a novel approach for EEG-based seizure detection
leveraging a BERT-based model. The model, BENDR, undergoes a two-phase training
process. Initially, it is pre-trained on the extensive Temple University
Hospital EEG Corpus (TUEG), a 1.5 TB dataset comprising over 10,000 subjects,
to extract common EEG data patterns. Subsequently, the model is fine-tuned on
the CHB-MIT Scalp EEG Database, consisting of 664 EEG recordings from 24
pediatric patients, of which 198 contain seizure events. Key contributions
include optimizing fine-tuning on the CHB-MIT dataset, where the impact of
model architecture, pre-processing, and post-processing techniques are
thoroughly examined to enhance sensitivity and reduce false positives per hour
(FP/h). We also explored custom training strategies to ascertain the most
effective setup. The model undergoes a novel second pre-training phase before
subject-specific fine-tuning, enhancing its generalization capabilities. The
optimized model demonstrates substantial performance enhancements, achieving as
low as 0.23 FP/h, 2.5$\times$ lower than the baseline model, with a lower but
still acceptable sensitivity rate, showcasing the effectiveness of applying a
BERT-based approach on EEG-based seizure detection.

该研究提出了一种新的基于 EEG 的癫痫检测方法，利用一种基于 BERT 的模型。该模型经历了两个阶段的训练过程，分别在 TUEG 和 CHB-MIT 数据集上进行预训练和微调，以提高检测性能。优化后的模型在降低误报率的同时，取得了显著的性能提升，充分展示了 BERT 模型在 EEG 癫痫检测中的有效性。