EEG is the gold standard for seizure detection in the newborn infant, but EEG interpretation in the preterm group is particularly challenging; trained experts are scarce and the task of interpreting EEG in real-time is arduous. Preterm infants are reported to have a higher incidence of seizures compared to term infants. Preterm EEG morphology differs from that of term infants, which implies that seizure detection algorithms trained on term EEG may not be appropriate. The task of developing preterm specific algorithms becomes extra-challenging given the limited amount of annotated preterm EEG data available. This paper explores novel deep learning (DL) architectures for the task of neonatal seizure detection in preterm infants. The study tests and compares several approaches to address the problem: training on data from full-term infants; training on data from preterm infants; training on age-specific preterm data and transfer learning. The system performance is assessed on a large database of continuous EEG recordings of 575h in duration. It is shown that the accuracy of a validated term-trained EEG seizure detection algorithm, based on a support vector machine classifier, when tested on preterm infants falls well short of the performance achieved for full-term infants. An AUC of 88.3% was obtained when tested on preterm EEG as compared to 96.6% obtained when tested on term EEG. When re-trained on preterm EEG, the performance marginally increases to 89.7%. An alternative DL approach shows a more stable trend when tested on the preterm cohort, starting with an AUC of 93.3% for the term-trained algorithm and reaching 95.0% by transfer learning from the term model using available preterm data. The proposed DL approach avoids time-consuming explicit feature engineering and leverages the existence of the term seizure detection model, resulting in accurate predictions with a minimum amount of annotated preterm data.

中文翻译：

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早产儿脑电图癫痫检测的深度学习

EEG 是新生儿癫痫发作检测的金标准，但早产组的 EEG 解读尤其具有挑战性；训练有素的专家稀缺，实时解读脑电图的任务艰巨。据报道，与足月婴儿相比，早产儿癫痫发作的发生率更高。早产 EEG 形态与足月婴儿不同，这意味着在足月 EEG 上训练的癫痫发作检测算法可能不合适。鉴于可用的注释早产 EEG 数据数量有限，开发早产特定算法的任务变得更具挑战性。本文探讨了用于早产儿新生儿癫痫检测任务的新型深度学习 (DL) 架构。该研究测试并比较了解决该问题的几种方法：对足月婴儿数据进行培训；早产儿数据的培训；针对特定年龄的早产数据和迁移学习的培训。系统性能在 575 个连续 EEG 记录的大型数据库上进行评估h 持续时间。结果表明，在对早产儿进行测试时，基于支持向量机分类器的经过验证的经期训练的脑电图癫痫发作检测算法的准确性远远低于足月儿所达到的性能。在早产 EEG 上测试时获得了 88.3% 的 AUC，而在足月 EEG 上测试时获得了 96.6%。当对早产 EEG 进行重新训练时，性能略微提高到 89.7%。另一种 DL 方法在对早产队列进行测试时显示出更稳定的趋势，从术语训练算法的 AUC 为 93.3% 开始，通过使用可用早产数据从术语模型迁移学习达到 95.0%。所提出的 DL 方法避免了耗时的显式特征工程，并利用了术语癫痫检测模型的存在，