The ability to predict the occurrence of an epileptic seizure is a safeguard against patient injury and health complications. However, a major challenge in seizure prediction arises from the significant variability observed in patient data. Common patient-specific approaches, which apply to each patient independently, often perform poorly for other patients due to the data variability. The aim of this study is to propose deep learning models which can handle this variability and generalize across various patients. This study addresses this challenge by introducing a novel cross-subject and multi-subject prediction models. Multiple-subject modeling broadens the scope of patient-specific modeling to account for the data from a dedicated ensemble of patients, thereby providing some useful, though relatively modest, level of generalization. The basic neural network architecture of this model is then adapted to cross-subject prediction, thereby providing a broader, more realistic, context of application. For accrued performance, and generalization ability, cross-subject modeling is enhanced by domain adaptation. Experimental evaluation using the publicly available CHB-MIT and SIENA data datasets shows that our multiple-subject model achieved better performance compared to existing works. However, the cross-subject faces challenges when applied to different patients. Finally, through investigating three domain adaptation methods, the model accuracy has been notably improved by 10.30% and 7.4% for the CHB-MIT and SIENA datasets, respectively.

中文翻译：

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基于脑电图的跨受试者癫痫发作预测的领域适应

预测癫痫发作的能力是防止患者受伤和健康并发症的保障。然而，癫痫发作预测的一个主要挑战来自于患者数据中观察到的显着变化。由于数据的可变性，常见的针对每个患者的通用方法通常对其他患者效果不佳。本研究的目的是提出深度学习模型，该模型可以处理这种变异性并在不同患者中推广。本研究通过引入新颖的跨学科和多学科预测模型来解决这一挑战。多受试者建模扩大了患者特定建模的范围，以考虑来自专门的患者群体的数据，从而提供一些有用的（尽管相对适度的）概括水平。然后，该模型的基本神经网络架构适用于跨主题预测，从而提供更广泛、更现实的应用背景。为了获得性能和泛化能力，跨学科建模通过领域适应得到增强。使用公开的 CHB-MIT 和 SIENA 数据集进行的实验评估表明，与现有作品相比，我们的多受试者模型取得了更好的性能。然而，跨学科在应用于不同患者时面临挑战。最后，通过研究三种领域适应方法，CHB-MIT 和 SIENA 数据集的模型精度分别显着提高了 10.30% 和 7.4%。