Abstract：
Automatic seizure detection from Electroencephalography (EEG) is of great importance in aiding the diagnosis and treatment of epilepsy due to the advantages of convenience and economy. Existing seizure detection methods are usually patient-specific, the training and testing are carried out on the same patient, limiting their scalability to other patients. To address this issue, we propose a cross-subject seizure detection method via unsupervised domain adaptation. The proposed method aims to obtain seizure specific information through shallow and deep feature alignments. For shallow feature alignment, we use convolutional neural network (CNN) to extract seizure-related features. The distribution gap of the shallow features between different patients is minimized by multi-kernel maximum mean discrepancies (MK-MMD). For deep feature alignment, adversarial learning is utilized. The feature extractor tries to learn feature representations that try to confuse the domain classifier, making the extracted deep features more generalizable to new patients. The performance of our method is evaluated on the CHB-MIT and Siena databases in epoch-based experiments. Additionally, event-based experiments are also conducted on the CHB-MIT dataset. The results validate the feasibility of our method in diminishing the domain disparities among different patients.
摘要：
脑电图（EEG）的自动癫痫发作检测由于具有方便和经济的优点，在帮助癫痫的诊断和治疗中具有重要意义。现有的癫痫发作检测方法通常是针对患者的，培训和测试是在同一个病人身上进行的，限制了他们对其他患者的可扩展性。为了解决这个问题，我们提出了一种通过无监督域适应的跨主题癫痫发作检测方法。所提出的方法旨在通过浅层和深层特征对齐获得特定的信息。对于浅特征对齐，我们使用卷积神经网络(CNN)来提取与癫痫发作相关的特征。通过多核最大平均差异（MK-MMD）将不同患者之间的浅特征的分布间隙最小化。对于深层特征对齐，利用对抗性学习。特征提取器尝试学习试图混淆域分类器的特征表示，使提取的深层特征更易于推广到新患者。在基于时代的实验中，在CHB-MIT和Siena数据库上评估了我们方法的性能。此外,基于事件的实验也在CHB-MIT数据集上进行。结果验证了我们的方法在减少不同患者之间的领域差异方面的可行性。
