School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China.
Int J Neural Syst. 2024 Oct;34(10):2450055. doi: 10.1142/S0129065724500552.
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.
自动从脑电图（EEG）中检测癫痫发作对于辅助癫痫的诊断和治疗非常重要，因为它具有方便和经济的优势。现有的癫痫发作检测方法通常是针对特定患者的，训练和测试都是在同一位患者上进行的，这限制了它们在其他患者中的可扩展性。为了解决这个问题，我们提出了一种通过无监督域自适应的跨主体癫痫发作检测方法。所提出的方法旨在通过浅层和深层特征对齐来获得癫痫特有的信息。对于浅层特征对齐，我们使用卷积神经网络（CNN）提取与癫痫相关的特征。通过多核最大均值差异（MK-MMD）最小化不同患者之间浅层特征的分布差距。对于深层特征对齐，我们利用对抗学习。特征提取器试图学习混淆域分类器的特征表示，从而使提取的深层特征更能推广到新的患者。我们的方法在基于 epoch 的实验中在 CHB-MIT 和 Siena 数据库上进行了性能评估。此外，还在 CHB-MIT 数据集上进行了基于事件的实验。结果验证了我们的方法在减小不同患者之间的域差异方面的可行性。
