Recent developments in wearable devices have made accurate and efficient seizure detection more important than ever. A challenge in seizure detection is that patient-specific models typically outperform patient-independent models. However, in a wearable device one typically starts with a patient-independent model, until such patient-specific data is available. To avoid having to construct a new classifier with this data, as required in conventional kernel machines, we propose a transfer learning approach with a tensor kernel machine. This method learns the primal weights in a compressed form using the canonical polyadic decomposition, making it possible to efficiently update the weights of the patient-independent model with patient-specific data. The results show that this patient fine-tuned model reaches as high a performance as a patient-specific SVM model with a model size that is twice as small as the patient-specific model and ten times as small as the patient-independent model.

翻译：近期可穿戴设备的发展使得准确高效的癫痫发作检测变得比以往任何时候都更加重要。癫痫发作检测面临的一个挑战是，患者特异性模型通常优于患者无关模型。然而，在可穿戴设备中，通常最初使用的是患者无关模型，直到获得该患者的特异性数据为止。为了避免像传统核机器那样必须用这些数据构建新的分类器，我们提出了一种采用张量核机器的迁移学习方法。该方法利用规范多线性分解以压缩形式学习原始权重，从而能够利用患者特异性数据高效地更新患者无关模型的权重。结果表明，这种患者微调模型的性能达到了与患者特异性SVM模型相当的水平，而其模型大小仅为患者特异性模型的一半，并且比患者无关模型小十倍。