论文标题
通过知识蒸馏，弥合患者特异性和与患者无关的癫痫发作预测之间的差距
Bridging the Gap Between Patient-specific and Patient-independent Seizure Prediction via Knowledge Distillation
论文作者
论文摘要
客观的。深度神经网络（DNN）在各种脑机界面应用中表现出了前所未有的成功，例如癫痫发作预测。但是，由于癫痫信号的高个性化特征，现有方法通常以患者特定的方式训练模型。因此，只能将每个受试者的标记录音数量有限用于培训。结果，由于培训数据的不足，目前基于DNN的方法在一定程度上表现出较差的泛化能力。另一方面，与患者无关的模型试图利用更多的患者数据通过将患者数据汇总在一起为所有患者培训通用模型。尽管采用了不同的技术，但结果表明，由于患者的个体差异很高，与患者独立的模型相比性能差。因此，在患者特异性和与患者无关的模型之间存在很大的差距。方法。在本文中，我们提出了一种基于知识蒸馏的新型培训计划，该方案利用了来自多个受试者的大量数据。首先，它从具有预训练的通用模型的所有可用受试者的信号中提取信息。然后，可以借助蒸馏知识和其他个性化数据获得特定于患者的模型。主要结果。通过我们提出的计划，对波士顿 - 米特儿童医院的Seeg数据库进行了四种最先进的癫痫发作预测方法。由此产生的准确性，敏感性和错误的预测率表明，我们提出的培训方案一致地提高了最先进方法的预测性能。意义。提出的训练方案显着提高了患者特异性癫痫发作预测因子的性能，并弥合了患者特异性和与患者无关的预测指标之间的差距。
Objective. Deep neural networks (DNNs) have shown unprecedented success in various brain-machine interface applications such as epileptic seizure prediction. However, existing approaches typically train models in a patient-specific fashion due to the highly personalized characteristics of epileptic signals. Therefore, only a limited number of labeled recordings from each subject can be used for training. As a consequence, current DNN based methods demonstrate poor generalization ability to some extent due to the insufficiency of training data. On the other hand, patient-independent models attempt to utilize more patient data to train a universal model for all patients by pooling patient data together. Despite different techniques applied, results show that patient-independent models perform worse than patient-specific models due to high individual variation across patients. A substantial gap thus exists between patient-specific and patient-independent models. Approach. In this paper, we propose a novel training scheme based on knowledge distillation which makes use of a large amount of data from multiple subjects. It first distills informative features from signals of all available subjects with a pre-trained general model. A patient-specific model can then be obtained with the help of distilled knowledge and additional personalized data. Main results. Four state-of-the-art seizure prediction methods are trained on the Children's Hospital of Boston-MIT sEEG database with our proposed scheme. The resulting accuracy, sensitivity, and false prediction rate show that our proposed training scheme consistently improves the prediction performance of state-of-the-art methods by a large margin. Significance. The proposed training scheme significantly improves the performance of patient-specific seizure predictors and bridges the gap between patient-specific and patient-independent predictors.
