用于个性化癫痫发作检测的知识蒸馏图神经网络
Knowledge-Distilled Graph Neural Networks for Personalized Epileptic Seizure Detection
AI总结：
本文提出基于知识蒸馏的图神经网络方法，将全电极教师模型迁移至少电极学生模型，并结合个性化通道选择，在TUSZ数据集上实现仅用两个通道即可获得有竞争力的癫痫发作检测性能。
摘要：
用于癫痫发作监测的可穿戴设备能够显著改善癫痫患者的生活质量。然而，现有解决方案大多依赖全电极脑电图（EEG）测量，在日常使用中可能不够方便。本文提出一种新颖的知识蒸馏方法，将训练于全电极数据的复杂癫痫发作检测器（称为教师）中的知识迁移到新检测器（称为学生）中。二者均提供轻量级实现，并显著减少记录EEG所需的电极数量。我们考虑教师和学生癫痫发作检测器均为图神经网络（GNN）的情形，因为这些架构会主动利用连接信息。我们考虑两种情况：（a）使用预选通道为所有患者学习单一学生模型；（b）为每位患者学习个性化学生模型，并通过Gumbel-softmax方法进行个性化通道选择。我们在公开的坦普尔大学医院EEG癫痫发作数据语料库（TUSZ）上的实验表明，知识蒸馏和个性化在提升癫痫发作检测性能方面均发挥重要作用，尤其对于EEG数据稀缺的患者。我们观察到，仅使用两个通道即可获得具有竞争力的癫痫发作检测性能。这反过来表明，我们的方法在更现实的可穿戴设备场景中具有潜力，即使在记录次数很少的情况下也能实现个性化癫痫发作监测。
英文摘要：
Wearable devices for seizure monitoring detection could significantly improve the quality of life of epileptic patients. However, existing solutions that mostly rely on full electrode set of electroencephalogram (EEG) measurements could be inconvenient for every day use. In this paper, we propose a novel knowledge distillation approach to transfer the knowledge from a sophisticated seizure detector (called the teacher) trained on data from the full set of electrodes to learn new detectors (called the student). They are both providing lightweight implementations and significantly reducing the number of electrodes needed for recording the EEG. We consider the case where the teacher and the student seizure detectors are graph neural networks (GNN), since these architectures actively use the connectivity information. We consider two cases (a) when a single student is learnt for all the patients using preselected channels; and (b) when personalized students are learnt for every individual patient, with personalized channel selection using a Gumbelsoftmax approach. Our experiments on the publicly available Temple University Hospital EEG Seizure Data Corpus (TUSZ) show that both knowledge-distillation and personalization play significant roles in improving performance of seizure detection, particularly for patients with scarce EEG data. We observe that using as few as two channels, we are able to obtain competitive seizure detection performance. This, in turn, shows the potential of our approach in more realistic scenario of wearable devices for personalized monitoring of seizures, even with few recordings.
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