论文标题
通过头皮数据来增强基于EAR-EEG的睡眠阶段的知识蒸馏框架
A Knowledge Distillation Framework For Enhancing Ear-EEG Based Sleep Staging With Scalp-EEG Data
论文作者
论文摘要
睡眠在人类的幸福中起着至关重要的作用。传统的睡眠研究使用多个多摄影学与不适感有关，并且通常由收购设置引起的睡眠质量较低。先前的工作集中在开发较低的令人难以置信的方法来进行高质量的睡眠研究，而EAR-EEG是受欢迎的替代方法。但是，基于EAR-EEG的睡眠分期的表现仍然不如基于头皮的睡眠分期。为了解决头皮EEG和基于EAR-EEG的睡眠阶段之间的性能差距，我们提出了一种跨模式知识蒸馏策略，这是一种域适应方法。我们的实验和分析验证了现有体系结构所提出的方法的有效性，在该方法中，它可以提高基于EAR-EEG的睡眠分期的准确性3.46％，而Cohen的Kappa系数则提高了0.038的余量。
Sleep plays a crucial role in the well-being of human lives. Traditional sleep studies using Polysomnography are associated with discomfort and often lower sleep quality caused by the acquisition setup. Previous works have focused on developing less obtrusive methods to conduct high-quality sleep studies, and ear-EEG is among popular alternatives. However, the performance of sleep staging based on ear-EEG is still inferior to scalp-EEG based sleep staging. In order to address the performance gap between scalp-EEG and ear-EEG based sleep staging, we propose a cross-modal knowledge distillation strategy, which is a domain adaptation approach. Our experiments and analysis validate the effectiveness of the proposed approach with existing architectures, where it enhances the accuracy of the ear-EEG based sleep staging by 3.46% and Cohen's kappa coefficient by a margin of 0.038.
