Electroencephalography (EEG) is a commonly used technology for monitoring brain activities and diagnosing sleep disorders. Clinically, doctors need to manually stage sleep based on EEG signals, which is a time-consuming and laborious task. In this study, we propose a few-shot EEG sleep staging termed transductive prototype optimization network (TPON) method, which aims to improve the performance of EEG sleep staging. Compared with traditional deep learning methods, TPON uses a meta-learning algorithm, which generalizes the classifier to new classes that are not visible in the training set, and only have a few examples for each new class. We learn the prototypes of existing objects through meta-training, and capture the sleep features of new objects through the “learn to learn” method of meta-learning. The prototype distribution of the class is optimized and captured by using support set and unlabeled high confidence samples to increase the authenticity of the prototype. Compared with traditional prototype networks, TPON can effectively solve too few samples in few-shot learning and improve the matching degree of prototypes in prototype network. The experimental results on the public SleepEDF-2013 dataset show that the proposed algorithm outperform than most advanced algorithms in the overall performance. In addition, we experimentally demonstrate the feasibility of cross-channel recognition, which indicates that there are many similar sleep EEG features between different channels. In future research, we can further explore the common features among different channels and investigate the combination of universal features in sleep EEG. Overall, our method achieves high accuracy in sleep stage classification, demonstrating the effectiveness of this approach and its potential applications in other medical fields.

中文翻译：

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基于传导原型优化网络的少样本脑电图睡眠分期

脑电图（EEG）是监测大脑活动和诊断睡眠障碍的常用技术。临床上，医生需要根据脑电信号手动划分睡眠阶段，这是一项费时费力的工作。在本研究中，我们提出了一种称为转导原型优化网络（TPON）的少样本脑电图睡眠分期方法，旨在提高脑电图睡眠分期的性能。与传统的深度学习方法相比，TPON使用元学习算法，将分类器泛化到训练集中不可见的新类，并且每个新类只有几个示例。我们通过元训练学习现有对象的原型，并通过元学习的“学会学习”方法捕获新对象的睡眠特征。通过使用支持集和未标记的高置信度样本来优化和捕获类的原型分布，以增加原型的真实性。与传统原型网络相比，TPON可以有效解决少样本学习中样本过少的问题，提高原型网络中原型的匹配度。在公共SleepEDF-2013数据集上的实验结果表明，所提出的算法在整体性能上优于大多数先进算法。此外，我们通过实验证明了跨通道识别的可行性，这表明不同通道之间存在许多相似的睡眠脑电图特征。在未来的研究中，我们可以进一步探索不同通道之间的共同特征，并研究睡眠脑电图通用特征的组合。总体而言，我们的方法在睡眠阶段分类方面实现了高精度，证明了该方法的有效性及其在其他医学领域的潜在应用。