论文标题
Trier：来自EEG记录的模板引导的神经网络，可用于健壮且可解释的睡眠阶段识别
TRIER: Template-Guided Neural Networks for Robust and Interpretable Sleep Stage Identification from EEG Recordings
论文作者
论文摘要
神经网络在训练过程中通常会获得次优表示，从而降低了鲁棒性和分类性能。这是将深度学习应用于生物医学领域的严重问题，因为模型容易受到数据不规则和稀缺性的伤害。在这项研究中，我们提出了一种预训练技术，该技术在睡眠分期任务中应对这一挑战。受到经验丰富的医生用来从特征波形形状或模板模式中对睡眠状态进行分类的常规方法的启发，我们的方法引入了基于余弦相似性的卷积神经网络，以从训练数据中提取代表性波形。之后，这些功能指导模型基于模板模式构建表示形式。通过广泛的实验，我们证明了指导具有模板模式的神经网络是睡眠分期的有效方法，因为（1）分类性能显着增强，并且（2）在几个方面的鲁棒性得到了改善。最后但并非最不重要的一点是，模型的解释表明，在拟议方法的预测期间，正确解决了受过训练专家所利用的显着特征。
Neural networks often obtain sub-optimal representations during training, which degrade robustness as well as classification performances. This is a severe problem in applying deep learning to bio-medical domains, since models are vulnerable to being harmed by irregularities and scarcities in data. In this study, we propose a pre-training technique that handles this challenge in sleep staging tasks. Inspired by conventional methods that experienced physicians have used to classify sleep states from the existence of characteristic waveform shapes, or template patterns, our method introduces a cosine similarity based convolutional neural network to extract representative waveforms from training data. Afterwards, these features guide a model to construct representations based on template patterns. Through extensive experiments, we demonstrated that guiding a neural network with template patterns is an effective approach for sleep staging, since (1) classification performances are significantly enhanced and (2) robustness in several aspects are improved. Last but not least, interpretations on models showed that notable features exploited by trained experts are correctly addressed during prediction in the proposed method.
