The present study proposes a deep learning model, named DeepSleepNet, for
automatic sleep stage scoring based on raw single-channel EEG. Most of the
existing methods rely on hand-engineered features which require prior knowledge
of sleep analysis. Only a few of them encode the temporal information such as
transition rules, which is important for identifying the next sleep stages,
into the extracted features. In the proposed model, we utilize Convolutional
Neural Networks to extract time-invariant features, and bidirectional-Long
Short-Term Memory to learn transition rules among sleep stages automatically
from EEG epochs. We implement a two-step training algorithm to train our model
efficiently. We evaluated our model using different single-channel EEGs
(F4-EOG(Left), Fpz-Cz and Pz-Oz) from two public sleep datasets, that have
different properties (e.g., sampling rate) and scoring standards (AASM and
R&K). The results showed that our model achieved similar overall accuracy and
macro F1-score (MASS: 86.2%-81.7, Sleep-EDF: 82.0%-76.9) compared to the
state-of-the-art methods (MASS: 85.9%-80.5, Sleep-EDF: 78.9%-73.7) on both
datasets. This demonstrated that, without changing the model architecture and
the training algorithm, our model could automatically learn features for sleep
stage scoring from different raw single-channel EEGs from different datasets
without utilizing any hand-engineered features.

本研究提出了一种基于 DeepSleepNet 的深度学习模型，用于自动睡眠阶段评分，其利用卷积神经网络提取时间不变特征，并利用双向长短时记忆自动学习睡眠阶段之间的转换规则，无需使用任何手工特征，该模型在不同的单通道 EEG 数据集上均取得了相似的整体准确率和宏 F1 得分。