Neural networks are becoming more and more popular for the analysis of
physiological time-series. The most successful deep learning systems in this
domain combine convolutional and recurrent layers to extract useful features to
model temporal relations. Unfortunately, these recurrent models are difficult
to tune and optimize. In our experience, they often require task-specific
modifications, which makes them challenging to use for non-experts. We propose
U-Time, a fully feed-forward deep learning approach to physiological time
series segmentation developed for the analysis of sleep data. U-Time is a
temporal fully convolutional network based on the U-Net architecture that was
originally proposed for image segmentation. U-Time maps sequential inputs of
arbitrary length to sequences of class labels on a freely chosen temporal
scale. This is done by implicitly classifying every individual time-point of
the input signal and aggregating these classifications over fixed intervals to
form the final predictions. We evaluated U-Time for sleep stage classification
on a large collection of sleep electroencephalography (EEG) datasets. In all
cases, we found that U-Time reaches or outperforms current state-of-the-art
deep learning models while being much more robust in the training process and
without requiring architecture or hyperparameter adaptation across tasks.

本研究提出了 U-Time 算法，通过全连接前馈神经网络实现对睡眠数据进行分割和分类，相较于当下领先的深度学习算法，U-Time 算法具备更加强大的鲁棒性和更简单的训练流程，无需特定任务的调整或超参数调控，在多个研究数据集上展现了优异表现