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

U-Time: 一种用于时间序列分割的全卷积网络，应用于睡眠分期

U-Time: A Fully Convolutional Network for Time Series Segmentation  Applied to Sleep Staging

Modeling physiological time-series in ICU is of high clinical importance.
However, data collected within ICU are irregular in time and often contain
missing measurements. Since absence of a measure would signify its lack of
importance, the missingness is indeed informative and might reflect the
decision making by the clinician. Here we propose a deep learning architecture
that can effectively handle these challenges for predicting ICU mortality
outcomes. The model is based on Long Short-Term Memory, and has layered
attention mechanisms. At the sensing layer, the model decides whether to
observe and incorporate parts of the current measurements. At the reasoning
layer, evidences across time steps are weighted and combined. The model is
evaluated on the PhysioNet 2012 dataset showing competitive and interpretable
results.

利用长短期记忆和层次注意力机制，建立深度学习模型解决 ICU 中生理时间序列数据的不规则和缺失问题，实现对 ICU 死亡风险的预测，并在 PhysioNet 2012 数据集上取得了竞争力和可解释性的结果。