2 research outputs found
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging
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.Comment: To appear in Advances in Neural Information Processing Systems
(NeurIPS), 201