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Neural networks are becoming more and more popular for the analysis ofphysiological time-series. The most successful deep learning systems in thisdomain combine convolutional and recurrent layers to extract useful features tomodel temporal relations. Unfortunately, these recurrent models are difficultto tune and optimize. In our experience, they often require task-specificmodifications, which makes them challenging to use for non-experts. We proposeU-Time, a fully feed-forward deep learning approach to physiological timeseries segmentation developed for the analysis of sleep data. U-Time is atemporal fully convolutional network based on the U-Net architecture that wasoriginally proposed for image segmentation. U-Time maps sequential inputs ofarbitrary length to sequences of class labels on a freely chosen temporalscale. This is done by implicitly classifying every individual time-point ofthe input signal and aggregating these classifications over fixed intervals toform the final predictions. We evaluated U-Time for sleep stage classificationon a large collection of sleep electroencephalography (EEG) datasets. In allcases, we found that U-Time reaches or outperforms current state-of-the-artdeep learning models while being much more robust in the training process andwithout requiring architecture or hyperparameter adaptation across tasks.

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