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

TL;DR

U-Time is a fully convolutional neural network based on U-Net architecture that efficiently segments physiological time series data, specifically sleep EEG, outperforming existing models with greater robustness and ease of use.

Contribution

This paper introduces U-Time, a fully convolutional, non-recurrent deep learning model for time series segmentation, simplifying training and improving performance in sleep staging tasks.

Findings

U-Time matches or exceeds state-of-the-art sleep staging models.

It is more robust and requires no task-specific tuning.

Performs well across diverse EEG datasets.

Abstract

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.