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

By Mathias Perslev, M. Jensen, Sune Darkner, Poul J. Jennum, Christian Igel. Neural Information Processing Systems, 2019

337 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/8ca14463e6df517cec99df12c650c2319f4b0027

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Signal Processing

1. Tian Zhou
2. Liang Sun
3. Haixu Wu
4. Mingsheng Long
5. Qingsong Wen
6. Jianmin Wang
7. Yiyuan Yang
8. Jiehui Xu

Citations in the corpus

• DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection (2023, methodology): “[32], CL-MPPCA [67]; the density-estimation models: LOF [10], MPPCACD [76], DAGMM [92]; the clustering-based methods: DeepSVDD [60], THOC [64], ITAD [65]; the classic methods: OCSVM [68], OCSVM-based subsequence clustering (OCSVM*), IForest [46], IForest-based subsequence clustering (IForest*), Grad”
• SleepTransformer: Automatic Sleep Staging With Interpretability and Uncertainty Quantification (2021, neutral): “64 × 10 5 379 U-Time [45] 1 .”
• Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy (2021, methodology): “Thus, we also include the BOCPD (Adams & MacKay, 2007) and TS-CP2 (Deldari et al., 2021) from change point detection and UTime (Perslev et al., 2019) from time series segmentation for comparison.”
• XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging (2020, neutral): “Under a similar experimental condition, ARNN+RNN outperforms all the existing work across all the scenarios, except U-time [49] on SleepEDF-20.”