U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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.
Original language | English |
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Title of host publication | Advances in Neural Information Processing Systems 32 (NIPS 2019) |
Volume | 32 |
Publisher | NIPS Proceedings |
Publication date | 1 Oct 2019 |
Pages | 4415-4426 |
Publication status | Published - 1 Oct 2019 |
Event | 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) - Vancouver, Canada Duration: 8 Dec 2019 → 14 Dec 2019 |
Conference
Conference | 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) |
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Land | Canada |
By | Vancouver |
Periode | 08/12/2019 → 14/12/2019 |
- Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing, Statistics - Machine Learning
Research areas
ID: 239571874