XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging

By Huy P Phan, Oliver Y. Chen, Minh C. Tran, Philipp Koch, Alfred Mertins, M. de Vos. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020

316 citations recorded by the source corpus.

Automating sleep staging is vital to scale up sleep assessment and diagnosis to serve millions experiencing sleep deprivation and disorders and enable longitudinal sleep monitoring in home environments. Learning from raw polysomnography signals and their derived time-frequency image representations has been prevalent. However, learning from multi-view inputs (e.g., both the raw signals and the time-frequency images) for sleep staging is difficult and not well understood. This work proposes a sequence-to-sequence sleep staging model, XSleepNet,1 that is capable of learning a joint representation from both raw signals and time-frequency images. Since different views may generalize or overfit at different rates, the proposed network is trained such that the learning pace on each view is adapted based on their generalization/overfitting behavior. In simple terms, the learning on a particular view is speeded up when it is generalizing well and slowed down when it is overfitting. View-specific generalization/overfitting measures are computed on-the-fly during the training course and used to derive weights to blend the gradients from different views. As a result, the network is able to retain the representation power of different views in the joint features which represent the underlying distribution better than those learned by each individual view alone. Furthermore, the XSleepNet architecture is principally designed to gain robustness to the amount of training data and to increase the complementarity between the input views. Experimental results on five databases of different sizes show that XSleepNet consistently outperforms the single-view baselines and the multi-view baseline with a simple fusion strategy. Finally, XSleepNet also outperforms prior sleep staging methods and improves previous state-of-the-art results on the experimental databases.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Physiology

View Paper: https://www.semanticscholar.org/paper/3ab0f30c580a4db5e127c7adf9755b5b853c3d82

How researchers see this paper

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

In Cognitive Neuroscience

1. Oliver Y. Chen
2. Huy P Phan
3. M. de Vos

In Computer Vision and Pattern Recognition

1. M. de Vos
2. Alfred Mertins
3. Oliver Y. Chen
4. Philipp Koch
5. Huy P Phan

Citations in the corpus

• SleepTransformer: Automatic Sleep Staging With Interpretability and Uncertainty Quantification (2021, methodology): “In particular, compared to our recent developed model, XSleepNet [16], SleepTransformer’s model size is just two third of it of XSleepNet [16] while it is 2.7 times faster to train.”