Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture

Mingmin Zhao, Shichao Yue, Dina Katabi, Tommi S. Jaakkola, Matt T. Bianchi
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:4100-4109, 2017.

Abstract

We focus on predicting sleep stages from radio measurements without any attached sensors on subjects. We introduce a new predictive model that combines convolutional and recurrent neural networks to extract sleep-specific subject-invariant features from RF signals and capture the temporal progression of sleep. A key innovation underlying our approach is a modified adversarial training regime that discards extraneous information specific to individuals or measurement conditions, while retaining all information relevant to the predictive task. We analyze our game theoretic setup and empirically demonstrate that our model achieves significant improvements over state-of-the-art solutions.

Cite this Paper


BibTeX
@InProceedings{pmlr-v70-zhao17d, title = {Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture}, author = {Mingmin Zhao and Shichao Yue and Dina Katabi and Tommi S. Jaakkola and Matt T. Bianchi}, booktitle = {Proceedings of the 34th International Conference on Machine Learning}, pages = {4100--4109}, year = {2017}, editor = {Precup, Doina and Teh, Yee Whye}, volume = {70}, series = {Proceedings of Machine Learning Research}, month = {06--11 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v70/zhao17d/zhao17d.pdf}, url = { http://proceedings.mlr.press/v70/zhao17d.html }, abstract = {We focus on predicting sleep stages from radio measurements without any attached sensors on subjects. We introduce a new predictive model that combines convolutional and recurrent neural networks to extract sleep-specific subject-invariant features from RF signals and capture the temporal progression of sleep. A key innovation underlying our approach is a modified adversarial training regime that discards extraneous information specific to individuals or measurement conditions, while retaining all information relevant to the predictive task. We analyze our game theoretic setup and empirically demonstrate that our model achieves significant improvements over state-of-the-art solutions.} }
Endnote
%0 Conference Paper %T Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture %A Mingmin Zhao %A Shichao Yue %A Dina Katabi %A Tommi S. Jaakkola %A Matt T. Bianchi %B Proceedings of the 34th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2017 %E Doina Precup %E Yee Whye Teh %F pmlr-v70-zhao17d %I PMLR %P 4100--4109 %U http://proceedings.mlr.press/v70/zhao17d.html %V 70 %X We focus on predicting sleep stages from radio measurements without any attached sensors on subjects. We introduce a new predictive model that combines convolutional and recurrent neural networks to extract sleep-specific subject-invariant features from RF signals and capture the temporal progression of sleep. A key innovation underlying our approach is a modified adversarial training regime that discards extraneous information specific to individuals or measurement conditions, while retaining all information relevant to the predictive task. We analyze our game theoretic setup and empirically demonstrate that our model achieves significant improvements over state-of-the-art solutions.
APA
Zhao, M., Yue, S., Katabi, D., Jaakkola, T.S. & Bianchi, M.T.. (2017). Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture. Proceedings of the 34th International Conference on Machine Learning, in Proceedings of Machine Learning Research 70:4100-4109 Available from http://proceedings.mlr.press/v70/zhao17d.html .

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