Learning Sleep Stages from Radio Signals:
A Conditional Adversarial Architecture

Mingmin Zhao1      Shichao Yue1      Dina Katabi1      Tommi Jaakkola1      Matt Bianchi2

1Massachusetts Institute of Technology
2Massachusetts General Hospital


RF-Sleep learns to predict 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.

Open Positions:

We have openings for postdocs and interns! If you are interested in working on exciting projects like RF-Sleep, please send an email along with your resume to Prof. Dina Katabi at dk@mit.edu.





This dataset contains RF measurements during sleep with corresponding sleep stage labels.
To get access to the dataset, please read and sign the Data Use Agreement and upload it here.

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