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.





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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