Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture
Mingmin Zhao (1) Shichao Yue (1) Dina Katabi (1) Tommi Jaakkola (1) Matt Bianchi (2)
1Massachusetts Institute of Technology
2Massachusetts General Hospital
Overview:
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.
Video:
Paper:
Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture
Mingmin Zhao, Shichao Yue, Dina Katabi, Tommi Jaakkola, Matt Bianchi
International Conference on Machine Learning (ICML’17)
[PDF]
Talk:
Dataset:
This dataset contains RF measurements during sleep with corresponding sleep stage labels.