# What Radio Waves Tell Us about Sleep

arXiv: https://arxiv.org/abs/2405.11739 · HTML: https://mlsanity.com/paper/2405.11739/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 60 references that are in the map.
- First posted on arXiv 20 May 2024.

## Builds on (most cited first, 5 of 5)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Decoupling Representation and Classifier for Long-Tailed Recognition](https://mlsanity.com/paper/1910.09217/) (2019)
- [DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG](https://mlsanity.com/paper/1703.04046/) (2017)
- [The Modality Focusing Hypothesis: Towards Understanding Crossmodal Knowledge Distillation](https://mlsanity.com/paper/2206.06487/) (2022)
- [Sleep Staging from Electrocardiography and Respiration with Deep Learning](https://mlsanity.com/paper/1908.11463/) (2019)

## Built on by (most cited first, 4 of 4)

- [Scalable RF Simulation in Generative 4D Worlds](https://mlsanity.com/paper/2508.12176/) (2025)
- [Transformer Model Detects Antidepressant Use From a Single Night of Sleep, Unlocking an Adherence Biomarker](https://mlsanity.com/paper/2510.10364/) (2025)
- [Using Legacy Polysomnography Data to Train a Radar System to Quantify Sleep in Older Adults and People living with Dementia](https://mlsanity.com/paper/2601.04057/) (2026)
- [Physiology as Language: Translating Respiration to Sleep EEG](https://mlsanity.com/paper/2602.00526/) (2026)

## Abstract (arXiv)

The ability to assess sleep at home, capture sleep stages, and detect the occurrence of apnea (without on-body sensors) simply by analyzing the radio waves bouncing off people's bodies while they sleep is quite powerful. Such a capability would allow for longitudinal data collection in patients' homes, informing our understanding of sleep and its interaction with various diseases and their therapeutic responses, both in clinical trials and routine care. In this article, we develop an advanced machine learning algorithm for passively monitoring sleep and nocturnal breathing from radio waves reflected off people while asleep. Validation results in comparison with the gold standard (i.e., polysomnography) (n=849) demonstrate that the model captures the sleep hypnogram (with an accuracy of 81% for 30-second epochs categorized into Wake, Light Sleep, Deep Sleep, or REM), detects sleep apnea (AUROC = 0.88), and measures the patient's Apnea-Hypopnea Index (ICC=0.95; 95% CI = [0.93, 0.97]). Notably, the model exhibits equitable performance across race, sex, and age. Moreover, the model uncovers informative interactions between sleep stages and a range of diseases including neurological, psychiatric, cardiovascular, and immunological disorders. These findings not only hold promise for clinical practice and interventional trials but also underscore the significance of sleep as a fundamental component in understanding and managing various diseases.