# Sleep Staging from Electrocardiography and Respiration with Deep Learning

arXiv: https://arxiv.org/abs/1908.11463 · HTML: https://mlsanity.com/paper/1908.11463/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2019.

## Built on by (most cited first, 11 of 11)

- [SleepPPG-Net: a deep learning algorithm for robust sleep staging from continuous photoplethysmography](https://mlsanity.com/paper/2202.05735/) (2022)
- [A Deep Knowledge Distillation framework for EEG assisted enhancement of single-lead ECG based sleep staging](https://mlsanity.com/paper/2112.07252/) (2021)
- [SleepVST: Sleep Staging from Near-Infrared Video Signals using Pre-Trained Transformers](https://mlsanity.com/paper/2404.03831/) (2024)
- [What Radio Waves Tell Us about Sleep](https://mlsanity.com/paper/2405.11739/) (2024)
- [sleep2vec: Unified Cross-Modal Alignment for Heterogeneous Nocturnal Biosignals](https://mlsanity.com/paper/2602.13857/) (2026)
- [SleepNetZero: Zero-Burden Zero-Shot Reliable Sleep Staging With Neural Networks Based on Ballistocardiograms](https://mlsanity.com/paper/2410.22646/) (2024)
- [EEG aided boosting of single-lead ECG based sleep staging with Deep Knowledge Distillation](https://mlsanity.com/paper/2211.13125/) (2022)
- [Mamba-based Deep Learning Approach for Sleep Staging on a Wireless Multimodal Wearable System without Electroencephalography](https://mlsanity.com/paper/2412.15947/) (2024)
- [Sleep Apnea and Respiratory Anomaly Detection from a Wearable Band and Oxygen Saturation](https://mlsanity.com/paper/2102.13473/) (2021)
- [Energy-Efficient Real-Time 4-Stage Sleep Classification at 10-Second Resolution](https://mlsanity.com/paper/2508.11664/) (2025)
- [Holter-to-Sleep: AI-Enabled Repurposing of Single-Lead ECG for Sleep Phenotyping](https://mlsanity.com/paper/2603.18714/) (2026)

## Abstract (arXiv)

Study Objective: Sleep is reflected not only in the electroencephalogram but also in heart rhythms and breathing patterns. Therefore, we hypothesize that it is possible to accurately stage sleep based on the electrocardiogram (ECG) and respiratory signals. Methods: Using a dataset including 8,682 polysomnographs, we develop deep neural networks to stage sleep from ECG and respiratory signals. Five deep neural networks consisting of convolutional networks and long short-term memory networks are trained to stage sleep using heart and breathing, including the timing of R peaks from ECG, abdominal and chest respiratory effort, and the combinations of these signals. Results: ECG in combination with the abdominal respiratory effort achieve the best performance for staging all five sleep stages with a Cohen's kappa of 0.600 (95% confidence interval 0.599 -- 0.602); and 0.762 (0.760 -- 0.763) for discriminating awake vs. rapid eye movement vs. non-rapid eye movement sleep. The performance is better for young participants and for those with a low apnea-hypopnea index, while it is robust for commonly used outpatient medications. Conclusions: Our results validate that ECG and respiratory effort provide substantial information about sleep stages in a large population. It opens new possibilities in sleep research and applications where electroencephalography is not readily available or may be infeasible, such as in critically ill patients.