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Sleep Apnea

Sleep Apnea

Sleep apnea is a condition where breathing stops intermittently during sleep, often leading to significant health issues. Each episode can last from 10 to 20 seconds or more and is frequently accompanied by a brief arousal from sleep. This disturbance, largely unnoticed by the individual, can lead to severe daytime fatigue. Commonly, individuals seek help after being informed by their partners about loud snoring and noticeable breathing pauses during sleep.
The condition is more prevalent among...

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Related Experiment Video

Updated: Mar 29, 2026

Multi-Modal Home Sleep Monitoring in Older Adults

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

Cross-Modality Transfer Learning from PSG to FMCW Radar for Event-Level Apnea-Hypopnea Segmentation.

Saihu Lu^1,2, Peng Wang¹, Zhenfeng Li¹

¹Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS), Beijing 100094, China.

Bioengineering (Basel, Switzerland)

|March 28, 2026

Summary

This summary is machine-generated.

This study introduces a deep learning framework using radar for sleep apnea detection, overcoming limitations of traditional polysomnography. This non-contact method enables accurate, low-cost, and scalable home monitoring for sleep apnea-hypopnea syndrome.

Keywords:

AHI estimation FMCW radar apnea–hypopnea health care transfer learning

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Area of Science:

Biomedical Engineering
Artificial Intelligence in Healthcare
Sleep Medicine

Background:

Sleep apnea-hypopnea syndrome (SAHS) presents significant cardiovascular and neurocognitive risks.
Polysomnography (PSG) is the gold standard for SAHS diagnosis but is costly and inaccessible for home monitoring.
Frequency-modulated continuous-wave (FMCW) radar offers non-contact respiration sensing but faces challenges in event detection due to limited data and domain shifts.

Purpose of the Study:

To develop a deep learning framework for accurate sleep apnea event detection using FMCW radar.
To enable scalable and longitudinal home monitoring for SAHS.
To leverage transfer learning from PSG data to improve radar-based detection.

Main Methods:

A 1D U-Net segmentation backbone with multi-head self-attention (MHSA) was employed.
Cross-modality transfer learning involved pre-training on PSG data and fine-tuning on radar data.
Temporal post-processing refined event probabilities for apnea-hypopnea index (AHI) estimation.

Main Results:

The framework achieved high performance in radar-based event detection.
Key metrics included precision (0.8137±0.0332), recall (0.8369±0.0470), and F1-score (0.8167±0.0052).
Demonstrated feasibility of PSG-to-radar transfer learning for SAHS screening.

Conclusions:

The proposed deep learning approach enables accurate, low-cost, non-contact sleep apnea screening.
This technology supports scalable, longitudinal monitoring in home settings.
Transfer learning enhances the utility of radar for sleep disorder diagnostics.