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Related Experiment Video

Updated: Jul 21, 2025

Multi-Modal Home Sleep Monitoring in Older Adults

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

Prediction of Sleep Apnea Events Using a CNN-Transformer Network and Contactless Breathing Vibration Signals.

Yuhang Chen^1,2, Shuchen Yang³, Huan Li^4,5

¹School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China.

Bioengineering (Basel, Switzerland)

|July 29, 2023

Summary

Accurate prediction of obstructive sleep apnea (OSA) events is crucial for developing new treatments. A novel CNN-transformer model using breathing vibrations achieved 85.9% accuracy, outperforming traditional methods for better OSA management.

Keywords:

CNN contactless monitoring respiratory event prediction transformer

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Last Updated: Jul 21, 2025

Multi-Modal Home Sleep Monitoring in Older Adults

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A Model to Simulate Clinically Relevant Hypoxia in Humans

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Published on: December 22, 2016

Area of Science:

Biomedical Engineering
Sleep Medicine
Artificial Intelligence

Background:

Obstructive sleep apnea (OSA) affects over 425 million people globally.
Accurate prediction of OSA events is vital for treatment development but remains a research gap.
Current prediction methods require further advancement for improved clinical application.

Purpose of the Study:

To develop a novel framework for predicting sleep apnea events.
To leverage low-frequency breathing vibrations for sleep apnea detection.
To enhance the accuracy and efficiency of sleep apnea event prediction using advanced AI.

Main Methods:

Utilized piezoelectric sensors to capture breathing-induced vibrations.
Developed a CNN-transformer network to extract local and global features from vibration signals.
Conducted overnight recordings on 105 subjects for model training and validation.

Main Results:

Achieved 85.9% accuracy and 85.8% F1 score in five-fold cross-validation.
Demonstrated performance improvements of 3.5% (accuracy) and 5.3% (F1 score) over classical models.
Observed 2.3% and 3.8% improvements in leave-one-out cross-validation, highlighting model robustness.

Conclusions:

The proposed CNN-transformer model effectively predicts sleep apnea events.
This vibration-based framework offers a new approach for improving OSA treatment and clinical management.
The study provides a promising direction for non-invasive sleep apnea detection and monitoring.