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Updated: Jun 27, 2026

Multi-Modal Home Sleep Monitoring in Older Adults

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

Sleep Stage Classification During CPAP Therapy from CPAP-Airflow and Wearable Fingertip Signals.

Hsin-Yu Chen¹, Aatif Husain², Andrey V Zinchuk³

¹PranaQ Pte. Ltd., Singapore 018937, Singapore.

Sensors (Basel, Switzerland)

|June 26, 2026

Summary

Combining Continuous Positive Airway Pressure (CPAP) airflow and photoplethysmography (PPG) signals improves sleep stage detection accuracy. This fusion enhances sleep monitoring and auto-CPAP titration for obstructive sleep apnea-hypopnea syndrome (OSAHS) patients.

Keywords:

continuous positive airway pressure convolutional neural network deep learning home sleep monitoring photoplethysmography probabilistic ensembling sleep stage classification

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Area of Science:

Biomedical Engineering
Sleep Medicine
Artificial Intelligence

Background:

Continuous Positive Airway Pressure (CPAP) is the standard treatment for obstructive sleep apnea-hypopnea syndrome (OSAHS).
Photoplethysmography (PPG) sensors in wearable devices are used for home sleep apnea testing.
Both airflow and PPG signals contain rich physiological data for sleep analysis.

Purpose of the Study:

To develop a method for efficiently estimating sleep dynamics in patients undergoing CPAP treatment.
To enhance sleep stage detection accuracy by combining CPAP airflow and PPG signals.
To improve the precision of sleep monitoring and CPAP titration.

Main Methods:

One-dimensional convolutional neural networks were trained separately for CPAP-airflow and PPG signals.
A probabilistic ensembling method (late-fusion soft-voting) was used to fuse the outputs of the individual models.
The fused model predicted sleep stages based on synchronized softmax probability vectors.

Main Results:

The ensembled model achieved a higher overall Cohen's kappa score (0.587) compared to PPG-only (0.511) and CPAP-airflow-only (0.452) models for three-stage classification.
The F1-score for REM sleep detection improved to 0.706 with the ensemble method.
A deep sleep sensitivity of 0.596 was achieved in four-stage classification using probabilistic ensembling.

Conclusions:

Fusion of CPAP and PPG data enhances sleep stage detection accuracy and precision of sleep monitoring.
The improved REM identification is particularly significant for clinical applications.
The algorithm can optimize in-home auto-CPAP titration by addressing REM-related respiratory instability in OSAHS patients.