# Towards automatic home screening of obstructive sleep apnea using combined features from pulse wave amplitude, pulse-to-pulse interval and oxygen desaturation

**Authors:** Wanwara Thuptimdang, Krongthong Tawaranurak, Pattaraporn Panyarath, Wandee Rakim, Katinee Wae-asae, Michael C. K. Khoo

PMC · DOI: 10.1007/s11325-026-03592-4 · Sleep & Breathing = Schlaf & Atmung · 2026-02-19

## TL;DR

This study explores using pulse wave amplitude and pulse intervals from pulse oximeters to improve home screening for obstructive sleep apnea, finding that combining these features with oxygen saturation improves detection accuracy.

## Contribution

The novel contribution is evaluating the added value of pulse wave amplitude features in automated obstructive sleep apnea screening, alongside pulse intervals and oxygen saturation.

## Key findings

- Adding pulse wave amplitude to oxygen saturation improved sensitivity for arousal-related OSA segments and per-subject detection.
- Combining pulse wave amplitude with pulse intervals achieved the highest sensitivity for arousal-related segments.
- Pulse intervals outperformed pulse wave amplitude, but both improved detection of subjects with AHI ≥ 15 when combined with oxygen saturation.

## Abstract

Automatic home OSA screening with pulse oximeters often relies on oxygen saturation (SpO2), which may miss hypopneas associated with arousals. Photoplethysmography (PPG) from pulse oximeters can be processed to extract pulse wave amplitude (PWA) and pulse-to-pulse interval (PPI) which reflect autonomic activation during arousals. However, the role of PWA in automated OSA screening remains unexplored. This study evaluated the added value of PWA for detecting OSA segments, and estimating AHI compared with SpO2 and PPI.

From 90 PSG recordings, PWA and PPI were derived from finger PPG. We extracted statistical PWA features to capture amplitude drops, and spectral powers to represent pulse amplitude variability. PPI features included statistics and heart rate variability measures. Support vector machine classifiers with different combinations of PWA, PPI, and SpO2 features were trained to detect 60-second segments with apnea or hypopnea. Performance was assessed at both per-segment and per-subject levels for identifying AHI ≥ 15.

Adding PWA to SpO2 improved sensitivity for arousal-related OSA segments from 61.6% to 65.2% and increased per-subject sensitivity from 61.4% to 64.9%. Adding PPI to SpO2 improved per-segment sensitivity for arousal-related OSA segments to 73.3% and increased per-subject sensitivity to 77.2%. Combining PWA with PPI achieved the highest sensitivity for arousal-related segments (77.1%), and when both were added to SpO2, per-subject sensitivity for detecting AHI ≥ 15 reached 80.7%.

Both PWA and PPI improved detection of arousal-related segments and contributed to detecting subjects with AHI ≥ 15. However, PPI consistently outperformed PWA. SpO2 remained particularly important for identifying subjects with AHI < 15.

## Linked entities

- **Diseases:** obstructive sleep apnea (MONDO:0007147)

## Full-text entities

- **Diseases:** neurocognitive impairment (MESH:D019965), cardiovascular diseases (MESH:D002318), diabetes (MESH:D003920), dyslipidemia (MESH:D050171), sleep-related disorders (MESH:D012893), Hypertension (MESH:D006973), Apnea (MESH:D001049), upper (MESH:D012141), excessive daytime sleepiness (MESH:D006970), hypoxemia (MESH:D000860), metabolic dysfunction (MESH:D008659), hypopnea (MESH:D012891), overweight (MESH:D050177), coronary artery disease (MESH:D003324), stroke (MESH:D020521), AHI (MESH:D020181), heart failure (MESH:D006333), cardiac arrhythmias (MESH:D001145)
- **Chemicals:** oxygen (MESH:D010100)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

4 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12920278/full.md

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Source: https://tomesphere.com/paper/PMC12920278