Analysis of differential photoplethysmography signal patterns in apnea and hypopnea

TL;DR

This study shows that photoplethysmography can detect differences in breathing events during sleep, suggesting potential for wearable diagnostic tools.

Contribution

The novel contribution is identifying differential photoplethysmography signal patterns to distinguish apneas and hypopneas in different sleep postures.

Findings

Photoplethysmography signal characteristics significantly differ between apneas and hypopneas.

A machine learning model achieved an AUC of 0.80 in lateral posture and 0.83 in supine posture for classification.

Discriminative signal features remained consistent across different periods of the night.

Abstract

Objective. Photoplethysmography, a non-invasive optical technique that measures changes in blood volume in the microvascular bed of tissue, offers a promising approach for monitoring physiological changes during sleep. This study evaluates differential photoplethysmography signal patterns that can distinguish between apneas vs hypopneas, which are key features of sleep-related breathing disorders. Approach. We analyzed data from 263 severe (apnea hypopnea index ⩾30) obstructive sleep apnea patients, using recordings from the Multi-Ethnic Study of Atherosclerosis. Over 57 000 respiratory events occurring during stage N2 sleep were included. A machine learning model was trained on 89 features derived from the photoplethysmography signal, using the pyPPG toolbox, to classify: apneas vs hypopneas in the supine and lateral sleep posture, and posture-specific differences for each respiratory event type. Main results. Results showed that photoplethysmography signal characteristics significantly differed between apneas vs hypopneas. The model achieved an area under the receiver operation characteristic curve of 0.80 in the lateral posture and 0.83 in the supine posture. However, classification performance was low when distinguishing between apneas and hypopneas in the lateral vs the supine position with an area under the receiver operation characteristic curve of 0.62 for apneas and 0.64 for hypopneas. The discriminative signal features were consistent across different periods of the night. Significance. These findings indicate that photoplethysmography can detect meaningful differences in sleep-related breathing events and support its potential as a foundation for wearable diagnostic and monitoring tools that are personalized, accessible, and cost-effective.

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