Oxygen Saturation Sample Entropy, a Novel Diagnostic Tool in Sleep Disordered Breathing

TL;DR

This study introduces a new method using oxygen saturation signal complexity to diagnose sleep apnea and differentiate types of sleep disordered breathing.

Contribution

The novel use of Sample Entropy and Transfer Entropy in analyzing oxygen saturation data for sleep disorder classification.

Findings

Oxygen saturation Sample Entropy significantly differs in sleep disordered breathing versus normal cases.

High sensitivity (100%) and moderate specificity (60%) for diagnosing obstructive sleep apnea using oxygen saturation Sample Entropy.

Transfer Entropy patterns show distinct directional relationships between heart rate, respiratory rate, nasal flow, and oxygen saturation in sleep apnea patients.

Abstract

To assess whether a Network Physiology approach using Sample Entropy and Transfer Entropy can be applied to simple physiological signals obtained during sleep studies, to accurately distinguish between different types of sleep disordered breathing (SDB) and streamline the diagnostic process.

Retrospective study on patients who underwent a sleep study between January 2017 and December 2021. The training dataset, used for algorithm development, included four clinically important groups: normal, obstructive sleep apnoea alone, sustained nocturnal hypoxemia with a high AHI (≥ 30/hr) and sustained nocturnal hypoxemia with a low AHI (< 30/hr). Mean, standard deviation, Sample Entropy and Transfer Entropy was calculated for heart rate, respiratory rate, oxygen saturation and nasal flow for each patient. Sample entropy is a measure of signal complexity. This was validated in a separate test dataset. ROC analysis was used.

In the training dataset (n = 105), the Sample Entropy of the oxygen saturation signal was significantly different in patients with SDB compared to normal studies. The area under a ROC curve for predicting obstructive sleep apnoea from normal studies and sustained hypoxia with a high AHI (≥ 30events/hr) compared to sustained hypoxia with a low AHI (AHI < 30events/hr) was 0.943 and 0.785 respectively. When tested in the test dataset (n = 80), oxygen saturation Sample Entropy above 0.1456 was 100% sensitive and 60% specific in diagnosing obstructive sleep apnoea. Patients with OSA had significantly increased Transfer Entropy from HR → SpO2, RR → SpO2 and NF → SpO2; and significantly decreased Transfer Entropy from SpO2 → RR.

Network Physiology mapping of oxygen saturation can help distinguish between different types of sleep disordered breathing and has the potential to support simplified global diagnostic pathways for sleep apnoea utilising oximetry alone.

The online version contains supplementary material available at 10.1007/s00408-025-00864-w.

Figures