Context-aware analysis enhances autoscoring accuracy of home sleep apnea testing

Home sleep apnea testing based on peripheral arterial tonometry is increasingly being deployed because of its ability to test for multiple nights. However, home sleep apnea tests based on peripheral arterial tonometry do not have access to modalities such as airflow and cortical arousals and instead rely on alternative sources of information to detect respiratory events. This results in an a priori performance disadvantage. In this study, we describe the Panorama algorithm, which aims to reduce this disadvantage by incorporating information from characteristically repetitive sequences in physiological changes associated with respiratory events. These include changes in peripheral arterial tone, pulse rate, and oxygen saturation. The method was designed to facilitate manual review by providing the scoring rationale for each respiratory event.

The method was developed and evaluated using a dataset of 266 participants from a multicentric cohort suspected of having obstructive sleep apnea. All participants underwent simultaneous polysomnography and home sleep apnea testing based on peripheral arterial tonometry, and all polysomnography data were double-scored. Scoring was performed according to the 3% and 4% rules for hypopnea scoring. Clinical endpoint parameters, including the obstructive sleep apnea severity categorization accuracy and Cohen's kappa, were selected to compare the algorithm to a conventional context-unaware algorithm. Data analysis and reporting followed the TRIPOD+AI reporting guidance for prediction models that use machine learning.

Regarding obstructive sleep apnea severity categorization accuracy, the Panorama algorithm significantly outperformed context-unaware autoscoring by 9% using 3% rule scoring and 7% using 4% rule scoring.

The context-aware method significantly improves the performance of home sleep apnea tests based on peripheral arterial tonometry while still facilitating scoring review by providing event-specific scoring rationale.

Registry: ClinicalTrials.gov; Name: A Validation Study of the NightOwl PAT-based Home Sleep Apnea Test; URL: https://clinicaltrials.gov/ct2/show/NCT04191668; Identifier: NCT04191668.

Massie F, Vits S, Verbraecken J, Bergmann J. Context-aware analysis enhances autoscoring accuracy of home sleep apnea testing. J Clin Sleep Med. 2025;21(5):789-804.