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bioRxiv · 10.1101/2025.07.03.663061

Deep Learning Meets Sleep Medicine: A Proof-of-Concept Clustering of Minute-Resolution CPAP Telemetry

Calbert, L.·Scharf, M. T.·Androulakis, I. P.
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Abstract

This proof-of-concept study demonstrates that minute-resolution telemetry from continuous positive airway pressure (CPAP) devices can be effectively repurposed for large-scale chronotype and adherence phenotyping. We collated 30 consecutive nights from n=200 de-identified ResMed patients into 30 x 1440 colour images, embedded each image with a frozen ResNet-50 convolutional neural network, and clustered the embeddings with k -means. Six distinct phenotypes emerged, capturing both sleep timing (early birds, typical sleepers, night owls) and adherence patterns (high, medium-high, inconsistent, fragmented, and non-adherent). The approach leverages routinely collected clinical data without the need for additional sensors, promising significant benefits for personalized sleep medicine. External validation against actigraphy and questionnaire-based chronotype measures is planned to further strengthen these findings.

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Calbert, L., Scharf, M. T., Androulakis, I. P.. 2025-07-08. Deep Learning Meets Sleep Medicine: A Proof-of-Concept Clustering of Minute-Resolution CPAP Telemetry. https://doi.org/10.1101/2025.07.03.663061

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