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Open access Oct 2026

Dynamic prediction of obstructive sleep apnea progression: a deep learning approach using time-varying smartwatch data

Obstructive sleep apnea (OSA) was prevalent yet underdiagnosed, contributing to significant cardiovascular morbidity. Early identification of individuals progressing from moderate- to high-risk OSA was critical for timely intervention. To develop and validate deep learning-based survival models predicting...

Y. Guo, Ci-Zhong Li · 0 citations
Open access Oct 2026

Smartwatch-based machine learning for the detection of cardiac dysfunction

The cardiac dysfunction risk model demonstrated excellent discriminative ability and good calibration across internal and external cohorts, highlighting its potential to facilitate guideline-recommended early detection of asymptomatic heart failure, although further prospective validation in broader populations is warr...

Y. Guo, Y.-Y. Cai, H. Zhang · 0 citations
Open access Oct 2026

Photoplethysmography-based smartwatches for atrial fibrillation detection in stroke survivors: The Liverpool-Huawei Stroke Study

Detecting atrial fibrillation (AF) in stroke survivors is critical for preventing recurrent stroke. However, the diagnostic accuracy of photoplethysmography (PPG)-based smartwatches in this high-risk population remains uncertain. The Liverpool-Huawei Stroke Study evaluated the performance of Huawei smartbands for...

S. Lam, S. Harrison, Y. Zheng et al. · 0 citations

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