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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

Asymptomatic heart failure, a target for early intervention per guideline staging, is frequently missed without systematic screening. We aimed to develop a machine learning model based on smartwatch monitoring technology to screen individuals for cardiac dysfunction risk. In this prospective cohort...

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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