This study aims to develop a multimodal deep learning model that integrates noninvasive signals to predict the severity of obstructive sleep apnea. By establishing a clinically viable and user-friendly monitoring tool, the study seeks to enhance early screening accessibility and support the development of home-based sleep care systems.
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AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
apnea-hypopnea index, sound waveforms, and the correlation between apnea-hypopnea index and ballistocardiography waveforms
Timeframe: one night