Multimodal Deep Learning to Predict Sleep Apnea Severity from SpO2 and Ballistocardiography

Is this trial a fit for you?

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

What this trial studies

This prospective observational study develops and validates a multimodal deep learning model that combines peripheral oxygen saturation and ballistocardiography signals collected from a smart mattress to estimate the apnea-hypopnea index. Adult participants referred for in-lab polysomnography with suspected obstructive sleep apnea will have synchronized SpO2 and BCG data recorded during sleep. The model’s predicted AHI will be compared with PSG-derived AHI using predefined metrics, with secondary analyses on OSA severity discrimination and signal feature associations. Data will be de-identified, and no changes to standard clinical care will be made. The study is led by Fu Jen Catholic University and conducted at its hospital site.

Why it matters

Who can join: the full rules

Inclusion Criteria:

* Adults aged 20 years or older.
* Individuals referred for in-laboratory polysomnography due to suspected obstructive sleep apnea.
* Able and willing to provide informed consent.

Exclusion Criteria:

* Inability to provide informed consent.
* Presence of conditions that may interfere with sleep signal acquisition, as determined by the investigator.
* Prior diagnosis of sleep disorders other than obstructive sleep apnea that could confound study results.

Where it's running

• Fu Jen Catholic University Hospital
  New Taipei City, New Taipei City, Taiwan Recruiting

Study contacts

• Study coordinator: Ke-Yun Chao Principal Investigator
• Email: C00152@mail.fjuh.fju.edu.tw
• Phone: +886-905-301-879

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How to take part

1. Review the eligibility criteria above with your treating physician.
2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.