Multimodal Deep Learning Model for Predicting the Apnea-Hypopnea Index in Obstructive Sleep
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: electronic stethoscope, fingertip pulse oximeter, pressure-sensing mattresses.
- Who it may be relevant to
- Registry conditions: Obstructive Sleep Apnea (OSA), Polysomnography. Basic parameters: 30 years — 75 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Taiwan
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
A Multisensor Deep Neural Framework Combining Digital Auscultation, Oxygen Saturation, and Motion Data to Estimate the Apnea-Hypopnea Index in Obstructive Sleep Apnea
Overview
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.
Detailed description
Obstructive sleep apnea is a common sleep disorder closely associated with cardiovascular, metabolic, and neuropsychiatric comorbidities. It is characterized by repeated upper airway collapse during sleep, leading to intermittent hypoxia and sleep fragmentation. Although polysomnography remains the diagnostic gold standard for obstructive sleep apnea, its high cost, complexity, and limited accessibility pose challenges for large-scale screening and early identification. Recent advancements in noninvasive sensing technologies-such as electronic stethoscopes, wearable oximeters, and under-mattress pressure sensors-have enabled low-burden physiological monitoring solutions, offering new opportunities for simplified obstructive sleep apnea detection. In this study, synchronized multimodal physiological data will be collected during overnight sleep, including respiratory sounds, continuous saturation measurements, and standard polysomnography waveforms. Signal preprocessing and feature extraction will be performed to ensure data quality and temporal alignment. A deep learning model will be developed using these multimodal signals as inputs. The apnea-hypopnea index will be derived from overnight polysomnography. The model will be trained to estimate apnea-hypopnea index values and classify obstructive sleep apnea severity according to established clinical thresholds.
Interventions
- Device electronic stethoscope
digital device amplifying and recording cardiopulmonary sounds - Device fingertip pulse oximeter
a small device placed on the finger to measure blood oxygen saturation (SpO₂) and pulse rate noninvasively. - Device pressure-sensing mattresses
using ballistocardiography (BCG) for monitoring respiration and heart rate
Primary outcome measures
- apnea-hypopnea index, sound waveforms, and the correlation between apnea-hypopnea index and ballistocardiography waveforms [Time frame: one night]
Eligibility criteria
Inclusion criteria
- age 30-75 years
- clinically suspected obstructive sleep apnea and scheduled for polysomnography
- willing and able to provide written informed consent
Exclusion criteria
- intolerance to the electronic stethoscope or fingertip pulse oximeter
- significant structural airway abnormalities
- arrhythmia
- neuromuscular disorders
- pregnancy
- hospitalization within the past 1 month
- inability to provide informed consent or requiring legal guardian consent
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
Taiwan · 1 center
- Fu Jen Catholic University Hospital, Fu Jen Catholic University — New Taipei City
Identifiers
NCT: NCT07447999 · FJUH114486