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Deep Learning of Snoring to Predict Collapse Site, Treatment Outcomes, and its Clinical Translation in Obstructive Sleep Apnea

Award Number: R21HL188520
ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/01/2026
PERIOD OF PERFORMANCE END DATE: 08/31/2028

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Deep Learning of Snoring to Predict Collapse Site, Treatment Outcomes, and its Clinical Translation in Obstructive Sleep Apnea - ABSTRACT: Continuous Positive Airway Pressure (CPAP) is an effective treatment for OSA, in which the patient must wear an oxygen mask every night. Due to discomfort, only 30–50% of patients can tolerate CPAP. Alternatives to CPAP are available, such as mandibular advancement devices (MAD), hypoglossal nerve stimulation (HGNS), pharyngeal surgery, and positional therapy. The success of these treatments often depends on the site of UA obstruction. For example, HGNS therapy electrically activates tongue muscles. The eligibility to get HGNS treatment requires ruling out of collapse of the soft palate, as patients with this collapse pattern respond poorly to HGNS. Thus, it is clear that treatments are highly site-of-collapse specific. The primary site of collapse is categorized using the VOTE classification system, which denotes the velum (V), lateral pharyngeal walls (O), tongue base (T), and epiglottis (E). Drug-Induced Sleep Endoscopy (DISE) is the gold standard for determining VOTE, where an endoscope is used while the patient is under sedation to visualize where the collapse occurs. Due to its invasiveness and high cost, DISE is unavailable to most patients. Therefore, there is an urgent need for a widely accessible, non-invasive biomarker that can phenotype the airway and guide personalized therapy. Snoring can serve as such a biomarker, as the acoustic features of snores can reflect the structures that vibrate during UA obstruction. The central hypothesis of this project is that deep learning models can extract these acoustic signatures from snoring to identify the anatomical site of collapse and predict treatment outcomes. This work represents a secondary analysis of existing, clinically collected datasets that include synchronized snoring, DISE, and sleep-recording data. We propose a three-part strategy to test this hypothesis. Aim 1 will develop deep learning architectures that combine handcrafted acoustic features, self-supervised speech embeddings, and spectrogram-based models to classify the site of collapse from DISE snoring recordings. Aim 2 will extend the framework to predict HGNS treatment response using pre-treatment snore recordings. Aim 3 will systematically evaluate how much snore sampling frequency is needed for reliable classification and validate the models using routine polysomnography (PSG) snore sensors. Successful completion of this project will establish a scalable, non-invasive snore-based biomarker for identifying UA collapse sites and predicting treatment responsiveness.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $235,520 )
2026	2026	KENNESAW STATE UNIVERSITY	1000 CHASTAIN RD NW	KENNESAW	GA	30144	COBB	USA	National Center on Sleep Disorders Research	000	1	9/2/2026	NEW	$235,520
														Subtotal = $235,520

Grand Total All Awards = $235,520

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Deep Learning of Snoring to Predict Collapse Site, Treatment Outcomes, and its Clinical Translation in Obstructive Sleep Apnea

Award Number: R21HL188520

ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 09/01/2026

PERIOD OF PERFORMANCE END DATE: 08/31/2028

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