Quantifying sleep apnea heterogeneity using hierarchical Bayesian modeling
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866910005142224896 |
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| author | Palmer, Glenn Srivali, Narat Dunson, David B. |
| author_facet | Palmer, Glenn Srivali, Narat Dunson, David B. |
| contents | Obstructive Sleep Apnea (OSA) is a breathing disorder during sleep that affects millions of people worldwide. The diagnosis of OSA often occurs through an overnight polysomnogram (PSG) sleep study that generates a massive amount of physiological data. However, despite the evidence of substantial heterogeneity in the expression and symptoms of OSA, diagnosis and scientific analysis of severity typically focus on a single summary statistic, the Apnea-Hypopnea Index (AHI). We address the limitations of this approach through hierarchical Bayesian modeling of PSG data. Our approach produces interpretable random effects for each patient, which govern sleep-stage dynamics, rates of OSA events, and impacts of OSA events on subsequent sleep-stage dynamics. We propose a novel approach for using these random effects to produce a Bayes optimal clustering of patients. We use the proposed approach to analyze data from the APPLES study. Our analysis produces clinically interesting groups of patients with sleep apnea and a novel finding of an association between OSA expression and cognitive performance that is missed by an AHI-based analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11599 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantifying sleep apnea heterogeneity using hierarchical Bayesian modeling Palmer, Glenn Srivali, Narat Dunson, David B. Applications Obstructive Sleep Apnea (OSA) is a breathing disorder during sleep that affects millions of people worldwide. The diagnosis of OSA often occurs through an overnight polysomnogram (PSG) sleep study that generates a massive amount of physiological data. However, despite the evidence of substantial heterogeneity in the expression and symptoms of OSA, diagnosis and scientific analysis of severity typically focus on a single summary statistic, the Apnea-Hypopnea Index (AHI). We address the limitations of this approach through hierarchical Bayesian modeling of PSG data. Our approach produces interpretable random effects for each patient, which govern sleep-stage dynamics, rates of OSA events, and impacts of OSA events on subsequent sleep-stage dynamics. We propose a novel approach for using these random effects to produce a Bayes optimal clustering of patients. We use the proposed approach to analyze data from the APPLES study. Our analysis produces clinically interesting groups of patients with sleep apnea and a novel finding of an association between OSA expression and cognitive performance that is missed by an AHI-based analysis. |
| title | Quantifying sleep apnea heterogeneity using hierarchical Bayesian modeling |
| topic | Applications |
| url | https://arxiv.org/abs/2503.11599 |