Development and Validation of a Multimodal Deep Learning Model Using Peripheral Oxygen Saturation and Ballistocardiography Signals for Predicting Apnea-Hypopnea Index in Obstructive Sleep Apnea
A Prospective Observational Study to Develop and Validate a Multimodal Deep Learning Model Integrating Peripheral Oxygen Saturation and Ballistocardiography Signals for the Prediction of Apnea-Hypopnea Index in Patients With Suspected Obstructive Sleep Apnea
Obstructive sleep apnea (OSA) is a common sleep disorder associated with cardiovascular, metabolic, and neurocognitive complications. The standard diagnostic test for OSA is in-laboratory polysomnography (PSG), which can be costly, labor-intensive, and inconvenient for repeated or large-scale screening. Therefore, there is a need for a convenient and non-invasive method to help identify individuals who may be at risk for OSA.
This prospective observational study aims to develop and clinically validate a multimodal deep learning model that integrates peripheral oxygen saturation (SpO₂) signals and ballistocardiography (BCG) data collected using a smart mattress system to estimate the apnea-hypopnea index (AHI). Adult participants undergoing routine overnight PSG for suspected OSA will be enrolled. During sleep, synchronized SpO₂ and BCG signals will be recorded non-invasively.
The performance of the deep learning model will be evaluated by comparing the predicted AHI values with AHI values derived from PSG. This study seeks to evaluate whether this practical, non-invasive, and scalable approach may support early identification of OSA risk and improve access to sleep disorder assessment in both clinical and home-based settings.
Přehled studie
Postavení
Postavení
Podmínky
Podmínky
Detailní popis
This is a prospective observational cohort study designed to develop and validate a multimodal deep learning-based model for estimating obstructive sleep apnea (OSA) severity. Adult participants with suspected OSA who are scheduled to undergo overnight in-laboratory polysomnography (PSG) will be recruited at Fu Jen Catholic University Hospital.
During the PSG examination, participants will simultaneously undergo non-invasive physiological signal acquisition using a smart mattress system equipped with ballistocardiography (BCG) sensors, along with continuous peripheral oxygen saturation (SpO₂) monitoring. These signals will be time-synchronized with PSG recordings. No experimental intervention or alteration to standard clinical care will be performed.
Multimodal deep learning algorithms incorporating convolutional neural networks and sequence-based architectures will be developed to analyze SpO₂ and BCG signals and to estimate the apnea-hypopnea index (AHI). Model performance will be assessed by comparing predicted AHI values with PSG-derived AHI using predefined statistical metrics.
The primary objective is to evaluate the accuracy of AHI prediction using the proposed multimodal model. Secondary objectives include assessment of model discrimination for OSA severity classification and exploratory analyses of physiological signal features associated with sleep-disordered breathing. All analyses will be conducted using de-identified data. This study does not involve U.S. FDA-regulated drugs or devices and is not conducted under an investigational new drug application or investigational device exemption.
Typ studie
Typ studie
Zápis (Odhadovaný)
Zápis
Kontakty a umístění
Studijní kontakt
Studijní kontakt
- Jméno: Ke-Yun Chao Principal Investigator
- Telefonní číslo: +886-905-301-879
- E-mail: C00152@mail.fjuh.fju.edu.tw
Studijní místa
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New Taipei City
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New Taipei City, New Taipei City, Tchaj-wan, 24352
- Nábor
- Fu Jen Catholic University Hospital
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Kontakt:
- Clinical Research Coordinator
- Telefonní číslo: +886-2-2905-2000
- E-mail: Ck_qq@hotmail.com
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Kritéria účasti
Kritéria způsobilosti
Kritéria způsobilosti
Věk způsobilý ke studiu
Přijímá zdravé dobrovolníky
Pohlaví způsobilá ke studiu
Metoda odběru vzorků
Studijní populace
Adult individuals aged 20 years or older who are referred to Fu Jen Catholic University Hospital for in-laboratory polysomnography due to suspected obstructive sleep apnea will be invited to participate. The study population includes participants with a wide range of apnea-hypopnea index values, including individuals without obstructive sleep apnea based on polysomnography results.
Popis
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.
Studijní plán
Jak je studie koncipována?
Detaily designu
- Observační modely: COHORT
- Časové perspektivy: PROSPECTIVE
Počet skupin / kohort
Kohorty a intervence
Skupina / kohortaSkupina / kohorta |
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Adults With Suspected Obstructive Sleep Apnea
This cohort includes adult participants with suspected obstructive sleep apnea who are scheduled to undergo overnight in-laboratory polysomnography. All participants will receive standard clinical polysomnography while simultaneously undergoing non-invasive physiological signal acquisition using a smart mattress system and peripheral oxygen saturation monitoring. No experimental intervention or treatment assignment is performed. |
Co je měření studie?
Primární výstupní opatření
Primární výstupní opatření
Měření výsledku |
Popis opatření |
Časové okno |
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Mean absolute error of apnea-hypopnea index estimation
Časové okno: Two overnight assessments per participant: one in-laboratory polysomnography night and one subsequent home sleep monitoring night.
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Mean absolute error (MAE) will be calculated as the average absolute difference between the apnea-hypopnea index (AHI) predicted by the multimodal deep learning model using peripheral oxygen saturation (SpO2) and ballistocardiography (BCG) signals collected during home sleep monitoring and the AHI measured by in-laboratory polysomnography (PSG). The analysis will include participants with evaluable paired PSG and home monitoring data. MAE will be reported in events per hour, with lower values indicating better predictive performance. |
Two overnight assessments per participant: one in-laboratory polysomnography night and one subsequent home sleep monitoring night.
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Spolupracovníci a vyšetřovatelé
Sponzor
Sponzor
Spolupracovníci
Spolupracovníci
Termíny studijních záznamů
Hlavní termíny studia
Začátek studia (Aktuální)
Začátek studia
Primární dokončení (Odhadovaný)
Primární dokončení
Dokončení studie (Odhadovaný)
Dokončení studie
Termíny zápisu do studia
První předloženo
První předloženo
První předloženo, které splnilo kritéria kontroly kvality
První předloženo, které splnilo kritéria kontroly kvality
První zveřejněno (Aktuální)
První zveřejněno
Aktualizace studijních záznamů
Poslední zveřejněná aktualizace (Aktuální)
Poslední zveřejněná aktualizace
Odeslaná poslední aktualizace, která splnila kritéria kontroly kvality
Odeslaná poslední aktualizace, která splnila kritéria kontroly kvality
Naposledy ověřeno
Naposledy ověřeno
Více informací
Termíny související s touto studií
Klíčová slova
Další relevantní podmínky MeSH
Další identifikační čísla studie
Další identifikační čísla studie
- FJUH115580
Plán pro data jednotlivých účastníků (IPD)
Plánujete sdílet data jednotlivých účastníků (IPD)?
Popis plánu IPD
Individual participant data will not be shared publicly.
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Studuje produkt zařízení regulovaný americkým úřadem FDA
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