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Related Experiment Video

Updated: May 21, 2026

Multi-Modal Home Sleep Monitoring in Older Adults
07:40

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

A Multimodal Learning Framework for Detecting Systemic Hypertension in Sleep Apnea Patients Using ECG and PPG

Hisham ElMoaqet, Rami Janini, Mutaz Ryalat

    IEEE Journal of Biomedical and Health Informatics
    |May 19, 2026
    PubMed
    Summary

    We developed FusionNet, a deep learning model to detect hypertension in obstructive sleep apnea patients using sleep study data. This automated approach integrates multiple signals for improved cardiovascular risk assessment.

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    A Model to Simulate Clinically Relevant Hypoxia in Humans
    09:54

    A Model to Simulate Clinically Relevant Hypoxia in Humans

    Published on: December 22, 2016

    Area of Science:

    • Cardiology
    • Artificial Intelligence
    • Sleep Medicine

    Background:

    • Systemic hypertension (HTN) is a significant comorbidity in obstructive sleep apnea (OSA) patients, often managed separately, hindering comprehensive cardiovascular risk assessment.
    • Current methods lack scalable, automated frameworks for hypertension screening using polysomnography (PSG) data.
    • This gap necessitates innovative approaches for integrated diagnosis and risk stratification.

    Purpose of the Study:

    • To introduce FusionNet, a novel deep learning framework for automated detection of hypertension in OSA patients.
    • To leverage multimodal PSG signals for enhanced cardiovascular risk assessment.
    • To establish a proof-of-concept for a dual diagnostic paradigm integrating sleep and hypertension detection.

    Main Methods:

    • Developed FusionNet, a deep learning architecture employing Multiple Instance Learning (MIL) for temporal dynamics analysis.
    • Integrated electrocardiogram (ECG) and photoplethysmogram (PPG) signals via a gated fusion mechanism.
    • Incorporated frequency-domain heart rate variability (HRV), pulse rate variability (PRV), sleep stages, and apnea events for multimodal analysis.
    • Validated the model using Leave-One-Patient-Out (LOPO) cross-validation on a 33-patient dataset.

    Main Results:

    • FusionNet achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) of 87.34%.
    • The model demonstrated 79.03% accuracy and 81.25% specificity.
    • Performance metrics, including a Matthews Correlation Coefficient (MCC) of 0.5801, indicate a balanced and clinically conservative detection profile.

    Conclusions:

    • FusionNet successfully demonstrates the feasibility of deriving hypertension-related cardiovascular signatures from standard sleep studies (PSG).
    • This automated framework offers a potential pathway for integrated cardiovascular risk assessment in OSA patients.
    • Further validation in larger, independent cohorts is required before clinical implementation.