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Drug-Induced Sleep Endoscopy DISE with Target Controlled Infusion TCI and Bispectral Analysis in Obstructive Sleep Apnea
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Multimodal Sleep Apnea Detection with Missing or Noisy Modalities.

Hamed Fayyaz1, Niharika S D'Souza2, Rahmatollah Beheshti1

  • 1University of Delaware.

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|March 11, 2025
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Summary
This summary is machine-generated.

This study introduces a new method to detect sleep apnea, even with missing or noisy data from sleep studies (polysomnography). The model maintains high accuracy, improving sleep apnea diagnosis in challenging clinical settings.

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Area of Science:

  • Medical Informatics
  • Biomedical Signal Processing
  • Machine Learning in Healthcare

Background:

  • Polysomnography (PSG) is crucial for sleep staging and respiratory event detection.
  • Conventional methods struggle with incomplete or noisy physiological signals in real-world PSG data.
  • Missing or corrupted data is a significant challenge in clinical sleep studies.

Purpose of the Study:

  • To develop a robust pipeline for sleep apnea detection that compensates for missing or noisy modalities.
  • To create a model adaptable to any combination of available physiological signals from PSG.
  • To enhance the reliability of sleep apnea detection in diverse clinical environments.

Main Methods:

  • Proposed a comprehensive machine learning pipeline for sleep apnea detection.
  • Developed a model capable of handling arbitrary subsets of PSG modalities.
  • Validated the model's performance against state-of-the-art approaches using simulated noise and missing data.

Main Results:

  • The proposed model significantly outperforms existing methods in sleep apnea detection.
  • Achieved high performance (AUROC > 0.9) even with substantial data noise and missingness.
  • Demonstrated consistent accuracy across various data subsets and noise levels.

Conclusions:

  • The developed pipeline effectively addresses the challenge of missing or noisy modalities in PSG.
  • The model offers a reliable solution for sleep apnea detection, particularly in difficult scenarios like pediatric or remote monitoring.
  • This approach has the potential to improve diagnostic accuracy in real-world clinical applications.