IDEAS home Printed from https://ideas.repec.org/h/spr/spochp/978-3-031-68263-6_6.html
   My bibliography  Save this book chapter

A Probabilistic Perspective: Bayesian Neural Network for Sleep Apnea Detection

In: Handbook of AI and Data Sciences for Sleep Disorders

Author

Listed:
  • Minhee Kim

    (University of Florida)

  • Xin Zan

    (University of Iowa)

  • Xiaochen Xian

    (Georgia Institute of Technology)

Abstract

This chapter explores the application of Bayesian neural networks (BNNs) in the detection of sleep apnea, a prevalent sleep disorder with significant health implications. Most of the existing neural network-based approaches in sleep apnea diagnostics have predominantly focused on developing or applying various neural network designs. However, this chapter introduces a paradigm shift by emphasizing the importance of a probabilistic perspective in neural network-based sleep apnea diagnostics. We explore how BNNs, with their inherent capacity to handle uncertainty and learn effectively from limited data, offer a significant advancement over conventional deterministic models. Through comparative studies between Bayesian and conventional neural networks, we demonstrate the superior performance of BNNs in accurately predicting the severity of sleep apnea. Our findings suggest a promising future for BNNs in sleep apnea detection, potentially leading to more accurate, accessible, and efficient diagnostic tools.

Suggested Citation

  • Minhee Kim & Xin Zan & Xiaochen Xian, 2024. "A Probabilistic Perspective: Bayesian Neural Network for Sleep Apnea Detection," Springer Optimization and Its Applications, in: Richard B. Berry & Panos M. Pardalos & Xiaochen Xian (ed.), Handbook of AI and Data Sciences for Sleep Disorders, pages 183-196, Springer.
    • Handle: RePEc:spr:spochp:978-3-031-68263-6_6
    DOI: 10.1007/978-3-031-68263-6_6
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:spochp:978-3-031-68263-6_6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.