A Probabilistic Perspective: Bayesian Neural Network for Sleep Apnea Detection

Minhee Kim (), Xin Zan () and Xiaochen Xian ()
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Minhee Kim: University of Florida
Xin Zan: University of Iowa
Xiaochen Xian: Georgia Institute of Technology

A chapter in Handbook of AI and Data Sciences for Sleep Disorders, 2024, pp 183-196 from Springer

Abstract: 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.

Date: 2024
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DOI: 10.1007/978-3-031-68263-6_6

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