From Screening at Clinic to Diagnosis at Home: How AI/ML/DL Algorithms Are Transforming Sleep Apnea Detection

Pei-Lin Lee (), Wenbo Gu (), Wen-Chi Huang () and Ambrose A. Chiang ()
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Pei-Lin Lee: National Taiwan University Hospital
Wenbo Gu: National Yang Ming Chiao Tung University
Wen-Chi Huang: FTSE International Taiwan Ltd
Ambrose A. Chiang: Sleep Medicine Section, Louis Stokes Cleveland VA Medical Center

A chapter in Handbook of AI and Data Sciences for Sleep Disorders, 2024, pp 109-160 from Springer

Abstract: Abstract The realm of sleep medicine is experiencing a rapid evolution driven by advancements in sleep technologies. Emerging devices for obstructive sleep apnea (OSA) detection are becoming increasingly sophisticated and portable, due to the integration of innovative miniaturized sensor designs, advanced processing techniques, and artificial intelligence/machine learning/deep learning (AI/ML/DL) algorithms. AI models have become ubiquitous in sleep medicine, fundamentally altering the approach to OSA detection from screening at the clinic to at-home diagnosis utilizing cutting-edge sleep technologies. This chapter delves into ML models that leverage clinical features for OSA screening and illustrates their use with a case study. We also explore the current landscape of innovative AI/ML/DL models employing photoplethysmography and accelerometry for at-home OSA diagnosis. Finally, we offer insights on crucial considerations for model design, dataset selection, and performance evaluation and emphasize the importance of external testing using independent datasets. As the complexity of physiological signals increases with the data integration from various sensors, more advanced DL techniques might suite better for handling intricate data. This trend highlights a shift beyond traditional ML and basic DL models toward more advanced, customized, and powerful DL approaches.

Keywords: Artificial intelligence (AI); Machine learning (ML); Deep learning (DL); Obstructive sleep apnea (OSA); Polysomnography (PSG); Home sleep apnea testing (HSAT); Accelerometer; Photoplethysmography (PPG); Wearable; Apnea-hypopnea index (AHI); sleep staging (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-68263-6_4

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