Phenotype-based and self-learning inter-individual sleep apnea screening with a level IV-like monitoring system

Published

Journal Article

© 2018 Wu, Wu, Huang, Lin, Wang, Huang and Lo. Purpose: We propose a phenotype-based artificial intelligence system that can self-learn and is accurate for screening purposes and test it on a Level IV-like monitoring system. Methods: Based on the physiological knowledge, we hypothesize that the phenotype information will allow us to find subjects from a well-annotated database that share similar sleep apnea patterns. Therefore, for a new-arriving subject, we can establish a prediction model from the existing database that is adaptive to the subject. We test the proposed algorithm on a database consisting of 62 subjects with the signals recorded from a Level IV-like wearable device measuring the thoracic and abdominal movements and the SpO2. Results: With the leave-one-subject-out cross validation, the accuracy of the proposed algorithm to screen subjects with an apnea-hypopnea index greater or equal to 15 is 93.6%, the positive likelihood ratio is 6.8, and the negative likelihood ratio is 0.03. Conclusion: The results confirm the hypothesis and show that the proposed algorithm has potential to screen patients with SAS.

Full Text

Duke Authors

Cited Authors

  • Wu, HT; Wu, JC; Huang, PC; Lin, TY; Wang, TY; Huang, YH; Lo, YL

Published Date

  • July 2, 2018

Published In

Volume / Issue

  • 9 / JUL

Electronic International Standard Serial Number (EISSN)

  • 1664-042X

Digital Object Identifier (DOI)

  • 10.3389/fphys.2018.00723

Citation Source

  • Scopus