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

Hau Tieng Wu, Jhao Cheng Wu, Po Chiun Huang, Ting Yu Lin, Tsai Yu Wang, Yuan Hao Huang, Yu Lun Lo

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number723
JournalFrontiers in Physiology
Issue numberJUL
StatePublished - Jul 2 2018


  • Inter-individual prediction
  • Level IV-like monitoring
  • Phenotype metric
  • Self-learning AI system
  • Sleep apnea screening

ASJC Scopus subject areas

  • Physiology
  • Physiology (medical)


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