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

TITLE=Phenotype-Based and Self-Learning Inter-Individual Sleep Apnea Screening With a Level IV-Like Monitoring System

JOURNAL=Frontiers in Physiology

VOLUME=9

YEAR=2018

URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2018.00723

DOI=10.3389/fphys.2018.00723

ISSN=1664-042X

ABSTRACT=<p><bold>Purpose:</bold> 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.</p><p><bold>Methods:</bold> 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.</p><p><bold>Results:</bold> 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.</p><p><bold>Conclusion:</bold> The results confirm the hypothesis and show that the proposed algorithm has potential to screen patients with SAS.</p>