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Purpose: We propose a phenotype-based artificial intelligence system that canself-learn and is accurate for screening purposes, and test it on a Level IVmonitoring system. Methods: Based on the physiological knowledge, wehypothesize that the phenotype information will allow us to find subjects froma well-annotated database that share similar sleep apnea patterns. Therefore,for a new-arriving subject, we can establish a prediction model from theexisting database that is adaptive to the subject. We test the proposedalgorithm on a database consisting of 62 subjects with the signals recordedfrom a Level IV wearable device measuring the thoracic and abdominal movementsand the SpO2. Results: With the leave-one cross validation, the accuracy of theproposed algorithm to screen subjects with an apnea-hypopnea index greater orequal to 15 is 93.6%, the positive likelihood ratio is 6.8, and the negativelikelihood ratio is 0.03. Conclusion: The results confirm the hypothesis andshow that the proposed algorithm has great potential to screen patients withSAS.

Phenotype-based and Self-learning Inter-individual Sleep Apnea Screening with a Level IV Monitoring System