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 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 wearable device measuring the thoracic and abdominal movements and the SpO2. Results: With the leave-one 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 great potential to screen patients with SAS.
@article{arxiv.1802.09706,
title = {Phenotype-based and Self-learning Inter-individual Sleep Apnea Screening with a Level IV Monitoring System},
author = {Hau-Tieng Wu and Jhao-Cheng Wu and Po-Chiun Huang and Ting-Yu Lin and Tsai-Yu Wang and Yuan-Hao Huang and Yu-Lun Lo},
journal= {arXiv preprint arXiv:1802.09706},
year = {2018}
}