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

TL;DR

This paper introduces a phenotype-based, self-learning AI system for sleep apnea screening using a Level IV wearable device, achieving high accuracy by leveraging physiological knowledge and adaptive modeling.

Contribution

It presents a novel phenotype-driven, self-adaptive AI algorithm for sleep apnea screening on low-level monitoring devices, improving accuracy and personalization.

Findings

Screening accuracy of 93.6% for AHI ≥ 15

Positive likelihood ratio of 6.8

Negative likelihood ratio of 0.03

Abstract

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.