Predicting Apnea-Hypopnea Index in Patients with Obstructive Sleep Apnea Using Unsegmented ECG-Signal-Based Algorithms

Abstract

Obstructive sleep apnea (OSA) is a common sleep disorder and is diagnosed by polysomnography (PSG) as the gold standard. However, PSG is a time-consuming and costly test, and patients have to endure long waits before receiving a PSG test in a hospital. In view of this, portable and wearable screening tools for OSA prediction have been developed recently as a low-cost and easy-to-use method. In this paper, an OSA detection model, based on regression approach, using unsegmented electrocardiography (ECG) signals is developed to directly estimate the apnea-hypopnea index (AHI) value, which is the first report in the literature. In this manner, this work can provide more details of OSA assessment to users and doctors. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.