Machine learning approaches for predicting sleep arousal response based on heart rate variability, oxygen saturation, and body profiles.

ABSTRACT:

Objective

Obstructive sleep apnea is a global health concern, and several tools have been developed to screen its severity. However, most tools focus on respiratory events instead of sleep arousal, which can also affect sleep efficiency. This study employed easy-to-measure parameters-namely heart rate variability, oxygen saturation, and body profiles-to predict arousal occurrence.

Methods

Body profiles and polysomnography recordings were collected from 659 patients. Continuous heart rate variability and oximetry measurements were performed and then labeled based on the presence of sleep arousal. The dataset, comprising five body profiles, mean heart rate, six heart rate variability, and five oximetry variables, was then split into 80% training/validation and 20% testing data

SUBMITTER: Kuo CF 

PROVIDER: S-EPMC10576931 | biostudies-literature | 2023 Jan-Dec

REPOSITORIES: biostudies-literature