Detecting arousals and sleep from respiratory inductance plethysmography.

ABSTRACT:

Purpose

Accurately identifying sleep states (REM, NREM, and Wake) and brief awakenings (arousals) is essential for diagnosing sleep disorders. Polysomnography (PSG) is the gold standard for such assessments but is costly and requires overnight monitoring in a lab. Home sleep testing (HST) offers a more accessible alternative, relying primarily on breathing measurements but lacks electroencephalography, limiting its ability to evaluate sleep and arousals directly. This study evaluates a deep learning algorithm which determines sleep states and arousals from breathing signals.

Methods

A novel deep learning algorithm was developed to classify sleep states and detect arousals from respiratory inductance plethysmography signals. Sleep states were predicted for 30-s intervals (one

SUBMITTER: Finnsson E 

PROVIDER: S-EPMC11991959 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature