Artificial intelligence based platform for the automatic and simultaneous explainable detection of apnoea, oxygen desaturation, and artefacts in paediatric polygraphy exams (REST).

ABSTRACT: The gold standard for the diagnosis of sleep apnoea (SA) is polysomnography, consisting of overnight in-lab tests, which are expensive for both patients and healthcare systems. Airflow and pulse/oximetry signals contain most of the necessary information for detecting SA and widely simplify the data acquisition process, hence holding the promise to increase the availability of SA diagnosis and reduce waitlists. Deep learning has recently shown some interesting steps forward in analysing these signals in paediatric patients. Here we introduce a novel platform, REST, that is able to simultaneously detect the presence of apnoea, desaturation, and artefacts in input signals. To achieve this goal, we developed a novel 1D deep neural network architecture that leverages prior knowledge of the info

SUBMITTER: D'Orazio M 

PROVIDER: S-EPMC12484538 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature