Year: 2024
Type: conference-paper
Abstract: Aims: Sleep-related breathing disorders (SBD) are found in 6-17% of the global adult population and are associated with a high risk of complications such as strokes or type-II diabetes.Previous studies have proposed a sample-wise real-time finite state machine (FSM) SBD event detector using as input the nasal pressure signal.This study aims at improving this SBD event detection by using a light Gated Recurrent Unit (GRU) neural network.Methods: This work is based on data from a retrospective 5 centers study including 8 hours manually annotated polysomnography of 29 severe obstructive sleep apnea patients.The GRU was trained to classify in real-time each sample of an 8 Hz nasal pressure signal as either a normal or respiratory event (apnea or hypopnea).The GRU performances were compared to those reported for the FSM detector.Results: Apnea and hypopnea detection provided by the GRU detector shows improved performances on the testing database on averaged sensitivity (apnea: 80.2% vs 66.9%, hypopnea: 67.2% vs 47.1%) while preserving a similar averaged positive predictive value (apnea: 79.0% vs 80.7%, hypopnea: 33.7% vs 25.0%).Discussion: The GRU architecture appears as a promising tool for real-time detection of sleep apnea events.Further experiments on a larger database are warranted.
Source: Computing in cardiology
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4405435128