Open access
Aug 2026
Sleep apnea detection from respiratory sounds using time–Frequency features and neural networks
A lightweight deep learning model that detects apnea events using a single acoustic channel is proposed, demonstrating that respiratory sounds contain sufficient information for reliable apnea detection and validating their potential as a non-invasive, low-cost biomarker.
Joan S. Tunubalaa, Gissel V. Quitiana, Carlos M. Paredesa et al.
· F1000Research · 0 citations