RespireSegNet: Analyzing Sleep Breathing Patterns with Deep Audio Segmentation

Abstract

Analyzing respiratory patterns is essential for diagnosing and monitoring various health conditions, particularly during sleep when irregularities such as apneas are prevalent. This study presents RespireSegNet, a deep audio segmentation method tailored for sleep breathing analysis, which addresses limitations of traditional signal processing techniques. Utilizing PSG-Audio dataset with tracheal sound recordings and respiratory belt data, RespireSegNet applies WhisperSeg, a pretrained Transformer-based model, to segment and analyze breathing cycles. The model captures subtle respiratory sounds amidst noise, demonstrating high precision in detecting respiratory rates and cycle durations across sleep stages. Compared with FFT and PeakFinding methods, RespireSegNet achieved superior accuracy in both breathing rate detection and cycle length estimation. These results highlight RespireSegNet's potential as a robust tool for non-invasive sleep disorder diagnostics, paving the way for improved respiratory sound analysis in healthcare applications. © 2025 IEEE.