ApneaWhisper: Transformer-Based Audio Segmentation for Fine-Grained Non-Invasive Sleep Apnea Detection

The model's fine-grained temporal resolution enables precise apnea event localization, duration estimation, and subtype classification. While ApneaWhisper performs robustly for OSA, challenges remain in distinguishing central (CSA) and mixed (MSA) sleep apnea, due to subtle or ambiguous acoustic patterns. The frame-level segmentation also facilitates accurate apnea-hypopnea index (AHI) estimation, which could reduce dependence on full PSG studies in certain clinical and home-monitoring scenarios. Future improvements may involve multimodal integration (eg, oxygen saturation) and noise-robust training techniques.