Deep Model Based on Mamba Fusion Multi-Scale Convolution LSTM for OSA Severity Grading

Obstructive sleep apnea (OSA) affects nearly one billion individuals worldwide, and its rising prevalence exacerbates cardiovascular and metabolic burdens. Although overnight polysomnography (PSG) is the diagnosis gold standard, its cost and procedural complexity constrain population-level deployment. To address this gap, an end-to-end model is developed. It operates directly on full-night peripheral oxygen saturation (SpO2) sequences sampled at 1 Hz. The model integrates multi-scale convolution, state space modeling, and channel-wise attention to classify obstructive sleep apnea severity into four levels. Evaluated on the Sleep Heart Health Study cohorts, the approach achieved overall accuracies of 80.51% on SHHS1 and 76.61% on SHHS2, with mean specificity exceeding 92%. These results suggest that a single-channel SpO2 signal is sufficient for four-class OSA classification without extensive preprocessing or additional PSG channels. This approach may enable low-cost, large-scale home screening and provide a basis for future multimodal, real-time monitoring.