ModelS4Apnea: leveraging structured state space models for efficient sleep apnea detection from ECG signals

. Compared to existing methods, ModelS4Apnea provides high classification performance with significantly fewer trainable parameters than long short-term memory-based models, reducing training time and memory consumption. The model's ability to aggregate segment-level predictions enabled perfect per-recording classification, demonstrating its robustness in diagnosing sleep apnea across entire recordings. Moreover, its low memory footprint and fast inference speed make it well-suited for wearable devices, home-based monitoring, and clinical applications, offering a scalable and efficient solution for automated sleep apnea detection. Future work may explore multi-modal data integration, real-world deployment, and further optimizations to enhance its clinical applicability and reliability.