ModelS4Apnea: Leveraging Structured State Space Models for Efficient Sleep Apnea Detection From ECG Signals

Abstract

Objective. Sleep apnea is a common sleep disorder associated with severe health risks, necessitating accurate and efficient detection methods. Approach. This study proposes ModelS4Apnea, a deep learning framework for sleep apnea detection from electrocardiogram (ECG) spectrograms, integrating structured state space models (S4) for temporal modeling. The framework consists of a convolutional neural network module for local feature extraction, an S4 module for capturing long-range dependencies, and a classification module for final predictions. Main results. The model was trained and evaluated on the Apnea-ECG dataset, achieving an accuracy of 0.933, an F1-score of 0.912, a sensitivity of 0.916, and a specificity of 0.944, outperforming most prior studies while maintaining computational efficiency. Significance. 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.