DeformSleepNet: Adaptive Multi-Level Feature Extraction for Enhanced Sleep Stage Classification

Sleep quality plays a crucial role in human health, with sleep stages serving as key indicators of overall well-being. Despite the advancements in sleep stage classification, existing methods encounter several limitations: 1) the use of fixed-size convolution kernels restricts the adaptive extraction of multi-scale features, and 2) redundancy in multi-modal signals results in inadequate attention to highly correlated features. In this paper, we introduce DeformSleepNet, a novel architecture that combines standard and deformable convolutions to enable adaptive multi-scale feature extraction across various frequency bands. The Multi-level Feature Encoder (MFE) update different features in different manner, achieving a trade-off between accuracy and efficiency, effectively balancing accuracy and computational efficiency. Additionally, the Content-Aware Fusion Module (CAFM) dynamically adjusts the importance of different features during fusion, thereby enhancing the overall classification performance. Experimental evaluations conducted on three publicly available datasets demonstrate that DeformSleepNet outperforms main-stream methods. Ablation studies further validate the contribution of each individual module, highlighting the robustness and effectiveness of the proposed approach.