SpinTSA: A Temporal-Spatial-Attentive Network with Multiscale Anchors for Sleep Spindle Detection

Accurate identification of sleep spindles, transient EEG oscillations that are critical for memory consolidation and brain health, is essential for advancing sleep research and clinical applications. Despite considerable efforts, current methods still face significant challenges in capturing the complex dynamics of spindles across different temporal scales, maintaining robustness against inter-subject variability and common EEG artifacts, and generalizing detection across various sleep events without relying on detailed, event-specific rule crafting. To address these challenges, this study introduces SpinTSA, a new deep neural framework that integrates a UNet-BiLSTM backbone for joint spatial-temporal feature extraction, a Progressive Attention Downsampling (PAD) module with squeeze-and-excitation attention for refined feature recalibration, and a Multiscale Temporal Anchor (MTA) mechanism for precise and scalable event localization. Together, these components enable the model to effectively detect spindles with varying morphologies and durations. Evaluations on the public MASS-SS dataset and the DREAMS Sleep Spindles Database show SpinTSA's strong performance, achieving F1-scores of 79.2 % and 61.4 %, respectively, which are significantly higher than existing benchmarks. Furthermore, by using the flexible nature of the MTA module, SpinTSA can be easily extended to detect K-complexes, reaching an F1-score of 61.9% on the MASS-KC dataset. These results highlight SpinTSA's effectiveness and adaptability as a unified framework for automated sleep event detection, bridging the gap between research and clinical applications.