NeuroSleepNet: An Explainable Multi-Head Attention-Based Framework With Spatial and Multi-Scale Independent Temporal Context Learning for Automatic Sleep Stage Scoring
Automatic sleep stage scoring is crucial for monitoring sleep quality and sleep-related disorders. However, most existing electroencephalogram (EEG) based deep learning frameworks rely on current and previous input sequences (varying from 4-100 epochs), which increases computational complexity and creates dependency on transition rules. In this study, we propose NeuroSleepNet, a deep-learning framework that classifies sleep stages using only the current input sequence. NeuroSleepNet employs a two-stage representation learning that includes spatial and multi-scale independent temporal context learning. Multi-head self-attention transformer encoders were employed to transform extracted representations into contextual embeddings to improve the distinctiveness of learned features. Additionally, to handle the class imbalances in different sleep stages, a logarithmic scale-based weighting technique was incorporated into the loss function of NeuroSleepNet. Furthermore, an explainability module was integrated into NeuroSleepNet to visualize and interpret class-specific decision patterns. NeuroSleepNet demonstrated state-of-the-art performance, achieving an accuracy of 86.1 percent, a macro-F1 score of 80.8 percent, and a Cohen's kappa of 0.805 on the Sleep-EDF expanded dataset. It achieved 82.0 percent, 76.3 percent, and 0.753 on MESA, 80.5 percent, 76.8 percent, and 0.738 on Physio2018, and 86.7 percent, 80.9 percent, and 0.804 on the SHHS database, respectively, by utilizing only the current sequence. As a result, there is no need for previous long sequences or sequential dependencies, making it computationally efficient and well-suited for long-term automatic sleep monitoring.