Utilizing Attention-based Ensemble Mechanism to Identify Discriminative Feature Combinations for Sleep Staging

Deep learning significantly diminishes reliance on human experts for sleep staging, paving the way for fully automated sleep quality assessment in clinical and daily environments. However, existing methods mostly focus on the integration of novel networks through "black boxes" way to boost classification accuracy, often neglecting model interpretability and the identification of critical sleep features. To address these issues, we develop an innovative architecture utilizing Attention-based ensemble Mechanism (namely AeM) for effective sleep staging and further finding out discriminative feature combinations. AeM consists of three main parts, i.e., modality division and combination (MDC) module, individual feature extraction (IFE) module and attention-based weight and ensemble classification (AWEC) module. MDC module divides the raw input data by modality and recombines them along the channel dimension into new inputs. The IFE module employs multiple naive convolutional neural networks to extract features efficiently from each modality combination. Finally, the AWEC module integrates these features, effectively calculates the weight of each modal combination using the attention mechanism, and outputs the classification result. We evaluated AeM on three public-available datasets: ISRUC-S1, ISRUC-S3, and Sleep-EDF78. Experimental results demonstrate that AeM outperforms all comparative methods and can identify effective digital biomarkers in sleep staging tasks.