A Hierarchical Multimodal Framework for Sedation Monitoring in ICU Patients

In the intensive care unit (ICU), monitoring sedation levels is crucial. Clinicians often rely on intermittent behavioral scales like the Richmond Agitation-Sedation Scale (RASS), which can be subjective and delay timely interventions. While electroencephalography (EEG) offers a continuous and non-invasive alternative, but the complexity of consciousness renders a unimodal signal insufficient for comprehensive representation. To address these challenges, we propose a novel multimodal deep learning framework, Hierarchical Multimodal Fusion with Dynamic Correction (HMDC), that synergistically integrates EEG with peripheral physiological signals including blood pressure, heart rate, and oxygen saturation. The architecture features a dual-stream pathway to process both raw temporal EEG data and its spectral features from spectrograms. These neural representations are then intelligently fused and refined by a Dynamic Correction Module using a confidence-weighting mechanism. The model was developed and validated on a dataset comprising 2,880 labeled RASS assessments from 105 ICU patients, with scores ranging from -5 (comatose) to +1 (restless). The HMDC framework achieved a classification accuracy of 83.8%, significantly outperforming unimodal and simpler fusion baselines. By providing a temporally precise and physiologically grounded sedation assessment, this integrative approach establishes a robust correlation between multimodal signal patterns and clinical states, offering clinicians a unified tool for optimizing sedative titration and potentially minimizing delirium risks.