Development of a Deep Learning Model for Real-Time Monitoring of Anesthesia Depth and Its Clinical Validation

Real-time anesthesia depth monitoring is essential to mitigate awareness and over-hypnosis risks, yet most indices are prone to artifact sensitivity and inter-patient variation. This study developed a causal deep learning pipeline for streaming physiological windows, including a temporal convolutional neural network encoder, temporal dependency module, attention-based aggregation, and an optional uncertainty head. Using neuro fatigue sensor data as proxy benchmark, this study conducted subject-wise splits, regression/classification, and robustness tests for artifacts/windowing, plus multiple ablation studies and stability assessments under continuous load. The model outperformed feature-based and convolutional neural network-only baselines in accuracy, had lower inter-subject variation, and showed strong low-latency streaming inference performance. This confirms real-time anesthesia depth estimation feasibility, advocating future operating room pilots with synchronized electroencephalography, bispectral index, and event annotations to verify agreement and responsiveness.