Raw EEG as a viable alternative to engineered decompositions in anesthetic depth prediction.

PURPOSE: Assessing the depth of anesthesia remains a challenge in operating rooms worldwide, as hospitals often rely on proprietary monitors that are costly and inaccessible to low-resource institutions. This research explores whether machine learning can predict indicators of anesthetic depth from intraoperative EEG, and whether established preprocessing methods significantly improve performance. METHODS: Using EEG recordings from 143 patients receiving sevoflurane, we developed a deep neural framework that integrates convolutional, attention-based, and recurrent components. We hypothesized that empirical mode decomposition (EEMD) of EEG, a signal-processing approach in neurophysiology, would improve prediction of the bispectral index (BIS), a clinical measure of consciousness. Contrary to expectation, models trained on raw EEG achieved similar prediction error to EEMD-based pipelines. RESULTS: These findings suggest that EEG signal decomposition may discard important temporal-spectral features that neural architectures can learn directly from raw data. Moreover, the raw-signal pipeline is computationally lighter, making it better suited for real-time deployment on standard hospital hardware. CONCLUSION: These results challenge assumptions about the role of preprocessing in clinical neurophysiology and point toward more accessible and computationally efficient approaches to anesthesia monitoring.