EEG-based Cross-subject Prediction for Consciousness State Transitions under Sedation using a Deep Learning Framework

Intraoperative awareness due to inappropriate depth of anesthesia remains a critical concern in clinical practice. Traditional binary classifications of conscious ness and unconsciousness may fail to capture the gradual and variable transitions that occur during the induction and emergencephasesofanesthesia. These transitions can differ significantly across individuals and anesthetic agents. This study aims to classify three conscious states, such as consciousness, transitions, and unresponsiveness under sedation with propofol and midazolam, based on the electroencephalogram (EEG) signals. Using patient-controlled sedation paradigms, we identified transitions through be havioral responsiveness and proposeanoveldeeplearning framework, Deep-ConTrans, incorporating common spatial pattern-based spatial filtering, multi-domain feature extrac tion, attention-based fusion, and domain-adversarial training for robust classification. The average classification accuracies were improved, achieving 93.93 (±3.32)% for propofol and 97.42 (±1.68)% for midazolam, respectively, demonstrating superior performance over conventional methods. The model also demonstrated strong cross anesthetic generalizability, maintaining high performance when evaluated across propofol and midazolam in external validation. Specifically, transitions toward unresponsive ness were marked by increased delta power in frontal regions and increased alpha power in parietal regions. These spectral changes are consistent with cortical bistability and disruption in the posterior hot zone, both of which have been linked to alterations in consciousness. By identifying robust and drug-independent EEG signatures of transitions, this study highlights the potential for more granular and reliable intraoperative monitoring beyond binary assessment. The enhanced generalizability and sensitivity of Deep-ConTrans enable anesthesiologists to precisely identify critical transitions, thus improving anesthetic man agement, minimizing the risk of intraoperative awareness, and facilitating personalized sedation protocols based on real-time EEG dynamics.