Design of a recognition system to predict movement during anesthesia

This study developed an electroencephalograph (EEG) system using autoregressive modeling and neural networks to predict patient movement during anesthesia. The system achieved over 92% accuracy when combining EEG and hemodynamic data, showing promise for safer anesthesia monitoring.