The invention discloses a
sedation depth monitoring method based on a graph
convolutional neural network, and relates to the technical field of
sedation monitoring, and the method comprises the steps: collecting a multi-source biomarker of a patient, carrying out the filtering and artifact removal of an electroencephalogram
signal, and generating preprocessed electroencephalogram data; performing attention
pooling processing on the node-level feature
tensor, calculating a
sedation depth index, and generating a pain
interference factor according to a connection edge weight of the dynamic function connection graph; and when the pain
interference factor exceeds a preset pain threshold value and the sedation depth index meets a preset condition, generating a sedation optimization instruction, and when the virtual pharmacokinetic node displays metabolic
abnormality, generating an infusion rate adjustment instruction. According to the method, through a dynamic function connection diagram construction link, the time-varying intensity of the
skin conductance reaction
signal and the
rugosa electromyographic
signal is used as a dynamic weight, and accurate quantification of the pain-sedation
coupling effect is achieved.