Feature Extraction and Small-Sample Learning of Dexmedetomidine for Neurosurgery on Postoperative Agitation in Patients with Craniocerebral Injury

Abstract

Objective. To observe the controlled effect of dexmedetomidine for neurosurgery and the effect on postoperative cognitive function. The main task of this paper is to use data from a small sample. The proposed feature extraction algorithm based on the bilinear convolutional neurological network (BCNN) is based on a small sample of data. BCNN involves the simultaneous extraction of highly discriminative cross-sectional features from the input image using two parallel subnetworks. By optimizing the algorithm to minimize losses, the two subnetworks can be supervised by each other, improving the performance of the network and obtaining accurate recognition results without spending a lot of time adjusting parameters. The mean arterial pressure (MAP) and heart rate (HR) levels of cerebral oxygen metabolism were compared between the two groups before (T0), after (T1), immediately after (T2), and after intubation (T3). In the observation group, MAP and HR values at T3, arterial-internal jugular vein bulb oxygen difference [ $D\left(a-jv\right)O_2$ ] at T1, T2, and T3, cerebral oxygen uptake ( ${\text{CEO}}_2$ ) levels, and postawakening agitation scores were lower than those of the control group during the same period ( $P<0.05$ ).