Tissue Recognition Based on Electrical Impedance Classified by Support Vector Machine in Spinal Operation Area

Objective One of the major difficulties in spinal surgery is the injury of important tissues caused by tissue misclassification, which is the source of surgical complications. Accurate recognization of the tissues is the key to increase safety and effect as well as to reduce the complications of spinal surgery. The study aimed at tissue recognition in the spinal operation area based on electrical impedance and the boundaries of electrical impedance between cortical bone, cancellous bone, spinal cord, muscle, and nucleus pulposus. Methods Two female white swines with body weight of 40 kg were used to expose cortical bone, cancellous bone, spinal cord, muscle, and nucleus pulposus under general anesthesia and aseptic conditions. The electrical impedance of these tissues at 12 frequencies (in the range of 10–100 kHz) was measured by electrochemical analyzer with a specially designed probe, at 22.0–25.0°C and 50%–60% humidity. Two types of tissue recognition models ‐ one combines principal component analysis (PCA) and support vector machine (SVM) and the other combines combines SVM and ensemble learning ‐ were constructed, and the boundaries of electrical impedance of the five tissues at 12 frequencies of current were figured out. Linear correlation, two‐way ANOVA, and paired T‐test were conducted to analyze the relationship between the electrical impedance of different tissues at different frequencies. Results The results suggest that the differences of electrical impedance mainly came from tissue type (p