Discriminant analysis for anaesthetic decision-making: an intelligent recognition system for epidural needle insertion.

BACKGROUND Incorrect placement of epidural catheters causes medical complications. We used linear discriminant analysis (LDA) to develop an intelligent recognition system (i-RS) in order to guide epidural placement and reduce physician error. METHODS We analysed real-time dual-wavelength fibreoptic data recorded from the end of an epidural needle in a live porcine model. Two categories of tissue layers were necessary for correct placement of catheter: epidural space and ligamentum flavum. The data were tested using linear, quadratic and logistic parametric analysis to identify which method could distinguish the two anatomical structures. RESULTS LDA was the best fit for our model. There was ∼80% sensitivity and specificity for correct anatomical identification. Error rates based on cross-validation were 17.0% for the epidural space and 18.6% for ligamentum flavum. Error rates were greater with the 532 nm compared with 650 nm wavelength. CONCLUSIONS The sensitivity and specificity of LDA for identifying the correct anatomical structure was similar to a physician who is an expert in epidural placement. Overall performance of an i-RS could be improved by expanding the database for decision-making and adding a category of uncertainty. This would reduce complications caused by incorrect epidural placement.