Positive predictive values were 100%, 80%, 80%, 85% and 89% in the order of increasing BIS in the five BIS ranges. A four-level depth decision tree was built with four subparameters such as burst suppression ratio, power of electromyogram, 95% spectral edge frequency, and relative beta ratio. The performance of the decision tree and regression models was externally validated with positive predictive value and median absolute error, respectively.
Random sample consensus regression analyses were performed using the subparameters to derive multiple linear regression models of BIS calculation in five BIS ranges. Decision tree analysis was performed to determine the electroencephalography subparameters and their thresholds for classifying five BIS ranges. A histogram of data points was plotted to define five BIS ranges representing the depth of anaesthesia.
Retrospective data from 5,427 patients who underwent BIS monitoring during general anaesthesia were used, of which 80% and 20% were used as training datasets and test datasets, respectively. We investigated the BIS algorithm using clinical big data and machine learning techniques. However, only a portion of the proprietary algorithm has been identified. Bispectral index (BIS), a useful marker of anaesthetic depth, is calculated by a statistical multivariate model using nonlinear functions of electroencephalography-based subparameters.